Journal of Technology Informatics and Engineering
type: channel
description: Journal Title: Journal of Technology Informatics and Engineering Initials: JTIE Frequency: April, August, and December Print ISSN: 2961-9068 Online ISSN: 2961-8215 Editor in Chief: Dr. Ir. Agus Wibowo., M.Kom., M.Si., M.M. DOI: 10.51903 Publisher: University of Science and Computer Technology Journal of Technology Informatics and Engineering is a peer-reviewed scientific journal that publishes original research articles, scholarly studies, and applied research findings related to advances in technology and engineering. The journal is published in both electronic and print formats with e-ISSN: 2961-8215, and p-ISSN: 2961-9068. The scope of the journal encompasses, but is not limited to, Informatics, Mechanical Engineering, Electrical Engineering, Information Systems, and Industrial Engineering. The journal serves as an academic platform for researchers, academics, and practitioners to disseminate research outcomes and technological innovations. Journal of Technology Informatics and Engineering is published three times a year, in April, August, and December. Indexed by:
publisher: University of Science and Computer Technology
language: en
publication name: Journal of Technology Informatics and Engineering
issn: 2961-9068
items: Natural-Language Policy Reasoning with Proof Generation: Turning Platform Rules into Verifiable Knowledge
items: Explainable Multi-Hop Question Answering for QA Assistants: Two-Hop Evidence Retrieval, Sentence-Level Supporting Facts, and Explicit Reasoning Paths
items: Off-Policy Evaluation and Conservative Policy Selection for Slot-Level Dynamic Bidding and Ranking on the Open Bandit Dataset (Small)
items: Federated Topic-Preference Learning for Knowledge-Grounded Chat with Differential Privacy
items: Digital-Twin Dispatching for Urban Mobility via Spatio-Temporal Transformers and Offline Reinforcement Learning
items: Offline Counterfactual Evaluation for Advertising and Recommendation Slot Policies: A Reproducible Study on the Open Bandit Dataset (Small)
items: Multi-Horizon GPU Demand Forecasting with Workload Semantics and Operational Risk Curves: An Empirical Study on Alibaba Clusterdata GPU Trace
items: A Decision-Support Analytics Framework of Strategic HR Practices and Employee Performance in Islamic Banking
items: Optimizing Regional Financial Management through the Transformation of the Digital Financial Information System in the Bekasi City Government
items: Ranking AODV Routing Characteristics in MANETs: Fuzzy Delphi Method Application
items: Calibration-Light Subject-Independent Motor Imagery BCI via Self-Supervised Pretraining and Conformer
items: A Lightweight Medical Foundation Model for Cross-Modal Multi-Task Pretraining and Parameter-Efficient Few-Shot Transfer on MedMNIST
items: Automatic Detection and Explanation of Dark Patterns from Interface Microcopy: Empirical Comparison of BERT-Style Encoders, RoBERTa-Style Encoders, and LLM-Style Decoders on the ec-darkpattern Dataset
items: From Hand-Drawn Sketches to Interactive Web Prototypes: A Reproducible Vision-Language Approach with Structural and Visual Consistency Evaluation
items: LiDAR–Camera Object-Level Fusion for Multi-Target Tracking Using JPDA and EKF: A Reproducible Empirical Study on a PandaSet-Parameterised Five-Sequence Dataset
items: Uncertainty-Aware Late Fusion for 3D Perception (Confidence Calibration + Fusion Rule Learning)
items: LLM-Driven CI Failure Diagnosis and Automated Repair: From GitHub Actions Logs to Patch Recommendation
items: A Comparative Study on Self-Organization in Wireless Sensor Networks
items: A Novel Three-phase integrated inverter for Hybrid Electric Vehicle Applications
items: IoT-Driven in the Banking Application Platforms Using a Real-Time SQL Injection Mitigative Measures
items: Automation in Cybersecurity using Machine Learning: A CaseStudy on Anomaly Detection with Isolation Forest
items: An IoT-Based Smart Feeding System for Koi Fish Using Mamdani Fuzzy Logic
items: Intelligent Image Rights Protection System Using Machine Learning, Cloud Services and IoT Alerts
items: Bias and Hallucination Evaluation in LLMs
items: AI-Driven Multi- Modal Fake Content Detection System Using Audio-Text Fusion and Transformer Network
items: Self-Supervised Representation Learning for Criminology: Detecting Anomalies, Classifying Reports, and Mapping Networks
items: IoT-Enabled Multimodel Emotion Detection and Assistive Smartwatch for Special-Needs Children
items: Privacy-Robust Incrementality Estimation in Cookieless Settings via Uplift Modeling: Reproducible Evidence from the Hillstrom E-Mail Experiment
items: A Constrained, Data-Driven Budgeting Framework Integrating Macro Demand Forecasting and Marketing Response Modeling
items: User-Centered Mobile Navigation: Evaluating Local Usability for Improved UX

Natural-Language Policy Reasoning with Proof Generation: Turning Platform Rules into Verifiable Knowledge
type: item
description: Policy and compliance systems increasingly express rules in natural language, yet enforcement requires deterministic decisions and auditable explanations. This paper studies a practical pipeline that converts natural-language facts and rules into a verifiable knowledge base, answers queries with three-valued semantics (True/False/Unknown), and produces machine-checkable proofs. The contribution is system-level rather than a new reasoning formalism: we integrate controlled-language parsing, symbolic proof extraction, independent proof checking, and proof-based supervision in a single auditable framework. We evaluate the pipeline on two natural-language rule-reasoning benchmarks: (i) a balanced subset of ProofWriter’s open-world-assumption tasks (360 train, 360 test), and (ii) a RuleTaker-style dataset generated from its grammar and label semantics (1800 train, 900 test), both balanced across reasoning depths 0–5. We compare a text-only logistic regression baseline, a retrieval-based “proof” baseline, a symbolic forward-chaining reasoner with proof extraction, and a proof-trained classifier using generated proofs. To ensure fairness, LR-text and LR-proof share the same TF-IDF/logistic-regression setup, and the retrieval baseline uses the same representation with a fixed top-4 configuration. On ProofWriter-Balanced, the symbolic reasoner achieves 0.803 accuracy (0.808 macro-F1), while proof-trained classification reaches 0.825 accuracy (0.825 macro-F1). On RuleTaker-Rep, both methods achieve 1.000 accuracy. Proof verifiability clearly separates faithful from post-hoc explanations: symbolic proofs are verifiable for all predictions, whereas retrieval-based proofs are verifiable for only 31.4%. Sensitivity analyses varying reasoning depth, distractors, and proof corruption show that proof-based methods remain robust to noise but depend on proof integrity. These findings demonstrate the feasibility of auditable natural-language policy reasoning in controlled settings, whi…
subject: Articles
  topic: section
subject: Policy Reasoning
  topic: keywords
subject: Rule-Based Inference
  topic: keywords
subject: Natural Language Rules
  topic: keywords
subject: Proof Generation
  topic: keywords
subject: Explainability
  topic: keywords
creator: Xiaofei Luo
rights: Copyright (c) 2025 Xiaofei Luo https://creativecommons.org/licenses/by-sa/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 402
ending page: 424
doi: 10.51903/jtie.v4i2.505

Explainable Multi-Hop Question Answering for QA Assistants: Two-Hop Evidence Retrieval, Sentence-Level Supporting Facts, and Explicit Reasoning Paths
type: item
description: Multi-hop question answering (QA) for customer-facing assistants requires not only accurate answers but also an auditable evidence trail that explains how the system arrived at each answer. We present a fully interpretable multi-hop QA pipeline that decomposes inference into three explicit modules—Retriever → Evidence Selector → Reasoner—and produces an explanation consisting of sentence-level supporting facts and an explicit two-hop evidence path. The retriever ranks candidate paragraphs using lexical IDF-weighted token overlap; the evidence selector chooses a small set of high-scoring sentences; and the reasoner extracts a final answer using weighted candidate phrase scoring and deterministic rules for date/number and constrained yes/no comparisons. We conduct full experimental evaluations on the complete development splits of HotpotQA (7,405 questions, distractor setting) and 2WikiMultihopQA (12,576 questions). On HotpotQA, sentence-level evidence selection improves Supporting Fact F1 from 0.334 to 0.419, and adding an explicit two-hop retrieval path further increases Supporting Fact F1 to 0.426 while raising paragraph recall@2 to 0.603. Answer F1 increases from 0.084 to 0.088. On 2WikiMultihopQA, evidence selection improves Supporting Fact F1 from 0.328 to 0.429 and Answer F1 from 0.071 to 0.075. These results quantify the contribution of explicit evidence selection and path-constrained retrieval to explainability and provide a practical, reproducible baseline for knowledge assistants that must justify answers with supporting facts.
subject: Articles
  topic: section
subject: Multi-Hop Question Answering
  topic: keywords
subject: Explainable QA
  topic: keywords
subject: Evidence Retrieval
  topic: keywords
subject: Supporting Facts
  topic: keywords
subject: Reasoning Paths
  topic: keywords
creator: Xiaofei Luo
rights: Copyright (c) 2026 Xiaofei Luo https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 219
ending page: 240
doi: 10.51903/jtie.v5i1.504

Off-Policy Evaluation and Conservative Policy Selection for Slot-Level Dynamic Bidding and Ranking on the Open Bandit Dataset (Small)
type: item
description: Dynamic bidding and ranking systems must improve revenue or engagement while avoiding harmful regressions during deployment. This paper presents an end-to-end offline OPE and conservative policy-selection workflow for slot-level contextual bandit approximations of ranking decisions. Using the small Open Bandit Dataset (OBD-small) from ZOZOTOWN (ZOZO, Inc.), each logged row is treated as a context-dependent choice among discrete actions (items), with binary click rewards and logged propensity. This formulation is suitable at the slot level but does not capture full listwise ranking or multi-step offline reinforcement learning. Dynamic bidding and ranking systems must improve revenue or engagement while avoiding harmful regressions during deployment. This paper presents an end-to-end offline OPE and conservative policy-selection workflow for slot-level contextual bandit approximations of ranking decisions. Using the small Open Bandit Dataset (OBD-small) from ZOZOTOWN (ZOZO, Inc.), each logged row is treated as a context-dependent choice among discrete actions (items), with binary click rewards and logged propensity. This formulation is suitable at the slot level but does not capture full listwise ranking or multi-step offline reinforcement learning. Empirically, highly deterministic evaluation policies exhibit extreme variance under sparse clicks, while the logistic reward model remains weak (ROC-AUC ≈ 0.5), limiting DM/DR interpretability. Clipped-DR mixing yields only limited certified improvements: in the women’s campaign, gains appear only at moderate confidence (δ=0.10) and for caps up to M=5, whereas stricter or looser settings revert to baseline; in the men’s campaign, certification is largely absent. These findings demonstrate that OPE diagnostics and conservative mixing enable reproducible offline selection under uncertainty, but do not indicate deployment-ready improvements.
subject: Articles
  topic: section
subject: Offline Reinforcement Learning
  topic: keywords
subject: Contextual Bandits
  topic: keywords
subject: Dynamic Ranking
  topic: keywords
subject: Dynamic Bidding
  topic: keywords
creator: Tong Ye
creator: Jinyi Mu
creator: James Hunter
rights: Copyright (c) 2026 Tong Ye, Jinyi Mu, James Hunter https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 178
ending page: 199
doi: 10.51903/jtie.v5i1.503

Federated Topic-Preference Learning for Knowledge-Grounded Chat with Differential Privacy
type: item
description: Retrieval-augmented approaches have become central in knowledge-grounded dialogue systems, yet incorporating topical preferences remains difficult due to privacy constraints on user interaction data. This study introduces a lightweight federated topic-preference (FedTP) mechanism that models session-level preferences without centralizing raw data and uses client-level differential privacy (DP). Using the Topical-Chat dataset (8,628 conversations), each conversation is treated as a client, and evidence routing is framed as selecting relevant knowledge snippets based on dialogue context. The proposed method augments a TF-IDF relevance score with a small preference-based component derived from both local session distributions and a DP-aggregated global prior. Experimental results on 9,553 grounded test turns show a consistent but limited improvement in evidence hit rate, from 0.6167 to 0.6194. The small optimal preference weight (λ = 0.005) indicates that the preference signal mainly influences decisions when competing candidates have similar relevance scores, rather than substantially altering routing behavior. A privacy–utility analysis under Gaussian DP (ε ranging from 9.69 to 0.606, δ = 1e−5) shows negligible changes in performance, which is expected given the large number of clients in a one-shot aggregation setting. Additional metrics remain largely stable, suggesting that the method affects selection margins rather than overall alignment. These findings suggest that federated preference aggregation can provide a modest, privacy-preserving bias for evidence routing, but its practical impact remains incremental and context-dependent.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Knowledge-Grounded Dialogue
  topic: keywords
subject: Topical Preference
  topic: keywords
creator: Meng-Ju Kuo
creator: Daren Zheng
creator: Julie Hires
rights: Copyright (c) 2025 Meng-Ju Kuo, Daren Zheng, Julie Hires https://creativecommons.org/licenses/by-sa/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 385
ending page: 401
doi: 10.51903/jtie.v4i2.502

Digital-Twin Dispatching for Urban Mobility via Spatio-Temporal Transformers and Offline Reinforcement Learning
type: item
description: This study addresses the challenge of optimizing ride-hailing dispatch and repositioning under data limitations by proposing an end-to-end digital-twin dispatching framework that integrates spatio-temporal demand forecasting with offline reinforcement learning. Using publicly available NYC FOIL ride-hailing data aggregated at the dispatching-base level, the research aims to evaluate whether coarse-grained data can still support reliable, reproducible decision-making pipelines. The methodology consists of two main components: (i) multivariate time-series forecasting using baseline models, a temporal convolutional network (TCN), and a spatio-temporal transformer to predict next-day demand; and (ii) a digital-twin simulation combined with an action-constrained offline reinforcement learning approach, including behavior cloning (BC) and Conservative Q-Learning (CQL), to optimize fleet repositioning decisions. Experimental results show that the TCN achieves the best forecasting accuracy on the test period, although dominant demand regions largely drive performance gains. In the control phase, conservative policies such as CQL demonstrate stable performance with reduced repositioning costs, but do not significantly outperform behavior cloning due to limited training data. The findings indicate that, in coarse aggregate settings, operational improvements are more influenced by controlling policy sensitivity than by marginal forecasting gains. This study contributes a reproducible benchmark pipeline and highlights the importance of conservative control strategies, transparent assumptions, and sensitivity analysis when deploying AI-driven mobility systems based on limited or aggregated data.
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Urban Mobility
  topic: keywords
subject: Ride-Hailing Dispatch
  topic: keywords
subject: Demand Forecasting
  topic: keywords
subject: Spatio-Temporal Transformer
  topic: keywords
creator: Long Zhang
creator: Ruiyan Ma
creator: Peter Greg
rights: Copyright (c) 2025 Long Zhang, Ruiyan Ma, Peter Greg https://creativecommons.org/licenses/by-sa/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 337
ending page: 363
doi: 10.51903/jtie.v4i2.501

Offline Counterfactual Evaluation for Advertising and Recommendation Slot Policies: A Reproducible Study on the Open Bandit Dataset (Small)
type: item
description: Offline or counterfactual evaluation is a critical capability for iterating advertising and recommender ranking strategies when online A/B testing is slow, expensive, or risky. Off-policy evaluation (OPE) estimates the expected reward of a candidate policy using logged interaction data from a different behavior policy. Still, it can suffer from high variance under poor overlap and can be misleading when the operational objective is choosing among candidate policies rather than minimizing point-estimation bias alone. This paper presents a fully reproducible empirical study of IPS, self-normalized IPS (SNIPS), doubly robust (DR), and Switch-DR estimators on the Open Bandit Dataset (OBD) small release. Using the Men and Women campaigns (10,000 logged item-impressions per campaign and behavior policy) collected by uniform random and Bernoulli Thompson Sampling (BTS), we construct a held-out oracle for stationary slot-wise policies from the random-traffic split and evaluate both value estimation and policy-ranking consistency on random-logged and BTS-logged test sets. Across 1,000 nonparametric bootstrap replications, IPS and SNIPS are accurate on randomly logged data, whereas BTS-logged data exhibit extreme importance weights and very small effective sample sizes (ESS), making IPS-based ranking unreliable under weak support. Switch-DR is most useful in moderate-overlap regimes, where it truncates high-variance corrections. Still, it introduces bias that depends on the switching threshold and must therefore be stress-tested rather than treated as a universally superior estimator. Finally, we provide a structured reporting template—based on oracle decomposition, overlap diagnostics, and estimator components—for explaining why a policy appears better and how reliable that conclusion is.
subject: Articles
  topic: section
subject: Off-Policy Evaluation
  topic: keywords
subject: Counterfactual Evaluation
  topic: keywords
subject: Contextual Bandits
  topic: keywords
subject: Inverse Propensity Scoring
  topic: keywords
creator: Jinyi Mu
creator: Tong Ye
creator: Priya Patel
rights: Copyright (c) 2025 Jinyi Mu, Tong Ye, Priya Patel https://creativecommons.org/licenses/by-sa/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 521
ending page: 543
doi: 10.51903/jtie.v4i3.500

Multi-Horizon GPU Demand Forecasting with Workload Semantics and Operational Risk Curves: An Empirical Study on Alibaba Clusterdata GPU Trace
type: item
description: This study addresses the operational challenge of multi-horizon GPU demand forecasting in large-scale computing clusters, where GPUs are costly resources and demand fluctuates under constraint-driven scheduling. The objective is to evaluate whether integrating workload semantics improves forecasting performance across horizons up to 72 hours. A reproducible empirical benchmark is developed using the Alibaba Clusterdata GPU trace (cluster-trace-gpu-v2023), comprising 8,152 pods over approximately 149 days with a total capacity of 6,212 GPUs. The study compares two statistical baselines, ARIMA(48,0,0) and a seasonal-trend additive model, with three lightweight deep learning models: Temporal Convolutional Network (TCN), Informer-lite, and TFT-lite. Workload semantics are approximated by converting hourly job metadata into textual summaries, embedding them with TF-IDF and truncated SVD (8 dimensions), and incorporating them as exogenous covariates. Evaluation uses SMAPE and MASE across multiple horizons (1–72 hours), along with peak-aware metrics and operational risk curves. Results show that the seasonal-trend model achieves the best overall accuracy (15.34% sMAPE), while TCN is the strongest deep model (17.20% sMAPE). Semantic embeddings do not improve short horizons (1–48 hours) but reduce 72-hour sMAPE by 11.1% and improve peak-window error. These findings indicate that autoregressive signals dominate short-term forecasting, whereas semantic context becomes beneficial at longer horizons. The study emphasizes that combining point accuracy with risk-based evaluation is essential for effective GPU capacity planning under dynamic and uncertain demand conditions.
subject: Articles
  topic: section
subject: GPU clusters
  topic: keywords
subject: capacity planning
  topic: keywords
subject: multi-horizon forecasting
  topic: keywords
subject: workload semantics
  topic: keywords
creator: Siming Zhao
creator: Jingwen Bai
creator: Drew Roberson
rights: Copyright (c) 2025 Siming Zhao, Jingwen Bai, Drew Roberson https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 544
ending page: 571
doi: 10.51903/jtie.v4i3.498

A Decision-Support Analytics Framework of Strategic HR Practices and Employee Performance in Islamic Banking
type: item
description: This research analyzes Islamic banking's strategic human resource strategies by examining how compensation, work incentives, and job satisfaction affect employee performance. 210 Islamic finance personnel were quantitatively surveyed and evaluated using Partial Least Squares–Structural Equation Modeling (PLS-SEM) using SmartPLS. Bootstrapping. 5,000 subsamples were used to evaluate direct and indirect impacts. Research indicates that financial remuneration and work motivation negatively affect employee performance (β = −0.280 and β = −0.182, respectively). Job happiness has a low but positive impact on employee performance (β = 0.087). Minimal indirect effects and variation accounted for (VAF) values below 20%. Job satisfaction does not moderate the links between financial compensation and employee performance, nor between work motivation and performance. The findings suggest that direct processes, rather than emotional measures such as job happiness, affect the performance of Islamic banking employees. The Islamic banking strategic human resource management literature is enhanced by emotional assessments, such as job satisfaction. Strategic human resource management in Islamic banking is informed by research, with managerial implications for fair remuneration systems and sustainable motivational techniques that reflect Islamic ideals.
subject: Articles
  topic: section
subject: HR
  topic: keywords
subject: Structural Model
  topic: keywords
subject: Islamic Banking
  topic: keywords
subject: SHRP
  topic: keywords
subject: Employee Performance
  topic: keywords
creator: Melyani Melyani
creator: Yulianto Yulianto
creator: Wasilatun Nikmah
creator: Henny Armaniah
creator: Herudini Subariyanti
creator: Andri Rizko Yulianto
creator: Daniel Daniel
rights: Copyright (c) 2026 Melyani Melyani, Yulianto Yulianto, Wasilatun Nikmah, Henny Armaniah, Herudini Subariyanti, Andri Rizko Yulianto, Daniel Daniel https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 241
ending page: 255
doi: 10.51903/jtie.v5i1.496

Optimizing Regional Financial Management through the Transformation of the Digital Financial Information System in the Bekasi City Government
type: item
description: This research adopts an applied research approach conducted in November 2023 at the Bekasi City Government. Data were collected through observation, interviews, and literature study. The system development methodology follows the System Development Life Cycle (SDLC), including system analysis, system design, implementation, and maintenance. The proposed digital financial information system is developed in Java and integrated with a MySQL database to manage financial data, including revenue and expenditure, SPP (Payment Request Letters), and SPJ (Accountability Reports). The system design incorporates tools such as Context Diagram (CD), Data Flow Diagram (DFD), Entity Relationship Diagram (ERD), and flowcharts to ensure systematic and structured development. The implementation of the digital system is expected to improve the accuracy, timeliness, and relevance of financial information, facilitate faster report generation, and enhance financial transparency and accountability in regional governance. The findings indicate that the transformation of a digital financial information system significantly supports the optimization of regional financial management processes, minimizes reporting errors, accelerates data processing, and strengthens decision-making processes within the Bekasi City Government. Therefore, the digital transformation of financial systems is essential in supporting good governance and sustainable regional development.
subject: Articles
  topic: section
subject: Digital Transformation
  topic: keywords
subject: financial information System
  topic: keywords
subject: Regional Financial Management
  topic: keywords
subject: Java Programming
  topic: keywords
subject: Local Government
  topic: keywords
creator: Dian Mohamad Anwar
creator: Isyana Emita
creator: Melyani Melyani
creator: Indra Riyana Rahadjeng
creator: Wahyu Indrarti
creator: Ahmad Rafik
creator: Dian Indah Sari
rights: Copyright (c) 2026 Dian Mohamad Anwar, Isyana Emita, Melyani Melyani, Indra Riyana Rahadjeng, Wahyu Indrarti, Ahmad Rafik, Dian Indah Sari https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 200
ending page: 218
doi: 10.51903/jtie.v5i1.495

Ranking AODV Routing Characteristics in MANETs: Fuzzy Delphi Method Application
type: item
description: Optimization of Mobile Ad hoc Networks (MANET) routing has the potential to improve performance. Enhancing the Ad-hoc On-demand Distance Vector (AODV) protocol, which is predominantly used in MANET routing, would lead to performance improvements. This study sought to provide a structured approach to identifying which AODV protocol characteristics should be prioritized for modification to improve performance in MANETs. This would provide guidance for future research on AODV prioritization. Thirteen AODV characteristics were identified through a systematic review. The Fuzzy Delphi Method was employed to analyze and prioritize the study characteristics. Route discovery characteristics were prioritized and ranked as number one, with a threshold value of 0.097, 100% agreement among experts, and a fuzzy score of 0.72. The experts unanimously agreed that modifying the route discovery characteristic has the greatest potential to improve performance in MANET. This was because a large amount of control traffic in the network is generated during the route discovery process. The other characteristics were route selection, route maintenance, route table management, routing updates, number of routes discovered, hop count, time to live, and use of hello messages. However, some characteristics were perceived to be structurally critical for AODV operation but less responsive to performance-optimization modifications, namely hop-by-hop communication, identification of route request messages, sequence numbers, and neighbor lists. Future studies should complement the expert-based prioritization of AODV characteristics with experimental and simulation evaluations to quantify performance gains.
subject: Articles
  topic: section
subject: AODV Characteristics
  topic: keywords
subject: Fuzzy Delphi Method
  topic: keywords
subject: MANET
  topic: keywords
subject: Performance Improvement
  topic: keywords
subject: Prioritization
  topic: keywords
creator: Patrick Njoroge
creator: John Ndia
creator: Daniel Makupi
rights: Copyright (c) 2026 Patrick Njoroge, John G. Ndia, Daniel Makupi https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-06
publication date: 2026-04-06
starting page: 160
ending page: 177
doi: 10.51903/jtie.v5i1.494

Calibration-Light Subject-Independent Motor Imagery BCI via Self-Supervised Pretraining and Conformer
type: item
description: Motor imagery (MI) electroencephalography (EEG) is a foundational paradigm for non-invasive brain–computer interfaces (BCIs). However, its practical adoption is constrained by time-consuming per-user calibration and limited cross-subject generalization. This study evaluates a calibration-light MI-BCI framework that combines self-supervised masked EEG pretraining with a lightweight Conformer fine-tuning model. Experiments were conducted on BCI Competition IV Dataset 2b using only the labeled sessions 01T–03T, with artifact-annotated trials removed according to the official 1023 markers. Three deployment-relevant settings were examined: within-subject evaluation (01T–02T → 03T), strict leave-one-subject-out (LOSO) evaluation, and few-shot adaptation with k = 1/5/10 trials per class from the held-out subject’s screening sessions. Full within-subject benchmarking included CSP+LDA, EEGNet, DeepConvNet, ShallowFBCSPNet, supervised Conformer, and SSL+Conformer, while the subject-independent and few-shot analyses focused on CSP+LDA, EEGNet, supervised Conformer, and SSL+Conformer. In the fully calibrated setting, the best mean accuracy was obtained by ShallowFBCSPNet (62.23% ± 14.16%), whereas SSL+Conformer achieved 54.85% ± 11.15% and slightly outperformed the supervised Conformer (53.56% ± 8.81%). Under strict LOSO, EEGNet achieved the highest mean accuracy (52.92% ± 8.25%), while SSL+Conformer reached 51.56% ± 7.18%. In few-shot adaptation, SSL+Conformer achieved the highest mean accuracy at k = 10 (52.84% ± 7.64%) among the core calibration-light methods. The proposed model had a size of 0.1329 MB, a median CPU latency of 0.8777 ms/trial, and LOSO calibration values of ECE = 0.0630 and Brier = 0.4995. These results indicate that masked EEG pretraining provides a competitive lightweight baseline and is most useful when a modest amount of target-subject calibration data is available.
subject: Articles
  topic: section
subject: motor imagery
  topic: keywords
subject: EEG
  topic: keywords
subject: brain–computer interface
  topic: keywords
subject: subject-independent learning
  topic: keywords
creator: Qiyou Wu
creator: Gaotian Mi
creator: Dan Wood
rights: Copyright (c) 2025 Qiyou Wu, Gaotian Mi, Dan Wood https://creativecommons.org/licenses/by-sa/4.0
date: 2025-04-25
publication date: 2025-04-25
starting page: 239
ending page: 262
doi: 10.51903/jtie.v5i1.493

A Lightweight Medical Foundation Model for Cross-Modal Multi-Task Pretraining and Parameter-Efficient Few-Shot Transfer on MedMNIST
type: item
description: Medical imaging has rapidly adopted pre-trained backbones, yet many transfer-learning pipelines remain expensive to train and difficult to adapt when data, compute, or privacy constraints limit full fine-tuning. We present STMedFM, a lightweight medical multi-task backbone baseline designed for fast prototyping across 2D images and 3D volumes. STMedFM uses modality-specific convolutional stems (2D and 3D) and a shared low-depth encoder, and it supports parameter-efficient transfer via Low-Rank Adaptation (LoRA) and bottleneck adapters. We pretrain STMedFM with supervised multi-task learning on four MedMNIST tasks (PathMNIST, BloodMNIST, DermaMNIST, and OrganMNIST3D) using official train/validation/test splits. We then compare (i) training from scratch, (ii) full fine-tuning from the multi-task checkpoint, and (iii) parameter-efficient fine-tuning (LoRA or adapters) that updates only a small fraction of parameters. Under a fixed compute budget (200 pretraining steps; 120 fine-tuning steps for 2D tasks; 50 steps for the 3D task), multi-task pretraining improved performance on PathMNIST (test accuracy 0.568 → 0.634; macro AUROC 0.886 → 0.914) and preserved most gains under PEFT (LoRA AUROC 0.909; Adapter AUROC 0.913) while training only 4,041–5,225 parameters versus 160,105 for full fine-tuning. For DermaMNIST, pretraining increased macro AUROC from 0.746 (Scratch, weighted) to 0.756 (Pretrain+Full), with similar AUROC under LoRA (0.760) and Adapter (0.763). In contrast, BloodMNIST and OrganMNIST3D showed mixed behavior, including cases where Scratch outperformed pretrained variants, indicating that transfer in this compact shared encoder is task-dependent and budget-sensitive. Calibration results were similarly non-monotonic: methods with better AUROC did not always achieve lower ECE. Overall, our results show that a small cross-modal multi-task model can serve as a practical MedMNIST-scale transfer baseline and that LoRA/adapters offer substantial parameter savings w…
subject: Articles
  topic: section
subject: medical foundation model
  topic: keywords
subject: multi-task learning
  topic: keywords
subject: MedMNIST
  topic: keywords
subject: parameter-efficient fine-tuning
  topic: keywords
creator: Gaotian Mi
creator: Tong Ye
creator: Dan Wood
rights: Copyright (c) 2025 Gaotian Mi, Tong Ye, Dan Wood https://creativecommons.org/licenses/by-sa/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 572
ending page: 589
doi: 10.51903/jtie.v4i3.492

Automatic Detection and Explanation of Dark Patterns from Interface Microcopy: Empirical Comparison of BERT-Style Encoders, RoBERTa-Style Encoders, and LLM-Style Decoders on the ec-darkpattern Dataset
type: item
description: Dark patterns (also called deceptive design patterns) are interface choices that steer or pressure users into unintended actions such as rushed purchases, unnecessary disclosures, or hard-to-cancel subscriptions. In e-commerce, many dark patterns are expressed directly in microcopy (e.g., button labels, banners, and inline messages), which makes text-only detection attractive for scalable auditing. This paper presents a fully reproducible experimental study on ec-darkpattern, a text-based dataset of e-commerce interface strings with balanced binary labels (1,178 dark pattern vs. 1,178 non-dark pattern) and seven dark pattern categories. We compare (i) a rule-based lexicon baseline, (ii) hashed n-gram linear models, (iii) a lightweight BERT-style bidirectional transformer encoder with word tokenization, (iv) a lightweight RoBERTa-style bidirectional transformer encoder with character tokenization, and (v) an LLM-style causal decoder trained as a classifier on the same inputs. On a fixed 80/10/10 split with seed 42, the best-performing model is a hashing + linear SVM baseline (F1=0.9437, ROC-AUC=0.9810), while the BERT-style encoder achieves F1=0.9038 (ROC-AUC=0.9695), the RoBERTa-style encoder achieves F1=0.8907 (ROC-AUC=0.9573), and the LLM-style decoder achieves F1=0.7884 (ROC-AUC=0.8808). These results should be interpreted as a controlled comparison under low-resource, no-pretraining conditions on a single fixed split, rather than as a general ranking of encoder-style versus decoder-style transformers. To support explainability, we generate token-level attributions using gradient-based saliency, summarize them as key phrases, and estimate explanation consistency via top-k token overlap on an exploratory 20-instance sample (mean Jaccard up to 0.7482 between the two character-based transformers). Finally, we curate an error-case library that links misclassifications to their most influential phrases. Within this short-microcopy setting, the findings show that lexic…
subject: Articles
  topic: section
subject: dark patterns
  topic: keywords
subject: deceptive design
  topic: keywords
subject: interface microcopy
  topic: keywords
subject: e-commerce
  topic: keywords
subject: text classification
  topic: keywords
creator: Haosen Xu
creator: Yushan Chen
creator: Aron Med
rights: Copyright (c) 2025 Yushan Chen https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 590
ending page: 612
doi: 10.51903/jtie.v4i3.491

From Hand-Drawn Sketches to Interactive Web Prototypes: A Reproducible Vision-Language Approach with Structural and Visual Consistency Evaluation
type: item
description: Service design workflows often begin with low-fidelity sketches that must be quickly translated into interactive prototypes. This paper studies the Sketch-to-Web problem: generating HTML/CSS prototypes from hand-drawn UI sketches and evaluating fidelity with both structural and visual metrics. Because the original Sketch2Code benchmark is distributed primarily as compressed artifacts that are not executable in our restricted runtime, we construct Sketch2Code-Synth, a size-matched and protocol-matched instantiation containing 731 hand-drawn-style sketches paired with 484 webpage prototypes while preserving the same sketch-to-HTML task interface. We implement a lightweight constrained sketch-to-HTML baseline (ProtoVLM) that combines HOG-based template recognition with template-conditioned HTML/CSS instantiation. We compare ProtoVLM against three baselines (kNN retrieval, heuristic computer vision layout extraction, and majority-template generation) and an oracle upper bound. Evaluation uses (i) DOM tree edit distance computed on a containment-induced layout tree, (ii) element-level IoU with Hungarian matching, and (iii) wireframe SSIM on 200×150 rasterized layouts. On the held-out test split (97 pages, 147 sketches), ProtoVLM achieves a mean tree edit distance of 2.224, mean element IoU of 0.755, and mean SSIM of 0.474. Relative to kNN retrieval, the main gain is in localization stability (IoU 0.755 vs. 0.697), while structural distance is similar (TED 2.224 vs. 2.422). Because the benchmark uses a controlled template library and wireframe renderings, the results should be interpreted as evidence on constrained layout recognition and prototype normalization rather than unconstrained real-world sketch understanding. In this setting, SSIM measures layout resemblance only, not interface realism or usability.
subject: Articles
  topic: section
subject: sketch-to-code
  topic: keywords
subject: web prototype generation
  topic: keywords
subject: vision-language model
  topic: keywords
subject: HTML/CSS synthesis
  topic: keywords
subject: service design
  topic: keywords
creator: Yushan Chen
creator: Maoxi Li
rights: Copyright (c) 2025 Yushan Chen https://creativecommons.org/licenses/by-sa/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 364
ending page: 384
doi: 10.51903/jtie.v4i2.490

LiDAR–Camera Object-Level Fusion for Multi-Target Tracking Using JPDA and EKF: A Reproducible Empirical Study on a PandaSet-Parameterised Five-Sequence Dataset
type: item
description: Multi-target tracking in cluttered scenes is essential for automated driving, where downstream planning requires stable object identities and accurate state estimates. This paper provides a fully reproducible empirical and sensitivity study of a classical object-level LiDAR–camera fusion tracker that combines Joint Probabilistic Data Association (JPDA) with an Extended Kalman Filter (EKF) under a constant-velocity state model. Because the MathWorks PandaSet subset is distributed as a ZIP archive that cannot be ingested into our execution environment, we generate a PandaSet-parameterised five-sequence synthetic dataset with explicitly specified sampling rates, measurement noise, detection probabilities, and Poisson clutter, and report end-to-end results with fixed random seeds. Using sequential fusion (LiDAR JPDA–EKF update followed by a camera bearing update), we obtain a mean MOTA of 0.880 and a mean position RMSE of 0.361 m, compared with LiDAR-only JPDA–EKF MOTA of 0.883 and RMSE of 0.395 m. Fusion, therefore, improves localization accuracy while sometimes reducing MOTA due to additional association ambiguity introduced by camera clutter; this trade-off is discussed in terms of downstream use cases that prioritize state accuracy. Sensitivity sweeps show that probabilistic association degrades more gracefully than hard nearest-neighbor assignment as clutter increases and delineate regimes where camera information is beneficial. A camera-only bearing tracker is included as a diagnostic baseline (not as a competitive approach); as expected, given the observability limits, it is not reliable under the studied clutter conditions. The dataset specification, parameters, and reporting artefacts form a reproducible template for diagnosing JPDA/EKF tracking and object-level fusion.
subject: Articles
  topic: section
subject: LiDAR–camera fusion
  topic: keywords
subject: multi-target tracking
  topic: keywords
subject: JPDA
  topic: keywords
subject: extended Kalman filter
  topic: keywords
subject: data association
  topic: keywords
creator: Qi Xin
rights: Copyright (c) 2026 Qi Xin https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-25
publication date: 2026-02-25
starting page: 54
ending page: 76
doi: 10.51903/jtie.v5i1.486

Uncertainty-Aware Late Fusion for 3D Perception (Confidence Calibration + Fusion Rule Learning)
type: item
description: Late fusion remains attractive for multi-sensor 3D perception because it preserves independent sensor pipelines, enables modular upgrades, and supports rigorous ablation experiments. This paper presents an uncertainty-aware late-fusion framework that combines per-modality confidence calibration with learning a fusion rule. We conduct full experimental evaluations on a PandaSet-style LiDAR+camera subset comprising 10 multi-frame sequences and 2,200 synchronized frames, with 49,549 annotated 3D objects across the Car, Pedestrian, and Cyclist classes. The framework calibrates LiDAR and camera confidence using temperature scaling and isotonic regression, estimates uncertainty-conditioned localization variance, and fuses associated candidates using multiple rules (max, mean, product/odds, and Dempster-Shafer) as well as a learned fusion rule (logistic regression trained on association features). On the test split, isotonic calibration reduces LiDAR Expected Calibration Error from 0.260 to 0.006 and Negative Log-Likelihood from 0.410 to 0.110, and it similarly improves camera confidence quality. Although mean Average Precision (mAP) remains similar to a LiDAR-only baseline in this controlled setting, calibrated late fusion provides substantially better decision reliability at fixed confidence thresholds and maintains conservative high-precision behavior under camera dropout. These results support an engineering conclusion: confidence calibration is the highest-leverage upgrade for late fusion in safety-critical stacks, and fusion rule choice can be tuned to downstream risk preferences.
subject: Articles
  topic: section
subject: 3D perception
  topic: keywords
subject: late fusion
  topic: keywords
subject: confidence calibration
  topic: keywords
subject: uncertainty estimation
  topic: keywords
subject: LiDAR-camera fusion
  topic: keywords
creator: Qi Xin
rights: Copyright (c) 2025 Qi Xin https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 215
ending page: 238
doi: 10.51903/jtie.v4i1.485

LLM-Driven CI Failure Diagnosis and Automated Repair: From GitHub Actions Logs to Patch Recommendation
type: item
description: Continuous Integration (CI) pipelines surface regressions early but also produce long, noisy logs. Diagnosing a failing GitHub Actions run and drafting a safe repair patch can be time-consuming, especially when dealing with dependency drift or configuration errors. We study a practical CI-repair pipeline decomposed into three measurable tasks: (1) coarse failure-type classification, (2) retrieval-based repair (log similarity reuse the closest historical fix diff), and (3) constrained patch generation that emits a unified diff via template+slot filling. The pipeline follows the schema and task framing of JetBrains-Research’s lca-ci-builds-repair dataset from Long Code Arena (212 samples). Because runtime restrictions in our environment prevent downloading the original Hugging Face-hosted parquet files, all quantitative results in this paper are evaluated on a locally generated proxy dataset, CI-Repair-Sim212, which matches the benchmark’s field schema and evaluation protocol. On CI-Repair-Sim212, failure-type classification reaches a ceiling (Macro-F1=1.000), whereas repair-pattern prediction remains harder (Macro-F1=0.796 with log+workflow). For patch recommendation, retrieval achieves Token-F1@1=0.898 and Pattern@1=0.783 when combining logs with workflow context, and constrained generation further improves diff similarity to Token-F1=0.923. Across tasks, adding workflow YAML context yields consistent gains, motivating hybrid CI assistants that prioritize retrieval when near-duplicate failures exist and fall back to constrained generation when close matches are absent.
subject: Articles
  topic: section
subject: CI/CD
  topic: keywords
subject: GitHub Actions
  topic: keywords
subject: failure diagnosis
  topic: keywords
subject: automated program repair
  topic: keywords
subject: retrieval-augmented generation
  topic: keywords
creator: Hanqi Zhang
rights: Copyright (c) 2025 Hanqi Zhang https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 190
ending page: 214
doi: 10.51903/jtie.v4i1.484

A Comparative Study on Self-Organization in Wireless Sensor Networks
type: item
description: Wireless sensor networks (WSNs) have emerged as a critical infrastructure for distributed sensing platforms in recent years. Their effective implementation requires self-organizing features that can adapt to rapidly changing ecological conditions. We have noticed in the comparative study that despite extensive research on individual self-organizing mechanisms, e.g., clustering, routing, and topology management. We believe there exists a significant analytical gap in systematically comparing these approaches across key performance metrics. Our study addresses this gap by conducting a comprehensive comparative analysis of four primary self-organization or autonomious mechanisms: clustering-based organization, dynamic routing protocols, topology adjustment strategies, and coverage reinforcement methods. In our work, using a simulation-based methodology with the NS-3 network simulator, we thoroughly tested these frameworks across networks with 50 to 500 nodes under varying traffic loads and mobility patterns. We assessed the performance using three key KPIs (key performance indicators). Reliability is measured by packet delivery ratio, scalability by convergence time, and energy efficiency by network lifetime parameters. Our results demonstrate that clustering approaches achieve 23% better energy efficiency in static deployments, whereas distributed routing protocols provide 34% better scalability in dynamic conditions. We also observed that topology adjustment mechanisms improve reliability by 18% under high node failure rates. These findings provide clear, evidence-based guidance for selecting the right self-organization technique for specific deployment scenarios and application requirements. We recommend that future research investigate hybrid mechanisms that combine multiple approaches and explore integrating machine learning to support adaptive strategy selection under heterogeneous network conditions.
subject: Articles
  topic: section
subject: Clustering
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Performance Metrics
  topic: keywords
subject: Reliability
  topic: keywords
subject: Scalability
  topic: keywords
subject: Self-Organization
  topic: keywords
subject: wireless sensor network
  topic: keywords
creator: Michael Simon
creator: Salwa M. Din
creator: Raja Jamal Chib
rights: Copyright (c) 2026 Michael Simon, Salwa M. Din, Raja Jamal Chib https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-18
publication date: 2026-02-18
starting page: 39
ending page: 53
doi: 10.51903/jtie.v5i1.483

A Novel Three-phase integrated inverter for Hybrid Electric Vehicle Applications
type: item
description: Multilevel inverters (MLIs) have become essential in power electronics due to their capability to improve power quality, minimize harmonic distortion and enhance efficiency in medium and HV applications. It achieves smoother waveform, reducing total harmonic distortion (THD). Hybrid electric vehicles (HEVs) are increasingly recognized for their ability to reduce fuel consumption and emissions in modern transportation. Their performance largely depends on the efficiency of power conversion systems, particularly in inverter setup. In this study a novel 3-phase 25-level MLI configuration is proposed with a fewer switches. The primary objective is to reduce harmonics through high-level MLI implementation and motor optimization in electric vehicle applications. Proposed research suggests a novel three-phase 25-level hybrid inverter tailored for hybrid electric vehicles, featuring innovative hybrid DC-DC and DC-AC topologies that utilize fewer switches to enhance cost-effectiveness. Simulation results indicate, the three-phase 25-level MLI improves voltage stability and further mitigates harmonics compared to single-phase configuration. While comparing with conventional converters, MLIs offer significant benefits, including high-frequency switching and advanced modulation techniques for precise control. It generates output voltage with minimal harmonics, lower dv/dt stress on switches and introduces common-mode voltage. In particular, the 3-phase 25-level inverter system, achieves a smoother waveform, reducing total harmonic distortion (THD). The proposed system has been validated using MATLAB/Simulink. Additionally, the comparative analysis between proposed 1-ph. and 3-ph. 25-level MLI topologies also presented.
subject: Articles
  topic: section
subject: Hybrid Electric Vehicles (HEV),
  topic: keywords
subject: Multilevel Inverter (MLI)
  topic: keywords
subject: Pulse Width-Modulation (PWM)
  topic: keywords
subject: Phase-Disposition (PD)
  topic: keywords
subject: Total Harmonic Distortion (THD),
  topic: keywords
creator: Tatarao Donepudi
creator: Anupalli Immanuel
rights: Copyright (c) 2026 Tatarao Donepudi, Anupalli Immanuel https://creativecommons.org/licenses/by-sa/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 288
ending page: 307
doi: 10.51903/jtie.v5i1.482

IoT-Driven in the Banking Application Platforms Using a Real-Time SQL Injection Mitigative Measures
type: item
description: The Internet of Things (IoT) integration into banking systems has revolutionized banking operations while also posing threats, including SQL injection (SQLi) attacks. Thus, the defenses of the existing system, such as access control mechanisms, firewalls, and signature-based Intrusion Detection Systems (IDSs), failed to detect both novel and obfuscated SQLi attempts. Hence, this research developed a machine-learning-based detection framework capable of identifying SQLi attacks on IoT-driven banking platforms. The model was trained on a Random Forest (RF) classifier and evaluated in a Python environment. Streamlit was used to deploy the model for real-time prediction, while performance visualization was through the Power BI dashboard. However, the results from the model’s evaluation were highly impressive, with 99.53% accuracy, 99.96% precision, and 98.78% recall. This demonstrated the model's ability to detect both known and unknown SQL patterns. However, the research concluded that combining behavioural analytics with a machine-learning approach is highly effective for securing IoT banking platforms and recommended periodic retraining using a deep-learning approach.
subject: Articles
  topic: section
subject: Intelligent Threat Detection
  topic: keywords
subject: IoT-Driven Banking Platform
  topic: keywords
subject: Real-Time SQL Injection.
  topic: keywords
creator: Amaka Eugenia Ngozi
creator: Oji Victor Kalu
creator: Ezea Jonathan Ikechukwu
creator: Okpalla Chidimma Lilian
creator: Gloria Ngozi Ezeh
rights: Copyright (c) 2026 Amaka Eugenia Ngozi, Oji Victor Kalu, Ezea Jonathan Ikechukwu, Okpalla Chidimma Lilian, Gloria Ngozi Ezeh https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
starting page: 77
ending page: 92
doi: 10.51903/jtie.v5i1.481

Automation in Cybersecurity using Machine Learning: A CaseStudy on Anomaly Detection with Isolation Forest
type: item
description: The escalating sophistication of cyber threats necessitates advanced anomaly detection techniques that transcend traditional signature-based methods. This paper presents an automated cybersecurity framework leveraging the Isolation Forest algorithm for unsupervised anomaly detection in network traffic. Using the NSL-KDD dataset, we demonstrate that Isolation Forest achieves 95.2% detection accuracy with a 4.7% false-positive rate, outperforming conventional methods such as One-Class SVM (88.1% accuracy) and Local Outlier Factor (82.3% accuracy) in both computational efficiency and precision. Key advantages include: (1) real-time processing capability (8.2s training time, 4× faster than density-based approaches), (2) effective identification of rare attack types (U2R/R2L), and (3) elimination of dependency on labeled training data. The proposed system integrates dynamic threshold tuning and SHAP-based feature weighting to enhance detection stability and reduce false alarms. The results validate Isolation Forest as a scalable and reliable solution for modern intrusion detection systems, with strong implications for SIEM integration and real-time cybersecurity automation. Challenges in parameter tuning and encrypted traffic analysis are discussed, alongside future directions involving hybrid deep learning architectures.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Isolation Forest
  topic: keywords
subject: Intrusion Detection System
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Noorul Hassan S.
creator: Sandhiya L.
creator: Kavya S.
creator: Priyadharshini E.
creator: Vanmathi T.
rights: Copyright (c) 2025 Noorul Hassan S., Sandhiya L., Kavya S., Priyadharshini E., Vanmathi T. https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 613
ending page: 624
doi: 10.51903/jtie.v4i3.478

An IoT-Based Smart Feeding System for Koi Fish Using Mamdani Fuzzy Logic
type: item
description: Koi feeding management requires precision in both feeding timing and feed quantity to maintain fish health and reduce mortality rates. Manual feeding practices are often inconsistent due to human limitations, leading to overfeeding or underfeeding. This study proposes an IoT-based smart feeding system for koi fish that integrates Mamdani fuzzy logic to determine adaptive feeding durations based on feed stock conditions. The system employs a NodeMCU ESP8266 microcontroller, an ultrasonic sensor for feed-level monitoring, a servo motor for feed dispensing, and the Blynk platform for real-time remote monitoring and control over the internet. Mamdani fuzzy inference is utilized to classify feed levels into linguistic variables (low, medium, and high) and generate appropriate feeding actions. Experimental results demonstrate that the proposed system operates reliably, with an average measurement error of 1.59%, indicating high accuracy in feed-level detection. The fuzzy logic controller effectively adjusts feeding duration according to feed availability, enabling consistent and controlled feeding schedules. The proposed system offers a practical and low-cost solution for intelligent koi fish feeding management and can be extended to broader applications in smart aquaculture systems.
subject: Articles
  topic: section
subject: Fuzzy Logic
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Koi FIsh
  topic: keywords
subject: Smart Feeding
  topic: keywords
creator: April Firman Daru
creator: Alauddin Maulana Hirzan
creator: Galuh Ardiansyah Putra
creator: Paminto Agung Christianto
rights: Copyright (c) 2026 April Firman Daru, Alauddin Maulana Hirzan, Galuh Ardiansyah Putra, Paminto Agung Christianto https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-30
publication date: 2026-04-30
starting page: 93
ending page: 106
doi: 10.51903/jtie.v5i1.480

Intelligent Image Rights Protection System Using Machine Learning, Cloud Services and IoT Alerts
type: item
description: The rapid growth of digital media platforms has intensified the misuse of images through unauthorized manipulation, morphing, and non-consensual redistribution, posing significant threats to individual privacy and intellectual property rights. Despite the availability of reporting and takedown mechanisms, their effectiveness remains limited due to procedural complexity, delayed response times, and concerns regarding user anonymity. This paper presents the Smart Image Rights Protection (SIRP) system, a user-centric framework designed to detect, monitor, and respond to unauthorized use of images in online environments. The proposed system utilizes perceptual hashing to generate resilient digital fingerprints for registered images, enabling accurate identification after common transformations such as resizing, cropping, and minor visual alterations. A cloud-ready similarity analysis module is designed to support scalable matching in future deployments, while the current evaluation is conducted on a controlled dataset. An IoT-enabled hardware interface provides real-time alerts to users upon detection of potential misuse. Experimental results on controlled manipulation scenarios show that SIRP achieves detection accuracy of 95.6% for resized images and 94.3% for cropped images, outperforming traditional pixel-based comparison methods. Furthermore, automated evidence logging and instant notifications substantially reduce the latency between detection and user response actions. By combining robustness under common transformations, cloud-assisted processing, and timely user engagement, SIRP offers a practical solution for protecting digital image ownership and personal privacy.
subject: Articles
  topic: section
subject: Digital Image Protection
  topic: keywords
subject: IoT-Based Monitoring
  topic: keywords
subject: Real-Time Detection
  topic: keywords
subject: Perceptual Hashing
  topic: keywords
subject: Image Tamper Detection
  topic: keywords
creator: S. P. Yaamini
creator: K. Sivaranjani
creator: B. Nujithra
creator: V. Anitha
rights: Copyright (c) 2026 S. P. Yaamini, K. Sivaranjani, B. Nujithra, V. Anitha https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 107
ending page: 123
doi: 10.51903/jtie.v5i1.477

Bias and Hallucination Evaluation in LLMs
type: item
description: The largest failure modes of LLMs to-date, bias and hallucination, have measurable harms in contexts where factuality and fairness are paramount. Both areas have experienced significant research growth; however, prior work on each generally operates as a disparate body of research, and there is a gap in a methodological framework for jointly measuring, tracing, and reducing both under the same experimental conditions. We provide that framework through an empirical evaluation (not a survey) of bias propagation and hallucination generation on four illustrative domains (medical, legal, finance, human resources) through a framework that addresses the three research questions: how can bias and hallucination be measured simultaneously through a replicable, domain-specific protocol; which techniques yield statistically meaningful improvements and a consistency of effectiveness; and how do causally informed methods fare against retrieval methods when tested for factual error reduction. We report new experiments using the GPT-4, LLaMA-2, and Falcon-7B models on the MIMIC-III, CrowS-Pairs, Yahoo Finance Q3 and XNLI-HR benchmarks while keeping our prompts uniform and our random seeds fixed. Methods included structural causal modeling, retrieval-augmented generation, uncertainty-aware RLHF, and hallucination-specific fine-tuning, with experiments on each method separately before merging them into combined frameworks. We observe that RAG achieved a 45% reduction in hallucination rates and that our causally guided active learning method reduced bias disparity by 25%; together, they substantially outperform either method alone. This contributes to a repeatable method for auditing bias and hallucinations, helping ensure alignment with EU AI Act standards and similar requirements.
subject: Articles
  topic: section
subject: Bias
  topic: keywords
subject: Hallucination
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Causal Inference
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
creator: R Sathiyaseelan
creator: A. B. Reshma
creator: P. Ganga
rights: Copyright (c) 2026 R Sathiyaseelan, A. B. Reshma, P. Ganga https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-30
publication date: 2026-04-30
starting page: 271
ending page: 287
doi: 10.51903/jtie.v5i1.476

AI-Driven Multi- Modal Fake Content Detection System Using Audio-Text Fusion and Transformer Network
type: item
description: The rapid proliferation of AI-generated synthetic media has posed substantial threats to digital trust, particularly through audio deepfakes and manipulated text. Existing unimodal detection systems that analyze either audio or text in isolation remain insufficient to counter advanced generative attacks that exploit both modalities simultaneously. This paper proposes an AI-driven multimodal fake content detection framework that jointly leverages acoustic and linguistic signals to enable robust deepfake identification. Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-Spectrograms are extracted from raw audio to capture spectral and temporal vocal patterns. At the same time, BERT-based transformer embeddings encode semantic and contextual information from transcripts generated via Automatic Speech Recognition (ASR). An attention-based fusion layer dynamically weights and integrates both feature streams, and a Random Forest–XGBoost ensemble classifier performs the final authenticity prediction. Experiments conducted on the ASVspoof 2019 benchmark demonstrate a classification accuracy of 95%, with precision of 93%, recall of 94%, and F1-score of 95%, outperforming standalone audio-only and text-only baselines by approximately 4–7%. These findings confirm that cross-modal feature fusion substantially reduces false-detection rates and improves generalization over single-modality approaches. The proposed system offers practical applicability in cybersecurity, voice biometrics, and digital forensics.
subject: Articles
  topic: section
subject: Audio-Text Fusion
  topic: keywords
subject: Deepfake
  topic: keywords
subject: Fake Content Detection
  topic: keywords
subject: Transformer
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: S. Jeeva
creator: J. S. Trisha
creator: S. Keerthana
rights: Copyright (c) 2026 S. Jeeva, J. S. Trisha, S. Keerthana https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-30
publication date: 2026-04-30
starting page: 256
ending page: 270
doi: 10.51903/jtie.v5i1.475

Self-Supervised Representation Learning for Criminology: Detecting Anomalies, Classifying Reports, and Mapping Networks
type: item
description: Crime analysis using various types of data, such as video surveillance, crime reports, and criminal networks, has been widely investigated in digital criminology. Most of the available data are unlabelled. In this work, we introduce a self-supervised learning framework for multimodal criminology, which enables the fully automatic learning of effective features for unlabelled video, text, and graph datasets and the completion of crime analysis tasks, including anomaly detection, crime report classification, and high-risk node prediction via contrastive learning, masked prediction, and graph self-supervised learning. The experimental results show that our SSL model learns high-quality features and achieves better performance than its supervised counterpart and baseline models. Unlike traditional deep learning-based models that require large amounts of labeled data, our proposed SSL model is label-efficient, scalable, and robust to artificial or anonymous data. Our work aims to develop an AI-based multimodal self-supervised learning approach for efficient, accurate, reliable, and safe crime analysis
subject: Articles
  topic: section
subject: Self-Supervised Learning
  topic: keywords
subject: Multimodal Analysis
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Crime Prediction
  topic: keywords
subject: Graph Representation
  topic: keywords
creator: Noorul Hassan S.
creator: Sivalakshmi S.
creator: Janani M.
creator: Fouziya A.
creator: Thirisha S.
rights: Copyright (c) 2026 Noorul Hassan S., Sivalakshmi S., Janani M., Fouziya A., Thirisha S. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 143
ending page: 159
doi: 10.51903/jtie.v5i1.473

IoT-Enabled Multimodel Emotion Detection and Assistive Smartwatch for Special-Needs Children
type: item
description: Children with neurodevelopmental and communication challenges often find it difficult to express emotional discomfort, stress, or urgent needs, which can delay timely support and increase anxiety. To address this issue, this paper presents an IoT-enabled assistive smartwatch designed to support near real-time emotion inference and basic communication for children with special needs. The proposed system integrates physiological sensing, including heart rate variability, galvanic skin response, and skin temperature, with camera-based facial expression analysis using a multimodal data fusion approach. In addition to emotion-aware monitoring, the smartwatch provides a simple icon-based communication interface and a routine reminder module to support daily activities. The framework enables caregiver-side visualization of emotional trends, communication events, and adherence to routines through a cloud-based dashboard. The system is evaluated through a design-level feasibility assessment using simulated experiments and analysis of benchmark datasets. Overall, this work presents a technically feasible and ethically conscious assistive framework that highlights the potential of combining IoT and emotion-aware computing for supportive care applications.
subject: Articles
  topic: section
subject: IoT
  topic: keywords
subject: Smartwatch
  topic: keywords
subject: Physiological Sensing
  topic: keywords
subject: Facial Expression Recognition
  topic: keywords
subject: Multimodel Data Fusion.
  topic: keywords
creator: A. Samrin
creator: S. Saidharshini
creator: S. Shalima
creator: B. Moohambiga
rights: Copyright (c) 2026 A. Samrin, S. Saidharshini, S. Shalima, B. Moohambiga https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 124
ending page: 142
doi: 10.51903/jtie.v5i1.471

Privacy-Robust Incrementality Estimation in Cookieless Settings via Uplift Modeling: Reproducible Evidence from the Hillstrom E-Mail Experiment
type: item
description: Measuring advertising incrementality in the absence of user-level identifiers is increasingly constrained by platform policies and privacy regulations. In cookieless environments, practitioners often observe only aggregated or weak signals (e.g., cohort-level conversion counts) and must still estimate the causal lift of an intervention while quantifying uncertainty. This paper studies cookieless incrementality evaluation through the lens of uplift and individual treatment effect (ITE) modeling under explicit privacy constraints. We conduct full experimental evaluations on the MineThatData (Hillstrom) E-Mail Analytics Challenge dataset (64,000 customers in a randomized controlled experiment with three arms). We cast the task as a binary treatment problem—sending any e-mail campaign versus sending none—and compare six ITE estimators (S-, T-, X-, R-, and doubly robust learners, plus transformed-outcome regression) against cohort-only estimators that emulate cookieless measurement. The cohort estimator uses only aggregated counts and a Bayesian beta–binomial model to shrink noisy rates, and we evaluate robustness under k-anonymity thresholds and Laplace-noised differentially private aggregates. Across held-out test data, the best ID-level model (T-learner with logistic regression) achieves a Qini coefficient of 6.675 and improves the estimated policy conversion rate when targeting the top 20% of customers by predicted uplift. Cohort-only estimation retains a weaker and more variable signal; its point estimate is sensitive to privacy constraints but yields valid uncertainty intervals with 0.892 empirical coverage for a 95% interval in cohort-level validation. The results demonstrate that (i) causal lift is estimable without identifiers when randomized experimentation is available, (ii) doubly robust estimators provide strong performance and fast scoring, and (iii) privacy-preserving aggregation introduces an accuracy–privacy trade-off that can be quantified and monitored…
subject: Articles
  topic: section
subject: Cookieless Measurement
  topic: keywords
subject: Incrementality
  topic: keywords
subject: Uplift Modeling
  topic: keywords
subject: Heterogeneous Treatment Effects
  topic: keywords
subject: Differential Privacy
  topic: keywords
creator: Jingwen Bai
creator: Haozhe Wang
creator: Qiyou Wu
creator: Boning Zhang
rights: Copyright (c) 2026 Jingwen Bai, Haozhe Wang, Qiyou Wu, Boning Zhang https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-18
publication date: 2026-02-18
starting page: 17
ending page: 38
doi: 10.51903/jtie.v5i1.468

A Constrained, Data-Driven Budgeting Framework Integrating Macro Demand Forecasting and Marketing Response Modeling
type: item
description: Budgeting and financial planning & analysis (FP&A) increasingly require combining macroeconomic signals, channel-level marketing effectiveness, and hard accounting constraints into a single, auditable decision process. This paper proposes and empirically evaluates an end-to-end framework that (i) forecasts category-level demand from public macro data, (ii) learns diminishing-returns marketing response curves, and (iii) solves a constrained portfolio optimization problem to allocate marketing spend while satisfying SG&A and cash-flow guardrails consistent with real public-company statements. Using quarterly Personal Consumption Expenditures (PCE) components from FRED (durable goods, nondurable goods, and services) as a proxy for market demand, we compare seasonal naïve, SARIMAX, gradient boosting, and a multivariate VAR model in a rolling backtest (2018Q1-2025Q3). In parallel, we estimate marketing response from the Advertising dataset (TV, radio, and newspaper spend) via linear models, gradient boosting, and a Hill-function saturation model. We then calibrate financial constraints-gross margin, SG&A ratio, and operating cash-flow coverage-directly from Apple Inc.’s FY2025 Form 10-K filed with the SEC, and integrate all components into a Monte Carlo-evaluated budgeting optimizer. Results show that multivariate models improve total-demand accuracy (≈2.85% MAPE) and that nonlinear response curves indicate strong diminishing returns and negligible incremental value for newspaper spend. The constrained optimizer produces stable allocations that trade off expected operating profit and downside risk, and it highlights a practical insight: budgets that exactly meet a ratio-based cap under point forecasts may violate constraints under realistic demand uncertainty. The proposed workflow is fully reproducible from public data sources and provides a template for transparent, constraint-aware budgeting.
subject: Articles
  topic: section
subject: budget optimization
  topic: keywords
subject: FP&A
  topic: keywords
subject: demand forecasting
  topic: keywords
subject: marketing mix
  topic: keywords
creator: Yifei Lu
creator: Hailin Zhou
creator: Yitian Zhang
rights: Copyright (c) 2025 Yifei Lu, Hailin Zhou, Yitian Zhang https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 493
ending page: 520
doi: 10.51903/jtie.v4i3.466

User-Centered Mobile Navigation: Evaluating Local Usability for Improved UX
type: item
description: Mobile navigation interfaces continue to be plagued by the key usability problem, particularly where widely accepted design traditions fail to address the specific local user population requirements and mental models. This research addresses this significant limitation by using a thorough user-focused measurement framework to search for and investigate context-dependent navigation problems in mobile applications. The research employed a multi-method qualitative approach, using in-depth questionnaires, semi-structured interviews, and laboratory-based usability testing of 15 participants on an interactive Figma prototype that simulated real-world navigation tasks. Our analysis step by step revealed significant navigation issues within the local context, quantified using a 73% task completion rate and a 2.8 participant error average for the core navigation tasks. The most significant usability issues were ambiguous iconography, inconsistent application of platform design patterns, and insufficient system feedback mechanisms. The results conclusively demonstrate that localized usability testing is not merely beneficial but necessary while creating genuinely good and accessible mobile experiences. The study provides a replicable, practical context-aware evaluation approach and tangible, right-now applicable design recommendations, for example, the need to enhance icons with descriptive text labels and use instant visual feedback mechanisms. The study provides developers and designers tangible take-away recommendations for significantly enhancing navigation user experience without trading off methodological availability and implementation convenience in different development environments.
subject: Articles
  topic: section
subject: User-Centered Design
  topic: keywords
subject: Mobile Navigation
  topic: keywords
subject: Usability Evaluation
  topic: keywords
subject: User Experience (UX)
  topic: keywords
subject: Local Context
  topic: keywords
creator: Sofia Petrova
creator: Ken Watanabe
rights: Copyright (c) 2025 Sofia Petrova, Ken Watanabe https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 478
ending page: 492
doi: 10.51903/jtie.v4i3.457