Interdisciplinary Journal of AI, Machine Learning & Data Science
The Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) is a peer-reviewed, open-access journal published by Scholiora Research. The journal publishes high-quality research articles, reviews, and innovative studies in artificial intelligence, machine learning, data science, and intelligent systems. IJAIMLDS promotes ethical and responsible AI research and ensures global accessibility through its open-access model.
All articles published in Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to share, copy, distribute, and adapt the work, provided that proper credit is given to the original author(s) and the source. Authors retain copyright and grant Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) the right of first publication.
Effects of Constraint Quality and Distribution on Semi-Supervised Clustering Performance
Semi-supervised clustering enhances traditional clustering algorithms by leveraging prior knowledge, such as class labels or pairwise constraints, to improve clustering quality. Despite numerous proposed methods, the effects of constraint set quality and distribution—particularly imbalanced or erroneous constraints—remain poorly understood. This paper investigates these issues through systematic simulations, evaluating six state-of-the-art semi-supervised clustering algorithms under imbalanced or incorrect constraint conditions. Experiments on multiple real-world and synthetic benchmark datasets reveal that must-link constraints generally provide greater benefit than cannot-link constraints, while relying solely on cannot-link constraints may reduce performance. Furthermore, incorrect constraints—especially erroneous must-link constraints—can significantly degrade clustering performance. These findings underscore the critical importance of carefully designing constraint sets to achieve reliable and effective semi-supervised clustering, providing clear guidance on the conditions under which different algorithmic approaches perform best.
Copyright (c) 2026 Tianshu Yang, Nicolas Pasquier (Author) https://creativecommons.org/licenses/by/4.0
A Comprehensive Review of Machine Learning and Deep Learning Techniques for Sign Language Recognition.
Sign language serves as the primary means of communication for the deaf and hard-of-hearing community. However, communication barriers still exist between sign language users and the general population. Recent advances in machine learning (ML) and deep learning (DL) have enabled automated sign language recognition (SLR) systems capable of translating gestures into text or speech. This paper presents a systematic review of ML- and DL-based techniques used for SLR across various languages, including American Sign Language and Arabic Sign Language. The review examines classical machine learning approaches, convolutional neural networks (CNNs), hybrid architectures such as CNN-LSTM and CNN-HMM, and video-based models including 3D CNNs. Comparative analysis indicates that deep learning methods significantly outperform traditional techniques due to their ability to automatically extract spatial and temporal features. However, challenges such as limited datasets, signer variability, computational complexity, and real-time deployment constraints remain significant. This review identifies research gaps and discusses future directions, including multimodal learning, lightweight architectures, and multilingual datasets, to improve the scalability and applicability of SLR systems.
Copyright (c) 2026 shivani Gupta, Dr. Sumit Jain, Rakhi Jha (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid Deep Learning Framework with Swarm-Optimized Ensemble Learning for Depression Detection from Social Media Text
Depression is a pervasive mental health disorder and a leading contributor to global disability, often remaining undiagnosed due to social stigma and the limitations of traditional screening methods. The widespread use of social media platforms generates a rich corpus of textual data that can serve as a passive indicator of an individual’s mental state. This paper proposes a novel hybrid deep learning framework for the detection of depressive symptoms from social media text. The framework integrates a Convolutional Neural Network (CNN) for extracting local linguistic features with a Bidirectional Long Short‑Term Memory (BiLSTM) network for capturing long‑range contextual dependencies. To further enhance performance, the model is integrated into an ensemble learning architecture, where the hyperparameters of the individual models are optimized using a Particle Swarm Optimization (PSO) algorithm. The proposed methodology is evaluated on a publicly available dataset of Reddit posts annotated for depressive content. Experimental results demonstrate that the proposed hybrid ensemble model, optimized via PSO, achieves superior performance in terms of accuracy, precision, recall, and F1‑score compared to several baseline machine learning and standard deep learning models. The findings underscore the efficacy of combining feature extraction techniques with optimized ensemble strategies for developing robust, automated depression detection systems.
Copyright (c) 2026 Nikhil Karale, Vijay Gulhane (Author) https://creativecommons.org/licenses/by/4.0
An End-to-End Multi-Task ResNet-FPN Network for Simultaneous Plant Type, Disease Type, and Severity Classification
Accurate and rapid diagnosis of plant diseases is critical for food security and sustainable agriculture. This paper proposes a multi-task deep learning framework that simultaneously classifies plant species, disease type, and three severity levels (mild, medium, severe) from leaf images. The model employs a ResNet101 backbone enhanced with a Feature Pyramid Network (FPN) to extract multi-scale features, followed by three task-specific classification heads. Severity labels are automatically generated using a semantic segmentation approach based on adaptive thresholding in LAB and HSV color spaces, eliminating the need for manual annotation. To address class imbalance, the dataset is balanced via oversampling with on-the-fly data augmentation. Extensive experiments on the PlantVillage dataset demonstrate that the multi-task model achieves near‑perfect plant classification (macro F1 0.9995) and disease classification (macro F1 0.9978), and 99.1% macro F1 for severity estimation, outperforming single-task baselines while using 66% fewer parameters. Comprehensive evaluation including confusion matrices, per-class precision/recall/F1, ROC curves, and sample predictions are provided. The proposed framework offers an integrated solution for plant health monitoring with high accuracy and efficiency.
Copyright (c) 2026 Dr. Md. Matiqul Islam (Author) https://creativecommons.org/licenses/by/4.0
EdgeAware: Distributed Threat Detection and Monitoring System for Smart Cities
EdgeAware is a distributed threat detection and monitoring system designed for smart-city applications where quick and coordinated actions are required for public safety and infrastructure management. Traditional city monitoring systems often rely on centralized processing and single threat event processing, and it can suffer from high latency, heavy bandwidth usage, limited scalability, and reduced reliability under real-world variability such as different viewpoints and domains. Many academic prototypes lack operational support for validation of event and multi-event monitoring. To address these gaps, EdgeAware integrates edge computing with a centralized monitoring workflow. The system demonstrates two multi-view object detection models for the detection of accident and pothole events, trained on YOLOv11s architecture using diverse perspective images from dashcam, drones, and surveillance system. It improves generalization across different domains and viewpoints. For edge deployment, models are converted to TensorFlow Lite and executed in an Android application that performs real-time inference on live camera streams. The app supports IoU-based tracking to filter duplicate detections, enabling event-level reporting. It transmits only representative frame per unique event, encrypted together with metadata and location information for secure and bandwidth-efficient communication. A backend platform is used for ingesting the events and providing a monitoring dashboard with visualization using Here Maps. This is for administrative validation and coordination with response teams such as police, ambulance, and road maintenance teams. The experimental results have also shown good quality in the detection aspect, with mAP@0.5 at 93.7%, as well as good precision and recall for the models. The edge pipeline also resulted in an average time of approximately 90 ms and a runtime end-to-end latency of 1.5 s, as well as a reduced storage footprint through compression.
Copyright (c) 2026 Amit Patil, Dr. Rajesh Bansode (Author) https://creativecommons.org/licenses/by/4.0
REMIX-FND: A Multi-Modal Domain-Invariant Framework with Adaptive Evidence Retrieval for Cross-Domain Fake News Detection
Moreover, it generalizes well, facilitates evidence utilization, and remains applicable as generated text by machines proliferates. In contrast, existing approaches frequently release incomplete models rather than a complete, deployable, and reproducible stack. REMIX-FND is a deployable, open-source framework that fills this existing gap. We combine multi-modal fusion with a novel application of Domain-Invariant Meta-Learning (DIML), meaning domain adversarial and MAML-style training. In addition, Monte Carlo dropout is employed for uncertainty-conditioned evidence retrieval depth, a Dynamic Source Reliability Graph (DSRG) for temporally decaying source reliability, and a six-detector ensemble for AI-generated text detection. The stack provides a reference API for tiered explanations, as well as tag-stratified diagnostics on a fixed test file. On the standard FakeNewsNet split, domain adversarial training boosts the weighted F1 score from 85.22 to 85.47 compared to a baseline of DistilRoBERTa, but a preliminary DIML checkpoint fails to perform as well as this run on the same split. Six-class stance classification on LIAR achieves 46.25% accuracy and outperforms classical CNN anchor methods on this dataset. The authors focus on the implementation of integrated open and harness measurements. Statistical aggregation, full OOD protocols, external accuracy of AI-text benchmarks, and latency analysis are left to future work.
Copyright (c) 2026 Priyadarshan Khadtale, Dr. Rajesh Bansode (Author) https://creativecommons.org/licenses/by/4.0
Image-Based User Feature Classification on Social Media Using Machine Learning
This study presents a robust machine learning framework for image-based user feature classification on social media, with a particular emphasis on applications in e-commerce environments. To address the critical challenge of limited labeled data within large-scale and unstructured image repositories, the proposed approach integrates semi-supervised learning techniques to enhance model generalization and data utilization efficiency. Three widely adopted machine learning algorithms, Decision Tree, Random Forest, and Support Vector Machine (SVM), were systematically evaluated using K-fold cross-validation and standard performance metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Random Forest model outperforms other approaches, achieving an accuracy of 0.9468 and an F1-score of 0.9456, while maintaining strong computational efficiency and robustness. These findings underscore the effectiveness of ensemble learning methods in handling high-dimensional, imbalanced datasets. The proposed framework offers significant practical implications for user profiling, personalized recommendation systems, and targeted marketing strategies in modern digital ecosystems
Copyright (c) 2026 Nguyen Thi Hoi, Tran Thi Nhung, Dam Gia Manh (Author) https://creativecommons.org/licenses/by/4.0
AI-Based Smart Classroom System for Real-Time Student Behaviour Analysis and Automated Lecture Notes Generation
This paper presents an AI-based smart classroom system for real-time student behaviour analysis and automated lecture notes generation. The proposed system integrates Artificial Intelligence, Computer Vision, Machine Learning, and Natural Language Processing techniques to improve classroom monitoring and learning support. The system captures classroom video and lecture audio using cameras and microphones for real-time processing. Student facial expressions, eye movement, and attention levels are analyzed using deep learning algorithms to identify behaviours such as attentive, distracted, confused, and sleepy. Simultaneously, lecture audio is converted into text using speech recognition techniques and summarized automatically using NLP-based methods to generate lecture notes. The generated analytics and reports are displayed through an interactive dashboard for teachers, students, and administrators. Experimental results demonstrate that the proposed system achieves efficient classroom monitoring with approximately 95.2% behaviour analysis accuracy and low latency processing suitable for smart educational environments. The proposed framework improves classroom engagement, reduces manual monitoring effort, and enhances digital learning support.
Copyright (c) 2026 Priyanga R, Sudharsan M, Vishnu P, Preethi Varthini S, Thivyapriya N (Author) https://creativecommons.org/licenses/by/4.0
Banana Leaf Disease Prediction Using Machine Learning and Deep Learning Techniques
Banana cultivation plays a critical role in global agriculture; however, its productivity is severely affected by various foliar diseases such as Sigatoka, Panama wilt and Banana Bunchy Top Disease (BBTD). Early detection of these diseases is essential to minimize yield loss and ensure sustainable agricultural practices. Traditional diagnostic approaches rely on manual inspection, which is often subjective, time-consuming and inaccessible to farmers in remote regions. This study presents a robust and automated framework for banana leaf disease prediction using both Machine Learning (ML) and Deep Learning (DL) techniques. The proposed system employs image-based analysis, incorporating preprocessing methods such as normalization, resizing and data augmentation. Classical ML models including Support Vector Machine (SVM), k-Nearest Neighbors (KNN)and Random Forest are compared with deep learning architectures such as Convolutional Neural Networks (CNN) and transfer learning-based ResNet50. Experimental results demonstrate that deep learning models significantly outperform traditional ML approaches, achieving a maximum accuracy of 97.3% using ResNet50. The findings highlight the potential of AI-driven systems in enabling early disease detection and supporting precision agriculture.
Copyright (c) 2026 shivani Gupta (Author) https://creativecommons.org/licenses/by/4.0
An Intelligent IoT-Based Irrigation Decision System Using Sensor Fusion and Long Short-Term Memory Networks
Efficient water management is a critical challenge in modern agriculture due to increasing water scarcity and climate variability. This paper presents an intelligent IoT-based irrigation decision system that integrates multi-sensor data fusion with Long Short-Term Memory (LSTM) networks for real-time irrigation scheduling. Environmental data, including soil moisture, temperature, humidity, and solar radiation, are collected through distributed IoT sensor networks and processed using data fusion techniques to improve data reliability and completeness. The fused data is then utilized by an LSTM-based deep learning model to capture temporal dependencies and accurately predict soil moisture trends and crop water requirements. The proposed system enables dynamic and adaptive irrigation decisions by considering real-time environmental variations, thereby replacing traditional threshold-based methods. Experimental analysis demonstrates that the LSTM-based approach significantly improves prediction accuracy and water-use efficiency compared to conventional machine learning models. Furthermore, the system supports scalable deployment and remote monitoring through cloud integration, making it suitable for precision agriculture applications. Overall, the proposed framework enhances sustainable water management, optimizes irrigation scheduling, and contributes to the development of intelligent agricultural systems.
Copyright (c) 2026 Kanchan Pable, Dr. Hemang Shrivastava (Author) https://creativecommons.org/licenses/by/4.0
AI-Based Driver Health and Drowsiness Risk Prediction System Using Vision and Heart Rate Monitoring
Driver fatigue and sudden physiological health deterioration together account for nearly a third of fatal road accidents worldwide, yet most deployed monitoring solutions address only one of these two risk categories. This paper describes a real-time, multimodal driver safety system that combines camera-based Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) analysis with continuous physiological telemetry from a commercially available Bluetooth Low Energy (BLE) smart watch. A weighted soft-voting ensemble of a Random Forest classifier (weight 0.45) and a Gradient Boosting classifier (weight 0.55), trained on a 2,000-sample physiologically calibrated synthetic dataset, classifies driver state into three distinct risk levels — NORMAL, WARNING, and CRITICAL — with an overall accuracy of 94.2% and an area under the ROC curve of 0.987. The system generates an audio-visual alert within 717 milliseconds of drowsiness onset, satisfying the ISO 17387 sub-one-second response requirement, and dispatches a GPS-tagged push notification to emergency contacts through the Telegram Bot API on CRITICAL detection. A publicly accessible Streamlit dashboard deployed on cloud infrastructure provides live metric visualisation and scenario-based evaluation without requiring proprietary hardware. Total hardware cost is below INR 3,000.
Copyright (c) 2026 Paul Francis S, SURENDHAR N, Sunaina Sangeet, Priyanga R (Author) https://creativecommons.org/licenses/by/4.0
Literature Review on Fake News Detection Using Machine Learning and Deep Learning Techniques
The unprecedented proliferation of digital media and social networking platforms has amplified the spread of fake news, posing serious threats to social stability, public trust, democratic integrity, and public health. This paper presents a comprehensive literature review of fake news detection methodologies, tracing the evolution from early rule-based and traditional machine learning approaches to modern deep learning architectures and transformer-based pre-trained language models. We systematically examine foundational frameworks for defining and categorizing misinformation, the application of feature engineering techniques including Bag-of-Words and TF-IDF representations, and the progression through classical classifiers such as Naive Bayes, Support Vector Machines, and Random Forests. We then review deep learning advances including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Bidirectional LSTMs with attention mechanisms, and state-of-the-art transformer models such as BERT, RoBERTa, and ALBERT. The review further examines word embedding techniques including Word2Vec and GloVe, benchmark datasets including LIAR, ISOT, FakeNewsNet, and the Kaggle Fake News Dataset, and standard evaluation frameworks. Research gaps including interpretability, multilingual detection, adversarial robustness, and real-time deployment are synthesized and discussed. The review concludes with a comparative analysis of model performance across approaches and a structured agenda for future research.
Copyright (c) 2026 Gourav Yadav, Dr. Piyush P. Moghe (Author) https://creativecommons.org/licenses/by/4.0
A Taxonomy of DBSCAN Variants Addressing Parameter Dependence and High-Dimensionality
Density-based clustering algorithms have emerged as a significant area of research within unsupervised machine learning due to their capability to identify clusters based on data density. Among these methods, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm has gained considerable attention owing to its robustness against noise and outliers. However, DBSCAN’s sensitivity to input parameters, its inability to detect clusters of varying densities, and its poor performance in high-dimensional spaces limit its applicability. Considering these challenges, this review categorizes variants of DBSCAN into four major groups: (1) parameter-independent, (2) parameter-reduced, (3) high-dimensional, and (4) hybrid algorithms. Within each category, we provide a detailed description of parameter estimation and clustering strategies. Our analysis highlights the use of graph-based methodologies, tree-based and grid structures, and other techniques including RNN, LSH, DLT, and triangular inequality to help automate parameter estimation and accelerate neighborhood searches. Moreover, hybrid strategies incorporating feature selection, feature extraction, graph-based modeling, game theory, and deep learning have shown promising potential in addressing both challenges simultaneously. By consolidating insights across these research directions, this review offers a comprehensive perspective on the evolution of DBSCAN, equipping researchers and practitioners with a deeper understanding of its limitations, existing solutions, and avenues for future exploration.
Copyright (c) 2026 Anunaya Manoj, Dr. Masood Husain Siddiqui, Dr. Deepak Kumar Singh (Author) https://creativecommons.org/licenses/by/4.0
Digital Twin Modeling for AI-Based Personalized Drug Response Prediction and Virtual Treatment Simulation: A Literature Review
The rapid advancement of Artificial Intelligence (AI), Machine Learning (ML), and Digital Twin technologies has transformed modern healthcare systems toward precision and personalized medicine. Traditional treatment approaches often fail to account for patient-specific biological variations, resulting in inconsistent drug responses and ineffective treatment outcomes. Digital Twin modeling provides a promising solution by creating virtual replicas of patients using real-time physiological, genetic, and clinical data. These virtual models can simulate disease progression, predict drug responses, and optimize treatment strategies before actual clinical implementation. This literature review explores recent advancements in AI-based personalized drug response prediction and virtual treatment simulation using Digital Twin technology. The paper analyzes existing methodologies, machine learning algorithms, data integration techniques, and simulation frameworks used in healthcare Digital Twins. Furthermore, it identifies major research gaps including data privacy concerns, interoperability issues, computational complexity, and limited real-time adaptability. The review concludes with future research directions involving Explainable AI, Federated Learning, IoT-enabled healthcare systems, and multi-organ Digital Twin architectures for improving precision medicine and patient care.
Copyright (c) 2026 Chetanya Jain, Dr Sumit Jain (Author) https://creativecommons.org/licenses/by/4.0
Fake News Detection Using Long Short-Term Memory Networks and Natural Language Processing: A Deep Learning Approach with GloVe Word Embeddings
The exponential growth of fake news across digital media platforms poses a significant threat to public trust, democratic processes, and social stability. This paper presents the design, implementation, and experimental evaluation of an automated fake news detection system using Long Short-Term Memory (LSTM) networks combined with Natural Language Processing (NLP) pre-processing and GloVe pre-trained word embeddings. The proposed system is trained and evaluated on the Kaggle Fake News Dataset comprising 44,898 labeled English-language news articles. A comprehensive NLP pre-processing pipeline — including text cleaning, tokenization, stop-word removal, and lemmatization — transforms raw article text into structured input sequences. GloVe embeddings (100-dimensional) initialize the model's embedding layer, providing rich semantic representations with 86.4% vocabulary coverage. The LSTM-based architecture, incorporating spatial dropout and recurrent dropout regularization, achieves a test accuracy of 96.34% and a macro F1-score of 96.34%, significantly outperforming four traditional machine learning baselines: Naive Bayes (84.67%), Random Forest (89.76%), Logistic Regression (91.23%), and Support Vector Machine (93.48%). An ablation study quantifies the individual contribution of each system component. Error analysis identifies systematic misclassification patterns. The paper discusses ethical considerations, practical deployment implications, and directions for future research including transformer-based architectures, multilingual detection, and explainable AI integration.
Copyright (c) 2026 Gourav Yadav (Author); Dr. Piyush Moghe (Translator) https://creativecommons.org/licenses/by/4.0
An AI-Driven Digital Twin Framework for Personalized Drug Response Prediction and Virtual Treatment Simulation
The rapid convergence of Artificial Intelligence (AI), Machine Learning (ML), and Digital Twin technologies is reshaping modern healthcare by enabling precision, patient-specific treatment strategies. Traditional pharmacotherapy operates on population-averaged guidelines that inadequately address the genomic, metabolic, and physiological variability inherent to individual patients, resulting in inconsistent drug responses and preventable adverse events. This paper proposes a novel AI-driven Digital Twin framework that constructs continuously updated virtual patient replicas by integrating Electronic Health Records (EHRs), pharmacogenomic data, wearable Internet of Things (IoT) biosensor streams, and medical imaging. The proposed hybrid prediction engine combines XGBoost ensemble learning, Long Short-Term Memory (LSTM) deep learning, and Transformer-based multimodal fusion to predict personalized drug efficacy and adverse drug reaction (ADR) risk. A Federated Learning protocol enables privacy-preserving multi-institutional model training without centralizing sensitive patient data, while SHAP-based Explainable AI (XAI) modules ensure clinical interpretability. Experimental evaluation on the MIMIC-III clinical database and PharmGKB pharmacogenomics dataset yields a drug response prediction accuracy of 94.7%, ROC-AUC of 0.963, and F1-Score of 0.921, substantially outperforming six established baseline models. The results validate the clinical feasibility and technical superiority of the proposed framework for real-world precision medicine deployment
Copyright (c) 2026 Er.Chetanya Jain, Dr. Sumit Jain (Author) https://creativecommons.org/licenses/by/4.0