International Journal on Advanced Computer Theory and Engineering
International Journal on Advanced Computer Theory and Engineering P-ISSN: 2319-2526 Frequency: Biannual Publisher Website: https://mriindia.com/ Editor in Chief: Dr. Parikshit Mahalle Article Processing Charges: 50 USD Indexing In: Google Scholar, SCILIT, BASE, Semantic Scholar, Scribd, ResearchGate
Role of Artificial Intelligence Technology in Learning Environments for Achieving the Sustainable Development Goal of Quality Education (SDG 4) by 2030
The rising prevalence of AI technologies is impacting existing learning environments, demonstrating its potential to promote equity, access, and quality in education. This research explores ways in which AI systems, such as intelligent tutoring agents, adaptive learning systems, assessment tools, and intelligent systems, are positioning us in terms of challenging the targets of the Sustainable Development Goal 4 (SDG 4), namely, equitable and inclusive high-quality education for all by 2030. A synthesis of current research, global educational policy, and the usage of artificial intelligence in education has been used to demonstrate how intelligent technologies contribute to personalization, real-time feedback, and data-driven decision-making. The potential for intelligent technology to address educational inequalities resulting from socio-economic differences is further explored alongside the consideration of the threats inherent in AI, for example, algorithmic bias, digital divides, or inequitable data handling practices. This work positions AI within active and adaptive pedagogical frameworks to illustrate its capacity as both a means of advancing and a threat to addressing SDG 4. Our research suggests that if we wish to see sustainable changes in education by 2030, we may need more than simply technological advancements to achieve these goals. We will need a policy, cross-sector collaboration, and, really, an ethical willingness to address issues to ensure AI implementations provide inclusive, quality education for all.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nd/4.0
Software System Adaptability: A Middleware for Orchestrating Legacy Component Behavior
The increasing need for software systems to adapt dynamically to changing operational contexts has highlighted the challenge of integrating legacy components, which were often designed without adaptability in mind. This paper introduces Tekio, a lightweight middleware framework built on the OSGi specification that enables legacy software libraries to function effectively within self-adaptive systems. Tekio incorporates the MAPE-K feedback loop to support dynamic reconfiguration through parameter adjustment, component replacement, context-event response, and quality-of-service adaptation. A case study in computer vision using the OpenCV library demonstrates that Tekio achieves near-native performance while enabling frequent runtime adaptations. Empirical evaluation shows minimal overhead in throughput, CPU utilization, and memory consumption, with settling times in the millisecond range for low-to-medium complexity adaptations. The results indicate that Tekio provides a viable pathway for extending the operational lifespan of legacy software in modern adaptive environments, balancing flexibility with performance efficiency.
A Smart Barcode Identifier & Recovery System for Lost Individuals in Mass Gatherings
Mass public events such as pilgrimages, festivals, and large community gatherings frequently lead to individuals—especially children and elderly people—becoming separated from their families. Conventional recovery methods are often slow, inefficient, and emotionally distressing. To overcome these challenges, this paper proposes an AI-driven Smart Barcode-Based Identification and Recovery System that integrates machine learning, computer vision, and facial recognition for real-time and automated identification. Each participant is provided with a barcode or QR code–embedded wristband linked to a secure digital profile containing personal details, emergency contacts, and medical information. Authorized personnel can scan the codes using a mobile application enhanced with AI-based identity verification and deep learning techniques. Machine learning pipelines improve identification accuracy through pattern recognition, anomaly detection, and predictive matching, thereby minimizing human error. The proposed intelligent recovery framework ensures fast, secure, and scalable operations by integrating cloud computing, data analytics, and automated decision-making. Predictive AI models and automated alert mechanisms significantly enhance response time, improve crowd management, strengthen emergency coordination, and provide a reliable, data-driven safety infrastructure for mass public events.
A Deep Learning Approach for Detecting Pesticide Residues on Brinjal
Pesticide residue classification is an essential task that involves finding and classifying pesticide levels built on hyperspectral image data. This process is extensively used in the field of agricultural quality review and food security monitoring. The proposed system covers three key phases, preparation of data, feature extraction, and classification. A modified deep learning neural network architecture, HSI-SpaClassNet, is used to categorize pesticide remains on Brinjal. To attain precise classification, the proposed system uses spatial features extracted from hyperspectral images as inputs to the model. Proposed approach provides better performance as compared to existing models such as MobileNet, EfficientNet, and DenseNet on the Brinjal dataset. Proposed model gives accuracy 98.58% while others deep learning models gives accuracy 97.25% for MobileNet, 96.15% for EfficientNetB0 and 91.86% for DenseNet Model. This paper presents a detailed comparison between the proposed architecture and existing algorithms highlighting the model’s effectiveness for rapid and precise pesticide residue detection.
Predictive Analytics Framework Using Machine Learning for Personalized Nutrition and Lifestyle Recommendations: A Technical Approach toward Women’s Wellness
The use of artificial intelligence, machine learning and predictive analytics in nutrition and lifestyle studies has expanded rapidly. However, many of the studies that are currently available are not sufficiently addressing women-specific health issues. The purpose of this study is to examine and compile the most current studies on AI and machine learning-based methods for evaluating women's nutrition and lifestyle. A comprehensive review of published research was carried out, with particular focus on studies that used machine learning methods for nutrition and health analysis, including Random Forest, Decision Tree, Support Vector Machine, K- Nearest Neighbours, and Naive Bayes. Less than 20% of the examined 30 peer-reviewed publications address individualized lifestyle or nutrition guidance systems, specifically for women, whereas more than 70% concentrate on obesity prediction or nutrient deficiency detection. These findings suggest a significant research gap in integrated predictive analytics frameworks that are women-centric. In order to create more individualized and data-driven nutrition and lifestyle solutions for women's health, this review offers recommendations for future research on AI- and ML-based blended framework represents a meaningful step toward personalized wellness recommendations. Tailoring dietary and lifestyle guidance specifically to women is a promising direction that aligns with modern trends in precision health.
Learning To Learn: A Survey of Recent Literature on Meta Learning
The essence of Meta-learning is “learning to learn”. Meta Learning is a subset of machine learning. Meta-learning is the process of using knowledge gained from many tasks during meta-training to enable a model to quickly learn new tasks from few examples. Meta learning algorithm, or the learning method itself, such that the modified learner is better than the original learner at learning from additional experience. This paper explore introduction of Meta learning, how it works , the structure of literature survey of Meta learning, Meta Learning for few shot learning in specialized domains, evaluation metrics and benchmark datasets for meta learning and future direction and open problems in meta learning for few shot learning.
Traditional to Modern Tools: A Comparative Study of Visual Lip Feature Extraction with Dlib
Visual Lip refers to the visible movements and contour of the lips during speech. It shows how the mouth moves when pronouncing sounds without any audio. This study report describes the various visual lip feature extraction methods and tools with the steps involved in each tool. Overall Relationship between different lip extraction Models and tools also discussed in study report. The current study reports the status of research literature chronologically, explaining various tools for lip feature extraction mainly focusing set up dependency for Dlib tool. The current study also explains various lip feature extraction tools including dlib, Mediapipe and OpenCV with conducting a comparative study, with its description, speed, accuracy, advantages, disadvantages and output with each tool. Set up dependency with each package and version for Dlib tool as it has version compatibility errors also taken into consideration in this report. Paper reports 10 common Visual Features that extracted from Dlib with its calculation. Our findings show that lip extraction has a variety of uses in everyday life. Various Challenges for feature extraction while using Dlib tool are also discussed in the paper. Until now, research on visual lip extraction using each tool has been conducted independently. Based on our findings, we determined that the comparative and in-depth dependency and setup for Dlib tool. Therefore this work is unique and published for the first time.
Artificial Intelligence Based Argumentation Framework for Critical Thinking Development
In academic, professional, and civic contexts, the development of critical thinking and reasoning skills are essential. Despite the success, traditional debate-based education methods have drawbacks such limited scalability, subjective evaluation, and teacher availability. The development of AI-powered argumentation training systems that provide adaptive feedback, replicate various debate scenarios, and thoroughly evaluate student progress has been made possible by recent advancements in artificial intelligence (AI). In the present works, methods, effectiveness, and limitations of the current research on AI-enabled systems are examined. The application software examines problems with system design, evaluation, and accessibility while highlighting current trends and pointing out weaknesses in existing methods. The application software will help to impact future research directions and result in the development of more efficient, scalable, and customized critical thinking tools.
Gesture-Based Recognition of ISL to Inclusive Communication in Marathi Text
Hand gestures are the media of individuals with total hearing loss but not useful to connect with ordinary person. The hand gesture language recognition becomes most potential area of research. In the present research work the MSL recognition system is developed with help of mediaPipe for real time recognition. It is not possible every time to get the interpreter for translation and understanding of Marathi sign language for common people. In the previous research work many researchers had undertakes the machine learning for classification and extraction of feature in the light of development of said system. They have faced several challenges in these research areas. The present research work will overcome the challenges of these research areas. As a result, it is found that by using direct image data and by using Euclidean distance function the ANN model shows the higher accuracy (approx. 95. %) than logistics regression, decision tree and SGD Model.
Enhanced Secure Digital Data Embedding and Extracting Using Advanced Crypto-Stego Mechanisms
In this proposed study, a new hybrid approach is employed, which ensures the safe embedding as well as retrieval of digital data by using modern cryptography techniques along with steganographic techniques. By combining NTRU encryption, which provides quantum-resistant security, with the Arnold Transform, which improves data scrambling, the method significantly increases the stability of encrypted data. The transmission and storage of data are enhanced using Generative Adversarial Networks (GAN)-based compression without carrier file integrity being compromised. Efficient embedding and robust resistance to steganalysis attacks are promised by the Convolutional Neural Networks (CNN)-based data hiding method.
Optimized Big Data Storage and Security in Cloud Computing using Advanced Encryption Techniques
Cloud security and effective cloud distribution across the network are the primary issues with cloud computing, but data security is a critical challenge for contemporary society. Advanced encryption techniques are used to secure sensitive data and guarantee privacy in a distributed setting significantly enhancing storage and big data security in cloud computing. Therefore, the input data is collected from the Big Data Derby Dataset. Then, lossless compression is applied using the Burrows-Wheeler Transform (BWT) model to minimize redundancy and enhance cloud-based data accessibility. The lossless compressed data is fed into an Enhanced Rivest-Shamir-Adleman Algorithm (ERSA) to improve data encryption and decryption performance as an optimized Modified Binary Banyan Tree Optimization (BBTO) algorithm is utilized to minimize the time involved for encryption and decryption processes. The proposed Big Data storage and security in cloud computing using advanced encryption techniques (BDSS-CC-AET) technique is implemented on the Python platform, and its performance is expected to outperform existing methods with an estimated high throughput (78mbps), high packet delivery ratio (93%) and low encryption time (381ms). Overall, the proposed BDSS-CC-AET is expected to demonstrate superior effectiveness in big data storage and security in cloud computing.
A comprehensive analysis of Generative Artificial Intelligence's (GenAI) underlying perks and drawbacks in commercial and academic settings
With the potential to revolutionize a variety of industries across the globe, generative artificial intelligence (AI) has emerged as a game-changing technology. This study examines both the advantages and disadvantages of generative AI, focusing especially on India's defences against these charges. This paper investigates how generative AI affects creativity, robotization, invention, and ethical considerations in the Indian context through a thorough assessment of existing research and case study analysis. The results highlight both opportunities and difficulties related to India's abandonment of generative AI, providing insight for experimenters, legislators, and diligence leaders. The possibility of using generative AI to improve literacy and tutoring is also examined in this research, along with the challenges that must be addressed for successful implementation.
Computer Science Department, ATSS College of Business Studies and Computer Application, Chinchwad, Pune-19
Preservation of cultural material, especially historical palm-leaf manuscripts in Kannada, is necessary for understanding the historical, literary and theological transformation of South India. Categorizing these texts and at-tributing their authorship using traditional methods is a time consuming, difficult task. Proposed work is an attempt to apply deep learning technology for author classification of the Kannada Handwritten palm leaf manuscripts. To our knowledge, in this paper we propose for the first time a Convolutional Neural Network ensemble specific to historical Kannada palm leaf manuscript classification, which is a novel approach in heritage document analysis. The study also considers VGG16, DenseNet, AlexNet and multilayer perceptron (MLP) methods. An approach that combines them is considered as the optimal choice of approach because it provides better accuracy and reliability. This proposed methodology of recognizing Kannada palm leaf writings and identifying the author had shown to have a great impact when applied with Deep learning followed by ensemble learning mechanism. We compare these four different methods in terms of accuracy and loss. It is to be noted that AlexNet method achieves an impressive accuracy of 1.00 and a negligible loss of 0.000007, whereas the multilayered method presents approximately lower accuracy of 0.98 with higher value for the loss which are preserved over various epochs in the proposed approach. VGG16 and DenseNet techniques achieve 0.99 accura-cy, but the estimates for their loss are different. We achieve an ensemble accuracy of 1.00 and a minimum loss of 0.000006. The experiment results show that the deep learning models are effective in extracting palm leaf texts. The ensemble methods have reported satisfactory performance. The proposed approach increases automation and categorization of cultural artifacts.
A Literature Review on Spy Software Applications
This paper presents the analysis of the literature review on the spyware tools which are invading privacy concerns and challenging the forensic investigation procedures. These tools are increasingly used in the illegal activities and due to the lack of standardized methodology it leads to the loss of data, violation of data privacy, inappropriate results and challenging courtroom procedures. This research focuses on the study of spyware tools including software and applications that are used for the covert surveillance. This paper provides the comprehensive review of the existing techniques summarizing their positive and negative outcomes. The literature review studied that there is lot of development in the detection, manufacturing and technical analysis of the spyware tools. It provides the future point of research by the detailed analysis of the existing techniques and methodology. The purpose of this research is to provide the broader view of development in this domain and highlighting the legal and ethical considerations.
A Comparative Study of Sentiment Analysis Techniques for Hindi Text: Machine Learning vs Deep Learning
The increase in interest in sentiment analysis (SA) is presently supported by the demand for extracting information from texts, as seen in social media discussions and individual reviews. Languages like Hindi, however, are not easy to analyze for sentiment analysis because the morphology of these languages is complex and has its own syntax. This paper proposes a comparison of two popular sentiment analysis approaches for Hindi text: traditional machine learning (ML) approaches and more advanced deep learning (DL) approaches. We compare other ML algorithms, including Naive Bayes, Support Vector Machines (SVM), and Logistic Regression, with DL models such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and a specific application of an Artificial Neural Network for automating the measurement of kinematic characteristics of punches in boxing. Applied Sciences. Transformer-based BERT. With a dataset of sentiment analysis of the Hindi language, the research intends to determine the strengths and weaknesses of each of the methods. Our findings reveal that although deep learning algorithms, particularly BERT, are more accurate and possess a stronger contextualized understanding, the machine learning algorithm is computationally efficient. Such results have a significant implication regarding undertaking language sentiment analysis in languages whose morphology is rich, e.g., Hindi.
GrapeScan - Grape Leaf Disease Detection
Crop yield and quality of grapes are affected adversely by grapevine diseases. Manual inspection of grapes for health and diseases is inefficient due to high volume and potentially high error rates. The authors developed a system called GrapeScan using deep learning algorithms to classify photos of grape leaves into diseased or healthy categories. The system is based on a Convolutional Neural Network (CNN) trained on a publicly available dataset of grape leaves and is available for use in real-time via a web interface. The authors report results indicating that their system has high classification accuracy and low inference time, making it appropriate for use in precision agriculture and other practical farming applications.
Detection of Citrus Plant Leaf Detection Using Non – Imaging Data
Plant leaf diseases pose a significant challenge to agricultural productivity, necessitating efficient and accurate detection methods. This study presents an integrated approach combining deep learning (CNN) and machine learning (SVM, Random Forest, KNN, Logistic Regression) for plant disease classification using imaging and spectral data. The proposed system processes leaf images to detect unhealthy regions, extracting statistical features such as contrast (10.59), entropy (4.31), and mean intensity (137.24) to assess disease severity. CNN-based models demonstrated strong training accuracy but exhibited overfitting in validation performance. Among machine learning models, SVM and Logistic Regression achieved the highest accuracy (70%), while Random Forest performed moderately (54%), and KNN struggled (39%) due to high-dimensional spectral complexities. Confusion matrices revealed that Healthy and Greening categories often overlapped, leading to misclassifications. The findings suggest that a hybrid deep learning + machine learning approach enhances classification accuracy by leveraging both image-based and spectral features. Future improvements involve ensemble learning, better feature engineering, and real-time field deployment for automated disease detection. This research provides a scalable and effective solution for precision agriculture, enabling early disease diagnosis and improved crop health monitoring.
Review on Abnormalities Detection from Medical Video Endoscopy using Deep learning Approaches
Endoscopy serves as a vital tool for identification and evaluation of GI diseases, with its capabilities further strengthened by inclusion of deep learning techniques. Early detection of such a GI disease such as polyp, lesion, and ulcer is very important for prevention serious complication like cancer crucial for effective treatment. . The application of artificial intelligence (AI), most notably deep learning, into medical video analytics has evolved into an innovative approach to this challenge. Systematic literature review (SLR) examines 42 studies to provide a systematically overview of detection abnormalities from endoscopic video using deep learning. It include CNN successfully use for detect, classification, segmentation on endoscopic images. Review systematically examines the powerful impact of deep learning on endoscopic videos, highlighting its current strengths and limitations. Future research directions are investigated, which special strategies outlined to tackle current challenges and enable the inclusion of deep learning into clinical workflow. This eventually aims to advance medical imaging technologies, resulting in one precise, individualized and optimized healthcare for patients.
Next-Generation Library Information Systems: Evaluating Native Multi-Model Database Technology
Multi-model databases handles the structured, semi-structured, and highly connected data of various applications such as Healthcare, Library Information System and many more. This research paper put focus on two multi-model databases, one is native multi-model database, ArangoDB, and a Hybrid multi-model database integrating PostgreSQL, Couch DB, and Neo4j. The objective of this paper is to measure the performance of these multi-model using various evaluation criteria as such as execution time, throughput, indexing efficiency, and latency. Also, further it highlights on query designing and data retrieval efficiency showing better approach for library management environment. A real-world college library system which handle big data workloads was chosen to evaluate the performance of these multi-models. The results shows that hybrid multi-model can be adapted in cases where a stronger transactional reliability is required. In contrast, ArangoDB, performs more efficiently in cross-model queries, especially a single AQL query unified document, graph, and relational data retrieval, minimizing query orchestration and communication overhead. ArangoDB performs 47% better in execution time and 42% in throughput than Hybrid Model. Natural Language Processing (NLP) was used for query translation that enabled users to submit queries in plain English, which automatically transformed into structured database commands, improving accessibility and user experience. This research will help developers and researchers to design a better multi-model which is efficient for providing faster and more organized academic resources to students and faculties.
Automated Detection and Classification of Parkinson's Disease Using Electroencephalography: A Review
In this review paper we have discuss in depth knowledge of Parkinson's disease (PD) is a progressive neurodegenerative condition with a variety of motor and non-motor symptoms, early and precise diagnosis is essential for successful treatment. A prospective route for the automated identification of neurological disorders like Parkinson's disease (PD) is electroencephalography (EEG), a non-invasive and economical technique for examining brain activity. The state-of-the-art in automated PD detection and classification using EEG data is examined in this study, with a focus on developments in deep learning (DL) and machine learning (ML) techniques for improved diagnostic precision. In particular, it looks at a variety of signal processing approaches, feature extraction strategies, and how well different classification algorithms distinguish PD patients from healthy controls. The potential of EEG-based biomarkers for monitoring the course of a disease and the effectiveness of treatment is also covered in the article, opening the door to tailored therapeutic interventions. By combining current research to identify areas for innovation in automated EEG analysis, the main objective is to present a thorough overview of current obstacles and future prospects in utilising EEG for early and reliable PD diagnosis and monitoring.
Comparative Study of Deep Learning Methods for Thyroid Cancer Detection
Deep learning (DL) has emerged as a powerful tool for improving the accuracy and consistency of thyroid cancer detection from ultrasound images. While numerous DL-based models have been proposed, their clinical applicability, generalizability, and interpretability vary significantly. This paper presents a comparative review of five influential and peer-reviewed deep learning paradigms for thyroid cancer detection: Swin-Attention Segmentation, Weakly Supervised Segmentation, Vision Foundation Models, the diffusion-based Tiger Model, and the human-interpretable TiNet framework. These models represent diverse methodological directions, including attention-driven segmentation, annotation-efficient learning, foundation model adaptation, generative data augmentation, and explainable diagnostic reporting. The review critically analyzes their architectural design, dataset usage, performance metrics, interpretability, and deployment readiness. Key research gaps are identified, including limited multi-center generalization, insufficient handling of rare thyroid cancer subtypes, inconsistent clinical benchmarking, and challenges in real-world deployment. By emphasizing clinical relevance alongside technical performance, this review aims to guide future research toward developing robust, interpretable, and clinically integrable AI systems for thyroid cancer diagnosis.
From Text to Toxicity: Exploring the Challenges in Marathi Hate Speech Detection
Hate speech on social media has become a serious problem in society. There is a increasing need to create systems that can automatically detect such hateful content. Marathi, an Indo-Aryan language widely spoken in India, remains under-represented in natural language processing research due to limited linguistic resources and annotated datasets. This study focuses on the major challenges faced in detecting hate speech in the Marathi language. Marathi is a low-resource and complex language. It does not have enough large and balanced datasets for proper model training. The presence of code-mixing, transliteration between Devanagari and Roman scripts, and various dialects increases the complexity of text processing. Ambiguity in meaning, sarcasm, and context-dependent expressions make automatic detection more difficult. This paper systematically reviews the challenges in Marathi hate speech detection arising from data scarcity, code-mixing, transliteration, dialectal variation, annotation ambiguity, and context-dependent expressions.
Automatic Tag Generation (ATG) Using Machine Learning Tech-niques for Women Violence Detection
This research addresses the challenge of automated detection and classification of multiple types of violence from image data using deep learning techniques. Given the societal importance of timely and accurate violence recognition, this study explores both custom Convolutional Neural Network (CNN) architectures and state-of-the-art transfer learning models pre-trained on ImageNet, including VGG16 and InceptionV3. The dataset comprises images across five violence categories—brutality, domestic violence, human trafficking, rape, and sexual harassment—collected and augmented to enhance model generalizability. Methods involved image preprocessing, data augmentation, and training models with categorical cross-entropy loss optimized via Adam. Transfer learning approaches outperformed the custom CNN, The models demonstrated varying degrees of success in classifying violence image categories. Transfer learning models, particularly VGG16 and InceptionV3, outperformed the custom CNN, achieving overall accuracy improvements from approximately 75% to 76%. These results confirm the effectiveness of leveraging pre-trained networks for complex image classification tasks with limited datasets. Class-wise analysis through confusion matrices and derived metrics such as precision, recall, and F1-score demonstrated varied detection performance, highlighting difficulties in differentiating visually similar classes. The results affirm that leveraging pre-trained deep architectures substantially benefits the classification of limited, complex image datasets. This paper contributes by providing a comprehensive evaluation of deep learning approaches for violence classification in images, motivating their use in practical monitoring and intervention applications. Future work is suggested to integrate temporal data and attention mechanisms to further enhance detection performance. The findings underscore the feasibility and importance of automated violence recognition systems for social safety.
Image Processing-Based Automation of Blastocoel Expansion Grading for Improved IVF Embryo Evaluation
To increase implantation success rates, in vitro fertilization (IVF) depends on precise evaluation of blastocyst quality, with blastocoel growth, one of the blastocyst's characteristics. Blastocoel expansion being a critical evaluation parameter in IVF. In order to objectively measure blastocoel expansion from human blastocyst, this work presents an automated image analysis method. Preprocessing the image to improve quality, segmenting the blastocyst and blastocoel sections using image processing techniques, estimating the area of each region, and computing the ratio of blastocoel to blastocyst area are all phases in the proposed method. Expansion grades are assigned using these quantitative measurements, which aid in the selection of embryos during IVF operations. According to experimental data, the results offer objective, and consistent evaluation standards while lowering the subjective variability present in manual assessments. The automation of blastocoel assessment by the system provides reliable information to embryologists, enabling them to make more informed clinical decisions and perhaps increasing the success rate of embryo implantation during IVF treatments.
Text Summarization for Marathi Text documents using TextRank Algorithm
In today’s digital world, the massive growth of information has created an urgent need for automatic tools that can condense lengthy documents into concise and meaningful summaries. Text summarization has emerged as an important research area to address this challenge, helping readers quickly understand the essence of large texts. While much progress has been made in English and other global languages, Indian languages such as Marathi still lack sufficient resources and research in this field. Marathi, being one of the most widely spoken languages in India, is used extensively in government communication, especially in legal and administrative documents. These legal documents are often lengthy, complex, and filled with formal language, making it difficult for readers to extract key information efficiently. To address this issue, we apply the TextRank algorithm, a graph-based ranking method, for automatic summarization of Marathi legal documents. The proposed system selects the most important sentences to create a concise extractive summary that preserves the original meaning. Experimental results show that this approach significantly reduces the length of the document while retaining critical information, thereby improving the accessibility and usability of legal texts.
Lightweight Hybrid Deep Learning and Machine Learning Framework for Multiclass Alzheimer’s Detection Using PCA-Compressed CNN Features
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, and its early identification is critical for timely clinical intervention. Deep learning models achieve strong performance in medical image analysis but often require high computational resources, limiting deployment in low-resource environments. This study proposes a lightweight hybrid framework that integrates deep feature extraction using EfficientNetB0 with traditional machine learning classifiers for multiclass Alzheimer’s disease classification. MRI images from the Alzheimer’s Multiclass Equal and Augmented Dataset were processed using an EfficientNetB0 feature extractor, and the resulting high-dimensional feature vectors were compressed using Principal Component Analysis (PCA) to reduce memory footprint and training time. Four machine learning classifiers—Random Forest, Linear SVM, Logistic Regression, and XGBoost—were trained and evaluated on the compressed features. Experimental results show that the hybrid approach achieves competitive performance, with XGBoost obtaining the highest accuracy of 75%, followed by Linear SVM at 74%. The results demonstrate that PCA-compressed deep features combined with classical ML models provide an efficient and scalable solution for multiclass Alzheimer’s diagnosis while maintaining low computational complexity. This framework is particularly suitable for real-world clinical applications where computational resources are limited.
POS Tagging: A Review of Recent Techniques
Part-of-Speech (POS) tagging is a fundamental task in Natural Language Pro-cessing (NLP) that involves assigning grammatical categories such as noun, verb, adjective, and adverb to words in a text. Accurate POS tagging serves as a critical preprocessing step for higher-level NLP applications, including syntactic parsing, machine translation, information retrieval, and sentiment analysis. Over the past decades, a wide range of POS tagging techniques has been proposed, by different research scholars from rule-based systems to da-ta-driven and neural approaches. This review provides a systematic examination of recent techniques, highlighting their core methodologies, strengths, limitations, and applicability to different languages.
Feature Selection in Stock Market Prediction: A Comprehensive Review
The current world is referred to as the "data world," as, according to Google, 328.77 million terabytes of data are generated every day and are continually increasing. One of the cause contributing to the data growth is the stock market. Thus, it is now necessary to reduce data by removing unnecessary data and extracting only the data that is important. The feature selection procedure is crucial for focusing on key data and reducing the dimensionality of the data. As per my knowledge there aren't many published articles that review the feature extraction and selection techniques utilized in stock market prediction at this time. The same motive will be covered in this paper, where we will analyze feature extraction and selection techniques utilized in the stock market. It includes embedded, filter, wrapper, supervised, unsupervised, semi-supervised, and hybrid methods.
Leveraging NLP and LLMs for Intelligent Mock Interview Systems
This paper presents the design and implementation of an LLM and NLP-powered Mock Interview Application that provides realistic, AI-driven interview simulations. The system integrates Large Language Models (LLMs) for dynamic question generation, contextual follow-ups, and personalized feedback, while Natural Language Processing (NLP) enables semantic understanding, sentiment analysis, and communication skill evaluation. Text-to-Speech (TTS) models facilitate voice-based interaction, and Machine Learning (ML) algorithms assess user responses based on content relevance, tone, and confidence. The modular architecture encompasses user authentication, interview setup, question generation, speech analysis, and performance visualization. Evaluation metrics include response accuracy, fluency, and emotional tone derived through NLP and LLM-based analysis. Future enhancements target multimodal emotion recognition, eye-contact detection, and multilingual support, positioning the platform as an advanced AI-driven career readiness and communication training assistant.
Recent Advances in Object Detection, Segmentation, Integration and Relationship Detection with Special Reference to Enhanced Scene Understanding
Understanding visual scenes comprehensively remains a central challenge in Computer vision and artificial intelligence. The field has witnessed tremendous evolution from traditional feature-based methods to deep learning architectures capable of simultaneous object detection, precise segmentation and complex relationship modeling. This review synthesizes recent developments across these interconnected domains, with particular emphasis on the YOLO family evolution through YOLOv10, transformer-based detection frameworks including DETR and its variants, advanced segmentation models such as SAM and HQ-SAMand scene graph generation techniques. We examine how multi-task learning and multimodal integration strategies are reshaping scene understanding capabilities. Critical analysis of current limitations—including Computational efficiency, domain generalization, data imbalance and interpretability—guides our discussion of emerging research directions. Foundation models, efficient transformers and zero-shot learning represent promising avenues for advancing robust, scalable scene understanding systems applicable to autonomous vehicles, medical imaging, robotics and intelligent surveillance.
Automated Prediction of Heart Disease based on EMR data using Deep Learning Algorithm: A Review and Evaluation Study for various Feature Extraction Techniques
For many years, healthcare providers in the United States have relied on the ACC/AHA Pooled Cohort Equations (PCE) Risk Calculator as their main resource for estimating a person’s likelihood of developing atherosclerotic cardiovascular disease, a leading form of heart-related illness. Despite being widely used, the calculator occasionally overestimates or underestimates risk but doesn't always perform equally across different groups. To overcome these challenges, we developed an automated ASCVD risk-prediction model tailored to particular patient groups, using machine-learning techniques applied to real-world electronic medical record data. We then compared its performance with the PCE approach. In our study, we reviewed 101,110 electronic medical records from patients seen between January 1, 2009, and April 30, 2020. The machine-learning models were trained using either cross-sectional clinical data alone or a combination of cross-sectional information and longitudinal patterns drawn from lab results and vital-sign measurements. To understand how each model identified true cases, we introduced a cost-focused metric called the “Screened Cases Percentage at a given Sensitivity,” which shows how many patients would require follow-up testing to detect most ASCVD cases. In every analysis we conducted, the machine-learning models outperformed the PCE calculator. The strongest results came from a random forest model that used both cross-sectional and longitudinal data, achieving an AUC of 0.902 (95% CI: 0.895–0.910). To identify 90% of ASCVD cases, this approach required screening only 43% of patients, compared with 69% when relying on the PCE. Overall, combining CS and LT data led to accurate predictions and fewer unnecessary screenings.
Advances in Geospatial Technology for Early Detection of Mango Leaf Diseases
Mango leaves are susceptible to pests and pathogens including anthracnose, powdery mildew, diebacks, bacterial infection which cause serious loss of mango production, quality and sustainability. Recent developments in geospatial technology have provided new opportunities for the early, accurate, and large-scale detection of disease. This article offers a comprehensive review of methods, datasets and tools for monitoring mango leaf diseases through remote sensing and geoscience. High-resolution UAV images, multispectral and hyperspectral satellite data (e.g. Sentinel-3) along with field level spectral information serve as the basic sources of data for disease detection. There is a need for preprocessing steps such as resizing, denoising and normalization to enhance the reliability of models. The feature extraction with the help of vegetation indices, Band Ratios and texture metric GLCM increases the discrimination capacity between healthy and infected leaf. SVM, Random Forest, CNNs, and hybrid models of machine learning and deep learning have demonstrated high performance in classification when trained on a well-processed dataset. A key advance presented in this review is the development of portable spectrometers, which provide very accurate leaf-level reflectance profiles and become the crucial ground truth for calibrating geospatial disease detection models. The combined use of spectrometer data with UAV and satellite imagery significantly enhances disease identification sensitivity, especially at early stages of disease infection when the visual symptoms are minimal. In general, this review confirms that combining geospatial technologies, spectroscopy, and smart classification frameworks will provide an efficient, accurate, and scalable solution for mango disease monitoring to support precision horticulture and sustainable orchard management.
AI for Good: Societal Impact and Public Policy
Artificial Intelligence (AI) has emerged as a transformative force that influences economic systems, institutional functions, and daily human behaviors. Beyond technological efficiency, AI carries the potential to strengthen societal welfare, democratize access to public resources, and promote inclusive governance. This paper investigates the societal applications of AI across domains such as healthcare, education, accessibility, environmental management, emergency response, and civic administration. It further explores risk frameworks, ethical constraints, and policy imperatives, arguing that public governance is pivotal to ensuring equitable and accountable AI implementation. The study concludes that the future of AI must be guided by human-centered ethical principles, international cooperation, and strategic regulatory planning to ensure societal benefit and minimize systemic risks.
Measuring the Predictive Power of Data Mining Techniques in Forecasting Social Media Addiction and Excessive Usage Trends
The exponential rise of social media usage has redefined communication, connectivity, and lifestyle across the globe, particularly in developing nations such as India. However, this digital transformation has also led to an alarming surge in social media addiction (SMA)—a behavioral condition characterized by compulsive engagement, mood modification, and diminished self-regulation. This study presents an integrated predictive framework that combines psychometric assessment with data mining and machine learning algorithms to forecast SMA and excessive usage trends among Indian users. A cross-sectional dataset comprising 750 respondents aged 18–35 years from five metropolitan cities (Mumbai, Delhi, Bengaluru, Hyderabad, and Pune) was analyzed using two validated instruments: the Bergen Social Media Addiction Scale (BSMAS) and the Scale of Excessive Use of Social Networking Sites (SEUS-14). Data were processed using Random Forest (RF), Gradient Boosting Classifier (GBC), and Support Vector Machine (SVM) models to predict addiction risk levels. The Random Forest model achieved the highest performance with an accuracy of 92.6% and ROC–AUC of 0.94, confirming its robustness and generalization capability. Psychometric analysis revealed moderate addiction and overuse levels, with mood modification, tolerance, and compulsive checking emerging as the most dominant behavioral features. A strong positive correlation (r = 0.74, p < 0.001) between BSMAS and SEUS-14 scores demonstrated concurrent validity, confirming that psychological dependency and behavioral excess represent converging constructs of digital addiction. The results substantiate the biopsychosocial model of behavioral addiction, showing that SMA arises from the interplay of emotional regulation needs, social validation cycles, and habit reinforcement. By integrating machine learning and behavioral science, this research contributes to the emerging field of computational psychology, offering a data-driven mechanism for early detection, digital wellness intervention, and policy formulation. The study underscores the potential of AI-assisted predictive analytics as a scalable and ethical solution for monitoring digital well-being within the Indian socio-technological context.
A Guide to Safe Vehicle Ad Hoc Networks: Perspectives and Analogies
Concerningly, traffic accidents have been on the rise globally, resulting in significant human and material losses. This highlights the critical need for creative and effective solutions to address this issue. A new wireless communication technology called Vehicular Ad Hoc Networks (VANETs) has recently gained a lot of attention from researchers because of its potential to improve traffic management and road safety. Nevertheless, in spite of these advantages, VANETs have considerable security concerns. Their essentially open and dynamic traits make them especially susceptible to numerous harmful threats, endangering the network's reliability and the users' safety. Establishing robust security mechanisms is essential for developing resilient and reliable VANET systems that can effectively mitigate threats and safeguard network integrity. An overview of vehicle ad hoc networks (VANETs), together including its design and salient features, is given in this study. It explores the primary security issues VANETs face and underlines the essential need of a safe network environment. The study investigates important security concerns and how they impact VANET integrity in order to underline the shortcomings threatening this integrity. Viewing these hazards, this work looks at and assesses some possible countermeasures. Organizing several security solutions into groups and carefully evaluating them helps one to determine which ones are most appropriate for particular security issues.
AI-Driven Inclusive Assessment Tool for Education
Traditional exam systems usually depend on fixed question sets and manual grading. Such methods fail to address differences in students’ skills or accessibility needs. In this work, an AI-based assessment platform is presented that focuses on flexibility and inclusivity. The system uses Natural Language Processing (NLP) techniques to automate the evaluation of descriptive answers. It applies adaptive logic to pick multiple-choice questions (MCQs) that match a learner’s level of understanding. Handwritten responses are checked with Optical Character Recognition (OCR) to keep the marking process accurate and fair. For better accessibility, Speech-to-Text (STT) and Text-to-Speech (TTS) tools are added, helping students who face visual or motor challenges take the test comfortably. The overall system follows a modular architecture, connecting React.js for the user interface, Node.js for backend operations, and Python-based AI modules for question generation and scoring. This paper focuses on the system design, methodology, and prototype-level feasibility analysis of an inclusive AI-driven assessment framework.
Model Construction of English Mixed Teaching and English Mobile Learning Combined with Adaptive Presentation Strategy based on ANFIS
In this study, we introduce a novel educational paradigm that integrates English mixed teaching and mobile learning within the framework of an Adaptive Neuro-Fuzzy Inference System (ANFIS). Focusing on the challenges and opportunities presented by modern language education, the study proposes a model that combines traditional classroom teaching with mobile learning platforms, emphasizing adaptability through ANFIS-guided instructional strategies. The model dynamically tailors content delivery based on individual learner needs, adapting to varying proficiency levels, learning styles, and preferences. The synergy of mixed teaching, mobile learning, and ANFIS-driven adaptation aims to create a comprehensive and personalized English language learning experience. Through empirical validation and analysis, this research aims to contribute insights into the efficacy of the proposed model, offering implications for the future of language education that balances traditional methodologies with contemporary technological advancements.
AI-Augmented ERP Systems as Catalysts for HR Digital Transformation: A Framework for Future-Ready Workforce Competency Study in Maharashtra State
This study examines the application of AI-advanced ERP systems in Maharashtra, India, and how this is utilized to digitize human resources (HR) and act as a stimulant to the competence of workers. However, with the introduction of artificial intelligence into the ERPs, these ERP systems open new prospects of intelligent decision-making, prediction analytics, and automation within the HR functions as the businesses transform towards Industry 5.0. The quantitative methodology of the study involved forty respondents who had varied sectors of organizations implementing the ERP systems, and this included HR managers and ERP professionals, as well as senior executives of an organization. Statistical analysis confirming the correlation between the variables, namely AI-ERP integration, HR-digital process maturity, and workforce competence, with multiple regression, reliability, and correlation is applied. The two principal findings are the model fit of R 2 = 0.767 that is significant with significant predictors of AI integration and ERP integration ( 0.238), digital HR process maturity ( 0.332), and human-AI synergy ( 0.304).The findings indicate that AI-ERP integration can significantly enhance the process of HR digital transformation and that this connection is mediated by the human-AI synergy, which enhances the impact on the competence of the workforce. The results indicate that HR departments can become less administrative than in the past and instead become strategic, data-oriented collaborators contributing to the agility and digital readiness of employees with the help of AI-driven ERP systems. The level of competence of the workers in case an AI-ERP solution is adopted by the organization can be increased by about 23–33%, based on the spheres of processes in the digital human resources and the severity of the human-AI cooperation. To achieve sustainable workforce competence, the study provides a regional framework that would relate the technological infrastructure, HR digitalization, human-AI relationship, and ecosystem readiness. It further offers practitioners and policymakers feasible concepts in how to bind the technology and human capital policies so as to develop a workforce that is ready to grapple with the future in a fast-living digital economy.
Intelligent Personalized Health Intervention System Using Deep Learning and Blockchain Technology for Chronic Disease
Chronic diseases such as diabetes, cardiovascular disorders, and kidney diseases continue to be among the most serious global health concerns, accounting for high morbidity and mortality rates worldwide [21], [25]. This research introduces an Intelligent Personalized Health Intervention System that leverages Deep Learning and Blockchain Technology to enable secure, predictive, and data-driven healthcare management. The system employs deep learning models for early detection of chronic disease risks [2], [14], while blockchain ensures tamper-proof and decentralized storage of sensitive health data [1], [10]. In addition, Optical Character Recognition (OCR) and Natural Language Processing (NLP) are used to automatically extract and analyze information from medical documents, thereby reducing manual intervention and improving diagnostic accuracy [9], [17]. The proposed framework integrates patient health records, predictive analytics, and personalized recommendations within a secure web-based environment, aiming to enhance patient engagement, ensure data privacy, and support timely medical decision-making [12], [24].
Smart Medical Health Prediction Application Using Data Mining Integrated With Deep Learning for Cataract Detection
Cataract is a major cause of avoidable vision loss, and early screening is essential to prevent blindness. This work proposes a smart medical health prediction system that combines data mining with deep learning to automatically detect cataract from ocular images. Preprocessing techniques are used to clean and enhance images, while a convolutional neural network classifies eyes as normal or cataract-affected. The system is designed as an assistive tool to provide fast, low-cost, and reliable screening support, especially in resource-limited settings. Experimental results show good accuracy and sensitivity, demonstrating that the integrated approach improves early detection and supports clinical decision- making in ophthalmology.
Who Loses to Automation? AI-Driven Labour Displacement and the Limits of Reskilling Policies in Platform-Based Informal Work in India
Rapid advances in artificial intelligence (AI) and automation have led to serious concerns about labour displacement and job insecurity, yet their effects vary widely across different labour market contexts. This paper examines how AI-driven automation shapes employment outcomes in platform-based informal work in India, with a focus on labour displacement and limits of reskilling policies. Using a qualitative, secondary literature-based analysis which examines the distinction between job displacement and task displacement, the study examines how algorithmic management restructures work in platform-based employment. The paper argues that while AI primarily operates at the task level, the lack of job security, institutional protection and access to alternative labour tracks means that task-level displacement manifests as complete job displacement in the informal sector especially in platform-based informal work. Furthermore, it shows that prevailing reskilling policies are not suitable for this context as they assume access to stable employment, training opportunities and institutional support that the informal sector generally lacks. The findings highlight the importance of labour market structure in shaping the employment effects of automation and suggest that policy responses must move beyond reskilling to address informality and job quality in developing economies. On the basis of the mentioned findings, this paper proposes potential policy implications which focuses on the importance of a multifaceted approach to reskilling and job protection.