International Journal on Advanced Electrical and Computer Engineering
International Journal on Advanced Electrical and Computer Engineering E-ISSN: 2349-9338 P-ISSN: 2349-932X 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
Title generation is a specialized area of natural language processing (NLP), and most studies in this area focus on English, while other languages, such as Arabic, are still in their infancy. Recent research has focused on developing title generation systems for Arabic texts, which can be challenging given the nature of the Arabic language and of datasets. Using artificial intelligence (AI), titles were created and information from lengthy texts was made easier to obtain. The titles and content of each title from verified websites were compiled into a dataset. The title-generating work was started using the Arabic T5 transformer-based title generation model. Following that, the final model was assessed using assessment techniques such as BERTScore, ROUGE, BLEU, and F-Score. On the BERTScore scale, the suggested model performed well, earning the highest evaluation score of 87, lso the model was evaluated by four independent, expert evaluators, with the arithmetic mean of the evaluations being 7.25/10, indicating good acceptance of the results. In order to improve the model's performance, the researchers propose applying human evaluation, changing the model's structure, and growing the dataset.
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Multi Group classification based on Arabic
Hierarchical text classification represents a significant and critical challenge in the field of Arabic natural language processing. This challenge is further complicated by the language's morphological richness and the scarcity of large-scale, structured datasets. This paper presents a comprehensive comparative study of two modern approaches to fine-tuning this task: a generative text-to-text approach using the AraT5 model, and a direct classification approach using the AraGPT-2 model. These models were evaluated on a large, specially collected dataset comprising over 75,000 articles distributed across 600 subcategories, as well as a smaller benchmark dataset compared to the previous literature. Experimental results demonstrated that the generative AraT5 model achieved superior performance and hierarchical consistency on this large and complex dataset. Furthermore, our improved AraGPT-2 model, enhanced with advanced regularization techniques, significantly outperformed the current literature benchmark on the compared dataset, achieving a hierarchical F1 score of 93.54%. The results indicate that, while both approaches are effective, the generative approach demonstrates a clear advantage in dealing with a fuzzy classification space. This work establishes new performance benchmarks and provides critical insights into the impact of fine-tuning strategies and data complexity on the hierarchical classification of Arabic.
Evaluation of Artificial Intelligence in Answering Dermatological Medical Questions
This research presents a systematic evaluation of the adaptation of a large Arabic linguistic model (based on AraGPT2) to answer questions in the field of dermatology. The study aims to bridge the gap in specialized linguistic resources by fine-tuning the model to a newly created and purified dataset, collected using a hybrid methodology combining web scraping and filtered data enrichment. This dataset consists of 40,132 specialized question-answer pairs. The performance of the finely tuned model was quantitatively assessed using BERTScore, BLEU, Levenshtein distance, and two types of initial human evaluation. The quantitative results showed strong semantic performance, with the model achieving a BERTScore (F1) of 64.49%, confirming its ability to effectively understand medical context and meaning. In contrast, the verbal matching measures reflected a tendency toward free generation, scoring BLEU at 10.00% and Levenshtein distance at 28.13%. These results demonstrate that the model favors the free generation of new formulations over the verbatim retrieval of reference texts. Furthermore, the qualitative results of the proposed model showed a competitive overall performance of 4.01, achieving a high score of 4.55 in the criteria of linguistic clarity and readability for non-expert audiences. These results confirm that the model's primary contribution lies in its ability to enhance human comprehension (understanding) of complex medical data. The study also underscores the urgent need for subsequent clinical validation by field experts to ensure complete clinical accuracy and reliability.
MAAD: A Multi-Label Arabic Dataset for Transformer-Based News Summarization and Classification
This paper presents a Multi-Label Arabic Articles Dataset (MAAD), a sizable corpus of 602,792 news articles from six prominent Arabic media outlets covering ten subject areas, is presented in this work. The MAAD underwent extensive pre-processing noise filtering and duplicate elimination using hashing with cosine similarity, linguistic normalization, and topic validation through LDA modeling and expert review, achieving 95% categorization accuracy in order to address the lack of high-quality Arabic datasets for deep learning. The multi-label structure of MAAD allows for the simultaneous execution of several NLP tasks, in contrast to conventional single-task corpora. Four transformer models: ArabicT5, AraBART, mT5, and GPT were refined utilizing a single text-to-text architecture for both classification and abstractive summarization in order to evaluate its efficacy. Four standard metrics were used in the review process: F1-score for classification, ROUGE-1, ROUGE-2, ROUGE-L, and BLEU for summarization, accuracy, precision, and recall. Results demonstrated that ArabicT5 outperformed all comparative models, achieving ROUGE-1 = 0.90, ROUGE-2 = 0.90, ROUGE-L = 0.90, BLEU = 0.81, and classification accuracy = 0.98 with consistent scores (Precision, Recall, F1 ≥ 0.95). Furthermore, the model's efficacy in generating coherent and semantically accurate Arabic text was validated by a human examination of the generated summaries, which produced high ratings for Fluency (4.86) and Adequacy (4.35). These results demonstrate that language-specific pretraining greatly enhances model performance on the intricate morphology and syntax of Arabic. Consequently, MAAD serves as a robust foundation and a practical instrument for advancing the field of Arabic Natural Language Processing (NLP). Its utility extends significantly to enhancing the precision of automated journalism, as well as optimizing the workflows involved in news processing and content aggregation.
Semantic Similarity for Zero-Shot Hate Speech Detection in Low-Resource Languages
This study investigates the role of semantic similarity in improving zero-shot and cross-lingual hate speech detection across several low-resource and typologically diverse languages, including Arabic, Hebrew, Persian, Russian, Chinese, Korean, and Amharic. A novel framework is proposed that clusters languages based on semantic and genealogical similarity using multilingual sentence embeddings derived from the XLM-R model. Each cluster was used to fine-tune the multilingual mDeBERTa-v3-base-mnli-xnli model, which was then evaluated in zero-shot settings on unseen languages within and across clusters. The results show that zero-shot transfer is highly influenced by linguistic proximity: models trained on Semitic languages (Arabic–Hebrew–Amharic) achieved strong zero-shot performance with F1 scores between 0.80 and 0.86 on unseen languages, while the Indo-European cluster (Russian–Persian) yielded competitive results (F1 ≈ 0.71–0.80). Training on typologically distant East Asian languages (Chinese–Korean) also demonstrated effective zero-shot generalization (F1 ≈ 0.75). Moreover, incorporating a newly developed Yemeni Arabic hate speech dataset enhanced Arabic performance and improved zero-shot transfer to related languages. These findings highlight the significance of semantic similarity in facilitating cross-lingual generalization and offer a scalable strategy for multilingual hate speech detection in low-resource settings.
Educational Video Time-Stamped based on Texttiling with Sentence-BERT Embeddings
Automatically segmenting long educational videos into coherent thematic units is essential for improving content navigation, enabling targeted review, and supporting downstream applications such as interactive video indexing and summarization. In this work, we propose an unsupervised, transcript-based segmentation method that enhances the classical TextTiling algorithm by incorporating contextual Sentence-BERT embeddings to detect topic transitions as drops in semantic coherence between consecutive segments, without requiring labeled training data. To evaluate our approach, we introduce EVTS (Educational Video Timestamp Segmentation), a large-scale dataset of 1,553 real-world educational YouTube videos, each annotated with creator-provided timestamps marking the start of distinct instructional segments. Experimental results show that our method aligns well with these human-defined boundaries, achieving an F1 score of 0.665, a precision of 0.735, and a Pk of 0.365 under an adaptive configuration. These findings indicate that the proposed approach effectively captures meaningful topic shifts and produces accurate, time-stamped segments suitable for real-world educational applications, even when evaluated against noisy, non-expert annotations.
Arabic Summarization Bases on Encoder-Decoder Model
A study conducted by several scholars discusses the abstraction of an Arabic text model through deep learning and natural language processing by developing an abstract model for summarizing Arabic text. The training of the T5 architecture is customized for the Arabic text assignment. A model with a well-summarized 89.4% accurate summary is derived and tested from diverse Arabic sources. Similar data was fed into the models for efficiency comparison. To summarize, the studies come to one point that the combination of innovative converter technologies and deep coding is a promising way for generating intelligent solutions for Arabic content summarization. This implementation can assist with tackling the immense amount of information precisely and fast.
Comparing the Performance of Specialized BERT Models in Classify-ing Arabic News: AraBERT, CAMeLBERT, and mBERT
This study evaluates the comparative performance of advanced Transformer-based models for classifying Arabic news articles into five categories: arts, events and issues, economics, politics, and sports. It further investigates the impact of linguistic specialization on classification accuracy. We utilized a dataset of 173,117 Arabic news articles, which was processed to address class imbalance through under sampling, resulting in a balanced set of 68,677 articles. Three models were trained and evaluated under uniformly tuned hyperparameters for a fair comparison: AraBERT (specialized in Modern Standard Arabic), CAMeLBERT (trained on a mix of MSA and dialects), and mBERT (a multilingual model) as a baseline. The results demonstrate that AraBERT achieved the highest accuracy of 96.04% on the test set, outperforming both CAMeLBERT (94.18%) and mBERT (94.14%). This outcome confirms the "specialization hypothesis," indicating that models specifically pre-trained on formal Arabic yield superior performance on news classification tasks. Furthermore, all Transformer models significantly surpassed traditional methods like CNNs and classical machine learning algorithms (e.g., SVM, Naive Bayes) by a margin of at least ~8.5%. The study concludes that AraBERT is presently the optimal model for this domain and recommends building comprehensive Arabic news datasets, exploring ensemble and hybrid modeling techniques, and expanding research into multi-label classification.
Enhancing Arabic Web Article Classification through Label Noise Reduction and Class-Balanced Training
We present an applied, data-centric study on improving large-scale Arabic web-article classification by detecting and correcting label noise in a 451K-article corpus. Building on a practical AraBERTv02 fine-tuned baseline, we develop an automated noise-detection and relabelling pipeline that combines model confidence, MC-dropout ensemble agreement, and probabilistic flagging inspired by Confident Learning. Using stratified reduced experiments (Train=50k, Val=5k, Test=5k) we automatically relabelled 880 training examples and retrained the classifier. On the held-out 5k test set the cleaned model improved Macro-F1 from 0.9367 (baseline) to 0.9503, a statistically significant gain confirmed by McNemar’s test (p ≈ 1.9e-5) and bootstrap 95% CI for Δ Macro-F1 = [0.0082, 0.0190]. We analyse per-class gains, error modes, and cost trade-offs of automated relabelling versus manual review, and release anonymized preprocessing scripts and experiment artifacts to support reproducibility. Our results show that conservative automatic relabelling — judiciously combined with uncertainty estimation — can yield meaningful, reproducible improvements for large-scale Arabic article classification.
Optimizing Economic Resilience: A Unified Framework for Cost-Sensitive Fraud Decisioning in Imbalanced Financial Systems
Traditional fraud detection systems, which rely solely on statistical performance metrics such as Accuracy and F1-score, are fundamentally inadequate for addressing the economic complexities inherent in the banking sector. The costs associated with misclassification are significantly imbalanced: False Negatives (FN) lead to immediate financial losses, whereas False Positives (FP) impose considerable operational burdens and result in customer attrition, often surpassing the minor losses incurred from FN. This study introduces an innovative and comprehensive Cost-Driven Decision Framework that prioritizes minimizing overall economic loss over maximizing predictive accuracy. Our methodology incorporates monetary cost modeling, tiered risk penalty assignment for detailed control, cost-optimized threshold selection (θ*), and a secondary suppression layer for high-cost alerts. Empirical validation using the PaySim dataset demonstrated that the proposed cost-aware system achieved a notable 34.7% reduction in total financial loss compared to conventional accuracy-driven models. The framework establishes a robust and economically sound approach to enhance financial resilience in dynamic payment ecosystems.
Adaptive Fuzzy Logic–Based Diagnostic Model for Early Detection of Pancreatic Cancer
Pancreatic cancer remains one of the most lethal malignancies due to its silent progression and the lack of reliable methods for early detection. Conventional diagnostic techniques, including CA 19-9 biomarker analysis and imaging modalities such as CT and MRI, often fail to identify early-stage tumors because of limited sensitivity, specificity, and their inability to manage imprecise clinical presentations. This study proposes an Adaptive Fuzzy Logic-Based Diagnostic Model designed to improve early pancreatic cancer detection by integrating heterogeneous data sources, including clinical symptoms, biochemical markers, genetic profiles, and imaging findings. The model employs an adaptive fuzzy inference system capable of interpreting uncertain and ambiguous medical data, enabling a refined risk stratification process. Using a dataset constructed from hypothetical but clinically consistent patient records, the model was trained and validated through optimized fuzzy rule sets and membership functions. Performance evaluation demonstrated significantly enhanced diagnostic accuracy, achieving 92% sensitivity and 89% specificity, outperforming conventional approaches that averaged 75% and 70%, respectively. The model also achieved an AUC of 0.94, indicating superior discriminative capability in distinguishing high-risk from low-risk cases. These results highlight the model’s potential as a clinical decision-support tool capable of improving early diagnosis, reducing misclassification, and supporting timely interventions. The study underscores the value of integrating fuzzy logic with multi-modal medical data and provides a framework for future AI-driven diagnostic advancements in oncology.
Deep-Fake Image Detection Using Machine Learning Techniques: A Comprehensive Review
The rapid development of deep generative models like Generative Adversarial Networks (GANs) and Diffusion Models has made visual synthetic images such as deep-fake very realistic, representing a possible threat to digital trust, privacy and national security. This survey offers an overview of machine learning (ML) based methods for deep-fake image detection, including classical ML classifiers, convolution neural network (CNN) architectures attention mechanisms, multimodal systems, and hybrid feature fusion models. The paper analyses commonly-used datasets, performance evaluation criteria and the most recent state-of-arts on benchmarks such as Celeb-DF, FaceForensics++ and DeepFake Detection Challenge (DFDC). A comparison of the two approaches would discuss the advantages and drawbacks as well as generalisability issues with ML to unknown manipulations. Finally, the review points to important open challenges such as cross-dataset generalization, explainability analysis, adversarial susceptibility and multimodel deep-fake attacks-and draws future research directions to make trustful detection deep-fake systems.
Bias in AI Systems and Its Impact on Deprived Groups
Artificial Intelligence (AI) is now used to make decisions in many areas such as jobs, healthcare, education, banking, policymaking, and government welfare schemes. Although people often believe AI is neutral and objective, these systems can actually repeat and worsen the social inequalities persistently prevailing in society. This paper explains how AI bias harms deprived and marginalised groups such as Dalits, Adivasis, religious minorities, women, people with disabilities. AI becomes biased when the data used to train it is incomplete, unequal, or influenced by stereotypes. Problems also arise when algorithms are designed in ways that favour dominant social groups. This leads to real-world consequences; hiring software may reject applicants based on caste-related names or rural backgrounds; predictive policing systems may unfairly target communities that are already vulnerable. These issues increase social exclusion by limiting access to essential opportunities and services. The paper trying to argues that reducing AI bias is not only a technical challenge but also a social responsibility. It calls for more inclusive datasets, transparent algorithms, regular bias checks, involvement of affected communities in AI design, and strong regulations. Ensuring fairness and accountability in AI is necessary to prevent digital discrimination and protect the constitutional promise of equality for all as well as establish constitutionalism.
A Comprehensive Survey on Human Depression Detection and Classification Using Machine learning
Depression is a prevalent mental health disorder affecting millions globally, with serious consequences for emotional, cognitive, and physical well-being. Recent advances in machine learning (ML) and deep learning (DL) have enabled automated and objective approaches to detect and classify depression across multiple modalities, including facial expressions, speech, EEG signals, and textual data. This paper presents a comprehensive review of recent ML-based depression detection techniques, focusing on model architectures such as CNNs, LSTMs, hybrid models, EfficientNet, Vision Transformers, VGG16, and ResNet50. Comparative analysis of publicly available datasets, including DAIC-WOZ, MODMA, and RAVDESS, reveals that multimodal approaches consistently achieve higher accuracy (up to 99%) than single-modality methods. The study identifies critical gaps, such as the need for real-time detection, explainable AI, lightweight models, and more diverse datasets, and highlights future directions to improve the scalability, robustness, and interpretability of ML-based depression detection systems.
A Study on an Integrated NER and Coreference Resolution Framework for Travel and Tourism Social Media Texts: Performance Comparison Using Advanced NLP Models
The study presents an integrated framework combining Named Entity Recognition (NER) and coreference resolution tailored for travel and tourism social media texts. Social media content is often noisy, informal, and multilingual, posing significant challenges for traditional NER methods. Leveraging advanced transformer based NLP models, including BERT and its variants, the framework addresses these challenges by improving entity extraction and contextual linking of references. This paper shows data collection, preprocessing techniques and model architectures adapted for the travel and tourism context. Evaluation on a manually curated social media dataset demonstrates notable improvements in entity recognition accuracy and coreference resolution consistency. The results highlight the potential of this approach for enhancing tourism analytics, trend monitoring, and recommendation systems. This work contributes to the growing body of research on applying deep learning techniques to domain specific social media text analysis in the tourism sector.
Comprehensive Study on the Role of IoT in Intelligent Home Automation Systems
The Internet of Things (IoT) most recently stood as an essential pillar of intelligent home automation systems with respects to technology through connectivity, real-time monitoring, and self- governing decisions through a network of connected smart devices. The paper presented here offers a thorough summary of the state of the art and architecture and applications of IoT in smart homes while paying special attention to how sensors, actuators, cloud platforms, and artificial intelligence combine to create an elevated level of increased residential comfort, security, and energy performance. We also elaborate on significant technology frameworks and applications, and highlight important challenges such as interoperability, data privacy, and cyber security. We discuss the emerging trends and best practices, and future considerations based on our findings based on analysis.
Intelligent Intrusion Detection Systems Using Machine Learning
Intrusion Detection System (IDS) watches network traffic for fraudulent activity and gives immediate alerts when it is observed. By notifying security administrators of known or possible threats or by sending alerts to a centralized security tool, an intrusion detection system (IDS) can assist speed up and automate network threat detection. This paper presents a comprehensive overview of Intrusion Detection Systems (IDS) and the machine learning techniques commonly employed to enhance their detection capabilities. Various IDS approaches are examined, with a focus on both signature-based and anomaly-based models. The study highlights key machine learning methods—such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Random Forests, and clustering algorithms—and evaluates their suitability for identifying malicious activities in network environments. The growing integration of AI in cybersecurity is also discussed, emphasizing its role in improving automated threat analysis and adaptive intrusion detection Additionally, the KDD-99 dataset is utilized to outline experimental procedures and demonstrate how these algorithms can be applied in practical IDS implementations. The findings emphasize the importance of selecting appropriate learning techniques to improve accuracy, reduce false positives, and strengthen overall network security.
The Role of Artificial Intelligence, Machine Learning, and Deep Learning in Liver Transplantation
Liver transplantation is a complex, resource-intensive procedure with critical challenges in donor- recipient matching, post-operative care, and long-term outcome prediction. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) into liver transplantation workflows promises to enhance clinical decision-making, optimize patient outcomes, and streamline resource allocation. This paper reviews recent advancements in AI, ML, and DL applications in liver transplantation, including donor organ evaluation, recipient selection, surgical risk prediction, and post-transplant monitoring. By summarizing key findings and ongoing research, we provide a roadmap for future innovations in this life-saving field.
A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms for Stress Detection
Stress detection has become an increasingly important area of research, with applications spanning healthcare, occupational safety, and mental health. Machine learning (ML) algorithms have shown considerable potential in identifying stress from various data sources such as physiological signals, voice recordings, and even text. This review paper explores and compares the two primary categories of machine learning algorithms supervised and unsupervised for stress detection. We systematically review existing studies, discuss their methodologies, and analyze the strengths and weaknesses of each approach. The goal is to provide insights into the most effective methods for automatic stress detection and highlight the challenges and opportunities for future research.
NeuroVibe – Neural Vibration Pathway for Deaf Users
People with hearing impairments often feel like they cannot sense the sound from their surroundings. NeuroVibe is an innovative wearable device that provides an alternative way of perceiving sound through bone conduction-based vibrotactile feedback; it transforms the sound frequencies into gentle mechanical vibrations that can easily be perceived via teeth. The device consists of a Bluetooth audio module (Cosmic 104), a mini audio amplifier (PAM8403), a cylindrical vibration motor, a 3.7 V/1800 mAh rechargeable lithium battery, connecting wires, and a protective enclosure. When connected with an audio source, NeuroVibe converts sound frequencies into proportional intensities of vibration that reach the cochlea through the jawbone, enabling the user to feel rather than hear the sound sensation. This work presents the design of the device, its working mechanism, and its vibration response for alphabetic sound patterns in the 3–7 kHz, frequency range. The results clearly indicate a relationship between frequency and vibration strength, thus pointing out NeuroVibe’s potential for being an affordable and effective assistive technology in people with partial or profound hearing loss.
Fruit Classification based on Color and Shape Features in Real Time Video Sequences
Fruit classification plays a crucial role in agricultural automation, including fruit harvesting robots, quality control, and crop monitoring. This paper presents a real time computer vision system for classifying fruits in outdoor environemnts using continuous video sequences. It focuses on extracting color and shape features to overcome challenges like varying lighting and motion. In this paper, fruit features were extracted from each frame and utilized machine learning classifiers to classify fruits. Each frame is processed using computer vision techniques to segment fruits and obtain relevant features, which are then classified using a Support Vector Machine (SVM) classifier. The results showed that by combining both color and shape features along with machine learning algorithms for classifying fruits in real time enhances recognition accuracy in an outdoor environments. The proposed system achieved overall classification accuracy of 91.5%. The future work will focus on improving accuracy in case of occlusion and green fruits.
Optimization Algorithms for Brain MRI-Based Alzheimer’s Disease Classification: A Comprehensive Review and Methodological Framework
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive functions. Magnetic Resonance Imaging (MRI) functions as a non-invasive diagnostic method which scientists use to identify Alzheimer's disease at its first stage. Deep learning models including Convolutional Neural Networks (CNNs), and Transformer-based architectures have emerged as the leading technology for AD classification during the last several years. The performance of neural networks depends on the correct selection and adjustment of hyperparameters and weights, and network structure design. The paper provides a complete analysis of traditional and nature-inspired and contemporary meta-heuristic optimization methods which serve AD classification purposes. We propose a methodological framework which combines deep learning with advanced optimizers including AdamW, Lookahead, Bayesian Optimization, Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and recent hybrid strategies. The paper presents a summary of optimization-based AD classification methods which identifies their current limitations and future research directions.
AI-Based Motor Insurance Risk Assessment
The rapid evolution of intelligent transportation systems has created a growing demand for data-driven motor insurance models that evaluate risk more accurately. Traditional methods rely heavily on static factors such as driver profiles, vehicle categories, and historical claims, which fail to represent real-time driving behavior. This paper presents an AI-Based Motor Insurance Risk Assessment System leveraging gyroscope and accelerometer data to analyze driver motion and predict accident likelihood. The system captures orientation and movement changes from onboard sensors to identify harsh acceleration, sudden braking, and sharp turns—critical indicators of unsafe driving. Machine learning algorithms process this data to classify drivers based on risk level and support fair, data-driven premium estimation. Experimental results demonstrate improved precision in identifying highrisk drivers and reducing manual assessment errors. The proposed framework provides a scalable, intelligent approach for building safer and more transparent insurance ecosystems.
Comparison of Machine Learning Models for Hand Sign Recognition
Hand sign recognition plays a crucial role in the development of assistive technologies for human–computer interaction and communication support for persons with hearing and speech impairments. This paper presents a comparative study of machine learning models for static hand sign recognition using a publicly available Indian Sign Language (ISL) dataset downloaded from Kaggle. A unified preprocessing pipeline was applied to all images, followed by extraction of three complementary feature sets: Hu Moments for capturing global shape properties, contour-based shape features for structural hand geometry, and Histogram of Oriented Gradients (HOG) for local gradient-based texture information. These features were combined into a single feature vector and used to train two major classifier families: K-Nearest Neighbors (KNN) with different values of k, and Support Vector Machines (SVM) with multiple kernels, including Linear, Polynomial, and Radial Basis Function (RBF). The performance of each model was evaluated using accuracy, precision, recall, and F1-score to identify the most effective classifier for multi-class hand sign recognition. Experimental results show that SVM with the RBF kernel consistently achieves superior classification accuracy compared to KNN and other SVM variants, demonstrating its suitability for high-dimensional feature representations in hand gesture recognition systems. The findings provide useful insights for selecting efficient machine learning models for real-time sign recognition applications.
A Semantic Embedding Approach for Peer-to-Peer Skill Exchange Networks
Acquiring new skills has been a issue for many students which we attribute to high costs associated with educators and also to few opportunities for practical teaching. In a traditional model which is lecture based we see little to no interaction from the student, which in turn produces a very passive learning environment. Also we see inactivity, a lack of interaction and low motivation from this approach. To fix this we put forth the idea of a peer to peer skill exchange system. In order to maintain participants long-term engagement, elements of gamification such as leaderboards, rankings, and rewards are included. The platform also encourages users to gain personal attributes such as confidence, effective communication, and collaborative participation in addition to technical skills development. Overall, the methodology leads to enhanced interactivity in the learning process and moves away from the traditional paradigms of learning, providing empowerment in the development of skills more than a personal gain.
AI Driven Context-Aware DDoS Detection and Mitigation Framework Using Optimized CNN–BiLSTM and Reinforcement Learning
The exponential growth of interconnected systems across the Internet of Things (IoT), Software-Defined Networks (SDN), and cloud environments has led to a drastic increase in the scale and advancement of Distributed Denial of Service (DDoS) attacks. Conventional defences based on machine learning are often static, traffic-centric, and lack adaptivity to dynamic network behaviour, resulting in high false-positive rates and delay in mitigation responses. To address these challenges, this paper presents a conceptual framework for an AI-driven, context-aware DDoS detection and mitigation system. The proposed approach employs a optimized Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) model for efficient, real-time detection of DDoS attacks. The model captures spatial and temporal correlations in traffic while incorporating contextual parameters such as Quality of Service (QoS), Service Level Agreement (SLA) priority, and workload telemetry. These contextual attributes enable the model to distinguish legitimate network fluctuations from malicious activities, so that the false positive rate will be reduced without compromising accuracy. For adaptive response, a Reinforcement Learning (RL)-based mitigation layer—exploiting Deep Q-Network (DQN) and Actor–Critic (AC) variants—continuously learns optimal mitigation actions based on real-time feedback. The interaction between detection and mitigation components forms a context-aware feedback loop, enabling dynamic policy refinement and continuous system improvement. Comprehensive literature analysis reveals that existing AI-based DDoS defense mechanisms lack integration between detection and mitigation, and often overlook contextual intelligence. The proposed framework bridges this gap by introducing a self-adaptive, feedback-driven defense system capable of maintaining detection precision, low latency, and QoS preservation in heterogeneous environments.
Cognition-Inspired Computational Methods for Automated Chikoo Plant Disease Diagnosis: A Review of Image Processing and Deep Learning
The problem of precision in early diagnosis of Chikoo (Manilkara achras) plant diseases remains a serious lapse in digital plant pathology, although significant advances in the computation diagnosis of other horticultural and field crops have been made. This review includes the development of automated plant disease detection- classically image processing or machine-learning pipelines up to deep learning structures, transfer learning procedures, as well as emergent cognition-based motivated and explainable systems. The survey is an integration of the evidence of image acquisition studies, feature extraction approaches by human hands, SVM/KNN/RF-based classifiers, CNNs and transformer model, attention, severity-aware networks, and explainable AI, including Grad-CAM, SHAP, and LIME. Whereas similar crops (like tomato, apple, peach, rice, and arecanut) were proven, Chikoo does not have any recorded computational dataset or diagnostic device. Through the awareness of multidisciplinary knowledge, i.e., farmer decision-making behaviour, mobile-based diagnostic services, and geolocation-linked high-throughput, this review presents a Chikoo-friendly research agenda in the future. The paper identifies the necessity of curated Chikoo datasets, transfer-learning baselines, attention-based models, explainability as well as deployable smartphone applications that combine human-like perception and inference. Altogether, this review places cognition-motivated explainable deep learning as a perspective of developing reliable, transparent, and usable by a farmer, Chikoo disease diagnostic frameworks.
A Thorough Literature Review on Automatic Speaker Diarization Employing Machine Learning and Deep Learning Methodologies
Automatic Speaker Diarization (ASD) is the process of dividing an audio recording into regions where each speaker is the same and figuring out "who spoke when" with-out knowing who the speakers are ahead of time. It is a necessary part of meeting transcription, conversational analytics, indexing for broadcast media, forensic audio processing, call-center monitoring, and modern systems for human-computer interac-tion. In the past twenty years, diarization research has moved from traditional statis-tical models like Gaussian Mixture Models (GMMs) based on MFCCs and Bayesian Information Criterion (BIC) segmentation to more advanced representation learning methods like i-vectors and Probabilistic Linear Discriminant Analysis (PLDA). Later advances in deep learning led to strong neural embeddings like x-vectors and ECAPA-TDNN, which made it much easier to identify speakers in difficult sound situations. The most current Self-Supervised Learning (SSL) models, such as Wav2Vec 2.0, HuBERT, and WavLM, have set new standards by learning strong speech representations without any labeled input. End-to-End Neural Diarization (EEND), UIS-RNN, and VB-HMM re-segmentation are some of the complementary methods that have improved how well we can handle overlaps and refine time. This evaluation offers a thorough examination of recent advancements, evaluat-ing the advantages and disadvantages of prominent diarization methodologies, pin-pointing enduring research deficiencies, and delineating prospective avenues for the enhancement of precise, multilingual, and real-time speaker diarization systems.
Handwritten Character Recognition from Ancient Inscriptions: A Comprehensive Literature Review
This paper provides a comprehensive review of the literature on handwritten character recognition, especially for Dhammalipi(Brahmi script) written on ancient inscriptions. There is a necessity to modernize old images and documents. The ancient times of stone inscriptions reveal the culture and assist in preserving and highlighting our nation's heritage. Handwritten Character Recognition (HCR) involves identifying and translating handwritten characters into digital text.HCR is a topic with extensive research, but there is still an opportunity for improvement in terms of accuracy and efficiency. Digitizing handwritten text is extremely important in today's world since it makes information available at any time and from any location. Digitized text is safer and environmentally friendlier than manual text, making it suitable for commercial use. This document examines multiple approaches and methods for obtaining, organizing, and deriving information from historical inscriptions in diverse languages. This literature will help many researchers to get the current and past position of work done on handwritten character recognition.
Two-Stage Machine Learning Pipeline for Fetal Head Analysis
Background: The accurate measurement of fetal head circumference (HC) and the precise estimation of gestational age are vital components of prenatal assessment. However, manual techniques are often labor-intensive and subject to variability among different observers. Automated methods have the potential to enhance measurement consistency and reduce the clinical workload. Purpose: This study presents an integrated two-stage machine learning pipeline aimed at automating fetal head segmentation and trimester-specific gestational age classification using ultrasound data. Methods: The proposed framework is composed of two stages. In the first stage, fetal head segmentation is conducted using a Random Forest classifier that leverages handcrafted pixel-level features. In the second stage, features related to shape, intensity, and texture are extracted from the segmented region and classified using a top-3 ensemble of LightGBM, Support Vector Machine with a Radial Basis Function (RBF) kernel, and XGBoost, with adjustments for class imbalance and feature optimization. Results: The segmentation framework achieved a mean accuracy of 89.02%. The trimester classification model achieved an overall accuracy of 85.62%, a precision of 86.57%, a recall of 85.62%, and an F1-score of 85.59%. Conclusion: The proposed two-stage pipeline effectively achieves precise segmentation of the fetal head and accurate trimester classification, highlighting its considerable potential for integration into clinical decision-support systems, especially in healthcare environments with constrained resources.
The Study of Current IoT Techniques Used in Plant Disease Detection
Plant diseases significantly affect global agricultural productivity and food security. Conventional disease detection techniques rely heavily on manual inspection, which is labor-intensive, time-consuming, and prone to subjectivity. The emergence of the Internet of Things (IoT) has enabled real-time monitoring of plant health and automated disease detection using interconnected sensors, imaging devices, and intelligent data processing frameworks. This paper presents a systematic review of current IoT techniques used in plant disease detection, focusing on sensor technologies, communication protocols, data processing platforms, and machine learning integration. A structured review methodology is adopted to analyze recent literature, and comparative tables are provided to highlight the strengths and limitations of existing systems. The study demonstrates that integrating IoT with artificial intelligence significantly improves detection accuracy, response time, and sustainability in precision agriculture.
Kurukh to Hindi Translation: A Review of Methods, Challenges and Future Directions
Many languages ​​have become extinct in the world today; the extinction of languages ​​is an unfortunate challenge, as the knowledge and culture embedded in those languages ​​also disappear. It is impossible for any human being to know all languages. The development of machine translation is a solution to overcome this limitation. Machine translation is a field of artificial intelligence and natural language processing that facilitates communication by translating from one language to another using machine translation systems. After translation, the quality of the translated output can be measured using evaluation strategies to determine whether it is of human translation quality, thus measuring its accuracy. This paper is a review of the work done on evaluating Kurukh a tribal and low-resource language machine translation challenges and future directions.
A Comprehensive Review of Deep Convolutional Neural Networks for Brain Tumor Detection and Classification
Brain tumours are a major health burden worldwide, and the early and accurate detection of them is vital to improve treatment radiation doses and overall patient survival. The development of deep learning, in particular CNN, has drastically changed medical image analysis by automating the process of feature extraction and diagnosis. In high-quality medical images, MICs can also cause erroneous detections. In this review paper, we comprehensively investigate the deep learning technologies for brain tumour detection and classification on MR and CT images in the state of the art. This paper demonstrates that the CNN model has superiority over traditional manual diagnosis, effectively learns the hierarchical features of images, and reduces misdiagnosed cases. It addresses essential model designs, benchmarks, and issues related to integrating these models into clinical workflows. Furthermore, the survey investigates the ability of CNN-based systems to provide robust and efficient telemedicine-based and high-quality neuro-oncological diagnostics utilizing high-dimensional imaging whilst accurately segmenting tumorous regions. The inclusion of AI in medical imaging drives not only an increase in workflow efficiency but also the prospect of personalized treatment planning. This paper highlights the breakthrough applications of CNNs in neuroimaging and future research directions, which include further enhancing the model generalization on various datasets and real-world clinical applications that can help radiologists achieve better patient care.
AI-Driven Cyber Defense: Enhancing Data Security and Securing Human and Non-Human Identities Against Modern Cyber Attacks
The rise of sophisticated cyber threats has driven the need for defense mechanisms to evolve beyond traditional rule-based systems. This study presents a comprehensive analysis of artificial intelligence-driven cyber defense systems, focusing on their application to enhance data security and protect human and non-human identities. We examine the integration of machine learning algorithms, deep learning architectures, and behavioral analytics to create adaptive defense mechanisms that can detect and mitigate advanced, persistent threats, zero-day exploits, and identity-based attacks. This research explores various AI techniques, including supervised and unsupervised learning, neural networks, and anomaly detection systems, demonstrating their effectiveness in real-time threat identification and response. Furthermore, we address the unique challenges of securing nonhuman identities, such as IoT devices, service accounts, and API keys, which have become critical attack vectors in modern cyber infrastructure. Our analysis reveals that AI-driven systems can reduce detection time by 73% and false-positive rates by 68% compared to traditional methods. The paper concludes with recommendations for implementing robust AI-based cyber defense frameworks and discusses future directions for adaptive security systems.
Reinforcement Learning-Driven Sparse Frequency Analysis for Accurate Underground Pipe Detection in GPR Scans
Ground penetrating radar (GPR) is an essential tool in the modern subsurface investigations due to the effectiveness and non-destructive potential of detecting objects. GPR is commonly used to identify and locate any subsurface structure and anomalous body. Ground surface variability, signal clutter and noise often hinder the use of GPR in the identification of underground pipes. In that regard, this paper presents a Sparse Frequency Transform-based Auxiliary Reinforcement Model that is capable of providing accurate and effective pipe detection inside GPR data. Image restoration to increase the quality of GPR scans is the starting point of the methodology where a sparse frequency transform is then used to isolate salient features and eliminate extraneous background signals. Such features are then fed into an auxiliary reinforcement model, and gradually, it enhances the detection accuracy by means of a rewarded-based learning process. Experimental evidence proves that the suggested model is better in relation to the traditional methods, including RefineNet, YOLOv4 and YOLOv8, in terms of accuracy, sensitivity, specificity, and precision. Combining sparse representation and reinforcement learning provides a solid platform on useful subsurface utility mapping and infrastructure evaluation.
Understanding Cognitive Cerebral Synergies: Leveraging R Analytics to Uncover AI and BIAI Insights
This study is an interdisciplinary research project that integrates Human Cognitive Neuroscience, Artificial Intelligence (AI), Brain-Inspired Artificial Intelligence (BIAI), and Data Analytics. Cognitive neuroscience examines the cognitive-cerebral synergies that involve complex interactions and coordination among brain regions involved in mental processes. AI and BIAI help analyse brain activity patterns and further develop an accurate predictive model. The use of R analytics acts as a catalyst in overall data analysis, data visualisation, and result modelling. In this study, R code produces graphical outputs for Brain Network Visualisation, AI-Inspired Neural Network Visualisation, Synergy Analysis Heatmap, Cognitive Load Visualisation, and AI Performance Comparison. This study focuses exclusively on understanding brain function and on developing AI models inspired by it. The study has applications in Brain-Computer Interfaces (BCIs), Cognitive Enhancement, and personalised medicine for the treatment of specific diseases. The quantitative research methodology of this study uses neuroimaging data from functional magnetic resonance imaging (fMRI), electroencephalography (EEG), or magnetoencephalography (MEG), which provide deeper insight into brain activity in healthy respondents. Such data can be processed using R packages such as caret, dplyr, and ggplot2, with emulated R code. This research methodology reports valuable insights into cognitive cerebral synergies and applies them in developing brain-inspired AI systems. The study aims to bridge the gap between cognitive neuroscience and AI. The future scope of this study may include integrating fMRI, EEG, and BCI to develop practical solutions for neurological disorders and disabilities in individuals.
A Light Weight Neural Network Model for Classification of Dementia
Dementia is a progressive neurodegenerative disease that is a major challenge to healthcare systems around the world and hence there is the need to have accurate and automated means of diagnosis. SMRI has become a useful modality for detecting neuroanatomical changes during the development of dementia, and yet, manual interpretation is tedious and prone to error. This paper will discuss a deep learning-based method of multiclass dementia classification based on a transfer learning model on the VGG-16 convolutional neural network. A big publicly available Kaggle data set comprising of about 44,000 T1-weighted brain MRI images was used, which comprised of four clinically relevant classes such as Non-Demented, Very Mild Demented, Mild Demented as well as Moderate Demented. All pictures were downsampled to the constant resolution size at 224 x 224 pixels and categorized as grayscale inputs to retain structural information and making them computationally efficient. A fine-tuning approach that was under a controlled strategy was used by unfreezing convolutional layers consecutively, allowing the detailed examination of convolutional layers parameter adaptation and generalization behaviours. The evaluation of the model performance was conducted based on accuracy metrics, learning curves, and analysis of the confusion matrix to present both quantitative and class-wise information. The final training accuracy of the proposed model was 89 percent and a validation accuracy of 76 percent which showed that the model converged well and the generalization was also good. The confusion matrix showed that Non-Demented cases were highly specific with potential difficulties likely to arise in making a distinction between the early stages of dementia since there were minor neuroanatomical overlaps.
Sensitivity-Based Breast Cancer Characterization on CBIS-DDSM Mammography Dataset Using Fine-Tuned EfficientNet-B2 Model
Mammography is crucial in detecting breast cancer early and reliably to reduce the rate of mortality. Models based on deep learning have shown encouraging results, but most of the current methods have poor sensitivity to malignant cases because the class imbalance is extreme and too much emphasis is put on accuracy-based evaluation. This paper presents a sensitivity-based classification framework of breast cancer based on a fine-tuned EfficientNet-B2 model of the CBIS-DDSM dataset. The suggested method integrates the use of class-weighted binary cross-entropy, partial fine-tuning, and higher convolutional layer, and the use of sigmoid to enable binary classification to counter the imbalance and enhance malignant detection. A large scale of experiments prove that the improved model is much higher than the fully frozen baseline and has a test sensitivity of 55.77% on malignant cases, with overall accuracy of 64.96% and ROC-AUC of 0.679. Confusion matrix, ROC curves, and learning dynamics further validate the robustness and clinical relevance of the proposed framework. The results highlight the necessity of sensitivity-oriented learning strategies for real-world breast cancer screening systems.
Predictive Model of NAAC Results of Accreditation Based On Machine Learning Methods Using A Multi-Year Institutional Data
India's higher education sector relies on NAAC accreditation for quality assurance, using a seven-tier grading system (A++ to C) that influences funding and autonomy. However, manual processes—lengthy Self-Study Reports, peer visits, and 12–24-month cycles—create backlogs for ~50,000 institutions. This study develops a machine learning framework to predict NAAC grades, integrating multi-year AISHE data (2010–2022) with NAAC records to form a 604,053-instance dataset. After preprocessing (KNN imputation, normalization, multicollinearity removal) and engineering 50 NAAC-aligned features, ensemble models (Random Forest, XGBoost, LightGBM) were compared. LightGBM achieved superior performance (92.3% accuracy, 91.8% F1-macro). SHAP analysis identified PhD ratio, pupil-teacher ratio, and research funding as key predictors. The reproducible model enables proactive institutional improvement and scalable accreditation forecasting.
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].
A Framework for Modi-Lipi Script Based On CAPTCHA
This paper presents a novel framework for generating CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) utilising the Modi Lipi script. Modi Lipi, a historical script primarily used for writing Marathi, presents a unique challenge for automated recognition due to its cursive and often complex conjunct characters. This inherent complexity is leveraged to create robust security measures. The proposed framework encompasses three main stages: Modi Lipi Character Generation, which dynamically creates diverse and warped script images; CAPTCHA Design and Integration, focusing on usability and security parameters for web applications; and Evaluation and Validation, where the generated CAPTCHAs are tested against both human users and state-of-the-art optical character recognition (OCR) and machine learning attacks. Our preliminary results indicate that Modi Lipi-based CAPTCHAs achieve a significantly lower success rate against automated attacks than standard Latin- or Devanagari-script CAPTCHAs, while maintaining a high degree of solvability for human users familiar with the script. This work contributes a culturally significant and effective method for enhancing digital security while simultaneously aiding in the preservation and popularisation of the Modi Lipi script.