DMPedia Lecture Notes in Computer Science & Engineering
DMP-LNCSE (DMPedia Lecture Notes in Computer Science and Engineering) is a peer-reviewed conference proceedings series published by Digital Manuscriptpedia. The series focuses on high-quality research presented at national and international conferences in computer science, information technology, electronics, electrical engineering, and related interdisciplinary fields. Each volume undergoes a rigorous double-blind peer review process, adhering to the highest standards of publication ethics as defined by the Committee on Publication Ethics (COPE). Each volume includes original papers that undergo peer review, plagiarism screening, and editorial quality checks prior to publication. DMP-LNCSE provides a reliable platform for publishing cutting-edge work in areas such as artificial intelligence, machine learning, cybersecurity, data science, networks, IoT, software engineering, and emerging technologies. The series aims to support conference organizers by offering a professional, standards-based publication outlet with ISBN assignment, DOI allocation (where required), online hosting, and indexing support. All published volumes are made available through Digital Manuscriptpedia’s digital library and are accessible for citation, academic reference, and scholarly use.
Enhancing Image Classification Accuracy Using Transfer Learning with Pretrained CNNs
This study conducts a detailed investigation into improving image classification performance using transfer learning with pre-trained convolutional neural networks (CNNs). The goal of this research is to improve the reliability and efficiency of models trained on diverse datasets through adaptive fine-tuning methods. Each of the four state-of-the-art CNN architectures (VGG16, ResNet50, InceptionV3, and EfficientNetB0) was fine-tuned and evaluated on CIFAR-10, Caltech-101, and a custom image dataset, demonstrating the variations observed in real-world datasets. The proposed method includes systematic preprocessing, feature extraction from pre-trained models, and selective fine-tuning of the upper convolutional layers with the Adam algorithm and dynamic learning rate scheduling for miniature fine-tuning of CNN architectures. This adaptive learning enables faster convergence, decreased overfitting, and improved accuracy without significant computational expenses. The research results show that EfficientNetB0 achieved the highest accuracy of 97.8%, followed by InceptionV3 (96.2%), ResNet50 (95.8%), and VGG16 (92.3%). The results further emphasize that the classification accuracy benefits substantially from transfer learning, especially on small or heterogeneous datasets. Furthermore, the hybrid fine-tuning mechanism was scalable and more resource-efficient for real-world use, allowing for models to be fine-tuned on-the-fly or with limited data. In general, this work demonstrates that transfer learning can operate as a paradigm for contemporary computer vision. It provides a balance between high accuracy and computational demands. In the future, we plan to expand this framework to include ensemble-based transfer learning and Vision Transformers (ViTs) to further improve robustness, interpretability, and performance across complex image classification settings.
Transfer Learning, Convolutional Neural Networks (CNNs), Image Classification, Fine-Tuning, EfficientNetB0
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Low Power Carry Select Adder Design in VLSI Systems
A digital circuit that performs addition is known as an electronic adder. Various numerical representations, including arithmetic and logical operations, can be used to create adders. Most adders work with binary numbers. The carry choose adder had the highest speed among the several adder types evaluated in this study. By employing the principles of the arithmetic logic unit (ALU), gate-level optimisation reduces the power consumption and area requirements of the carry selection adder. This study employed many methodologies, including the binary-to-excess converter (BEC) and Common Boolean Logic (CBL), to build the Carry Select Adder (CSLA). The comparative analysis indicates that the proposed ALU-based design consumes less power and has a smaller footprint than the conventional CSLA.
Common Boolean Logic (CBL), Arithmetic Logic Unit (ALU), adder, carry-select adder (CSLA).
Edge AI for Short-Term Delay Prediction in Distributed Logistics Networks (DECLN)
Last-mile logistics is becoming so complex that we need to make decisions to adapt to local conditions within a short timeframe, such as traffic congestion, weather, or even driver performance. This paper proposes an Edge AI model for distributed edge-cloud logistics networks (DECLN), deploying very lightweight predictive models on micro-hubs or delivery vehicles rather than relying solely on heavy predictive models in the cloud. Our edge AI models forecast short-term Delay Probability by combining local sensor data (activity reports for traffic congestion, severity reports for weather, estimates of arrival-time variation, and driver scores). Re-calculate Delivery Time in real time using local input data, then re-allocate delivery locations without requiring entirely cloud-based processing. We implement a basic regionalised logistic regression experiment comparing global (cloud-only) models to local (edge-simulated) models. These results, based on our framework, suggest that much of the reported prediction latency will be reduced and bandwidth usage will be lower while improving localised decision-making accuracy. This paper contributes practical methods to improve responsiveness as delivery environments change.
Edge AI, Logistics, Delay Prediction, Last-Mile Delivery, Micro-Hub Optimization, Edge-Cloud Collaboration
A 3D Mesh NoC Architecture with Supplementary 2D Links and Progressive Shortest-Path Routing
In this paper, we study a mesh-based 3D network topology that is enhanced with a small number of additional 2D links. The 3D network is formed by vertically stacking and scaling identical 2D mesh planes along the Z-axis. Within each 2D plane, four supplementary links are added to connect the corner nodes to nodes located in the central region of the mesh. These supplementary links are activated only when the 2D routing distance exceeds N in an N × N × N 3D mesh N >= 4. Under this condition, the added links consistently provide a shorter path, requiring fewer than N hops within the 2D plane, thereby reducing the overall hop count. The proposed network is evaluated using a progressive shortest-path routing algorithm. This algorithm computes the minimum 2D distance from the source node to candidate nodes equipped with supplementary links, as well as the distance from those nodes to the destination. The node that minimizes the combined 2D distance is selected, and the packet is routed through it. If the source and destination nodes are located in the same 2D plane, the packet is routed entirely within that plane. Otherwise, once the packet reaches the selected node, it is forwarded along the Z-axis, either upward or downward, to the destination plane, where it is finally routed to the target node. The results show the robustness of the proposed network using the link utilisation graph.
Network On Chip, System On Chip, Latency, Link Utilization, 3D NoC, Routing Algorithm.
Smart Disease Detector in Crops: A Deep Learning-Based Approach for Automated Plant Disease Identification
This paper presents a practical pipeline for image-based plant disease detection that operates without pre-trained weights or internet access. A compact Efficient Net-like convolutional network is trained from random initialisation on the Plant Village dataset (∼ 41,000 snapshots, 15 classes) at 224 × 224 resolution. Adam, Intensive Augmentation, Reduce LROn Plateau, Early Stopping, and the sampling of the optimal checkpoint to stabilise convergence and avoid overfitting are used to optimise the geometry. Despite early validation oscillations typical of de novo training, the model converges reliably and attains 98.4%–98.9% accuracy on held-out data, with a highly diagonal confusion matrix and uniformly strong per-class precision/recall. These results show that carefully tuned schedules and lightweight regularization can substitute for transfer learning when bandwidth or policy constraints prevent downloading external backbones, enabling classroom, extension, and field deployments. The paper details dataset preparation, architecture and training choices, learning curve behavior, and common failure modes, and concludes with limitations and a roadmap for higher-resolution, field-domain generalization and efficient on-device inference.
PlantVillage, plant disease detection, training from scratch, data augmentation, offline deployment, EfficientNet.
Steering Technique-Based Detection and Optimization of Overlapping Coverage in Wireless Sensor Networks
A wireless sensor network (WSN) is a collection of autonomous sensors and networks. The performance of WSN depends on the nature of the sensors and their deployment. Deploying many sensors in a typical region requires a high level of energy consumption for the same data range, due to overlapping. The detection and optimisation of overlapping coverage are the major problems in WSN. This paper focuses on the detection improvement and optimization of overlapping nodes. In this paper, a steering technique is proposed that contains six phases for the detection and minimization of the overlapping nodes of sensors containing adjustable radii. The area of a redundant node is also adjusted to overlap, and the rest of the redundant nodes are turned off. The radius of nodes is enhanced using two steps: (1) these nodes tend to enlarge their radius up to a threshold value, and (2) with the help of the Venn diagram and set theory, the nodes double their radius so that overlapping and intersections can be avoided. Finally, it is found that some nodes that were not overlapping are now left, and the network's lifetime has increased.
Wireless sensor network, Overlapping coverage, Steering technique, Redundant nodes
Blockchain-Enabled Predictive Analytics for Real-Time Supply Chain Decision Making
Modern supply chains are still troubled by several problems, including scattered data, a lack of transparency, and inefficient, slow, and error-prone decision-making processes. These problems often result in inaccurate forecasts, late detection of supply chain interruptions, and low resilience of the entire operation. This paper proposes a Blockchain–Machine Learning Integrated Framework that will create a secure, transparent, and analytical supply chain environment. The blockchain is expected to provide a permissioned distributed ledger that will allow the use of efficient consensus techniques, thus providing prompt event finalization and secure data sharing among the parties involved. Based on reliable data from the blockchain, the machine learning layer combines forecasting models, anomaly-detection systems, and real-time data streams to continuously improve predictive accuracy and enable timely decision-making. Certainly, a medium-sized retail supply chain scenario comprising 350,000 events across various points is used as a testing ground for evaluating the proposed framework. The experimental results indicate that the system performance improvements are quite significant: decision latency is reduced, forecast precision is increased, and anomaly detection is more accurate than the traditional and ML-only baselines. Furthermore, the system maintains high throughput as the number of nodes increases, demonstrating its applicability to real-world scenarios. The initial rollout and trial illustrate significant potential across diverse sectors such as manufacturing, pharmaceuticals, agriculture, and logistics by enabling monitoring, reducing data-processing risks, and supporting automation. In this way, the discoveries point out that the confluence of blockchain and machine learning fortifies the formation of forthcoming supply chain systems that will be robust, data-driven, and able to run autonomously.
Blockchain, Machine Learning, Real-time Decision Making, Supply Chain Management, Predictive Analytics.
Reducing Latency in Network-on-Chip Using an Enhanced Mesh Topology
This work presents a 2D Mesh-based topology with four extra lateral links, connected to eight special routes at the corners and the middle of the mesh. This work aims to reduce the network diameter by restricting the maximum hop count to N−1 in a regular N × N mesh. The proposed network is simulated using Dijkstra’s shortest-path routing algorithm, and it is found that lateral links are heavily used and incur average latencies of 10.662 and 11.869 cycles for N = 4 and N = 5, respectively. Also, the simulated results show a 56% reduction in maximum latency with the proposed topology for N = 5.
Network On Chip, System On Chip, Latency, Link Utilization, Topology, Hop Count.
IoT-Enhanced Smart Campus Management System
The emergence of smart campus technologies has transformed educational environments by integrating biometrics, the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML). This paper reviews key advancements in automatic attendance systems, intelligent learning environments, and campus infrastructure optimization. Biometric approaches such as fingerprint, facial recognition, RFID, and NFC enhance the accuracy of real-time attendance monitoring, reducing proxy incidents and administrative workload. IoT frameworks further enable wireless connectivity, real-time analytics, and cloud-based storage, improving system reliability and responsiveness. Concurrently, AI-driven Learning Management Systems (LMS) and Campus Management Systems (CMS) provide personalized learning recommendations, student performance predictions, and risk assessment models with predictive accuracy exceeding 90%. Based on these insights, an IoT-based Smart Campus Management System is proposed that integrates RFID check-in, AI-powered facial verification, and teacher-in-the-loop supervision for robust, privacy-preserving attendance monitoring. This multi-sensor, human-assisted framework enhances accuracy, scalability, and decision-making, contributing to the development of adaptive, secure, and data-driven smart campus ecosystems for future-ready education.
Smart Campus, IoT, Biometric Attendance, RFID, Face Recognition, AI, Machine Learning, Campus Management System (CMS), Edge Computing
AI and Machine Learning Applications in Smart Agriculture: A Comprehensive Review
Agriculture is undergoing a significant technological change through the use of artificial intelligence (AI) systems and machine learning (ML) techniques. The global demand for food security, sustainable practices, and environmental protection has created a need for intelligent systems that can produce the highest yields with the fewest resources. Machine learning has, over the last 10 years, become a major component of various agricultural fields, including yield forecasting, disease identification, irrigation management, and soil quality assessment. Various supervised and unsupervised learning methods, including convolutional neural networks (CNNs), long short-term memory (LSTMs), support vector machines (SVMs), and random forests, have been used by scientists, yielding prediction accuracies far beyond those of traditional statistical approaches. Deep learning, especially hybrid CNN–LSTM architectures, is very promising for detecting crop stress, nutrient deficiency, and yield variation, and can even be done in real time. The current review echoes the findings of 40 landmark research works that trace precision agriculture through machine learning from 2021 to 2025 and examine how it has contributed to the development of sustainable farming methods. Besides, it substantiates the existence of fixed obstacles, such as data heterogeneity, scalability, and ethical data governance; it also pinpoints new and forthcoming research on AI-IoT convergence and multi-sensor fusion frameworks aimed at generating resilient, resource-efficient agricultural ecosystems.
Machine Learning, Agriculture, LSTM, Artificial Intelligence. CNN, Deep learning, Remote Sensing.
Cancer Disease Prediction using Machine Learning
Cancer is still among the leading causes of death on the planet. Poor outcomes and higher mortality rates create an urgent need for early diagnosis using high-accuracy cancer-detecting methods. Some diagnostic methods in use include biopsy, imaging, and clinical evaluation. These techniques are highly expensive, time-consuming, and prone to errors due to human involvement. With the advancement of artificial intelligence, machine learning has become one of the strongest tools for predicting, classifying, and diagnosing cancer. Machine learning algorithms will therefore analyze a large volume of patient data, including genetic information, medical images, and patient clinical history, for any unusual patterns that may not be easily spotted by experts. Useful algorithms include Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbours, and Deep Learning. This study will focus on selecting appropriate algorithms, preprocessing, and feature extraction to achieve high prediction accuracy. Challenges also persist in data imbalance, overfitting, and ensuring patient data confidentiality. However, the integration of machine learning into cancer diagnosis has been promising, enabling higher early detection rates, supporting personalised treatment planning, and promoting low-cost, efficient, and patient-centred health care solutions.
cancer prediction, machine learning, data preprocessing, early diagnosis, health care AI.
Green Gauge-Decentralized Carbon Accounting: A Blockchain-Based Framework for Transparent and Scalable Emission Tracking
Day after day, climate change intensifies, necessitating tracking solutions for carbon emissions that offer transparent operations and efficiency, alongside scalability and sustainable behavioural incentives. The proposition to track carbon emissions is not new, yet standard tracking systems present multiple deficiencies, including double reporting, fraud, high operational costs, and constrained access for small organisations. We have developed a blockchain system that follows a framework to track both carbon emissions and trading activities, using smart contracts and decentralised ledger technologies to establish security, trust, and automation. Our system requires IoT sensor integration and AI analytics to enable continuous monitoring and safe storage, along with direct carbon trading without third-party involvement. The proposed framework addresses blockchain energy consumption issues by examining Proof of Stake (PoS) and hybrid consensus models. The model presented facilitates a massive reduction in carbon emissions and enhances transparency and efficiency.
Blockchain, Carbon Emissions, Climate Change, Decentralization, Smart Contracts, Carbon Credit Markets
A Review on Earlier Diagnosis of CVD Using Chest CT Image Segmentation and Classification
In recent years, cardiovascular diseases (CVDs) have become a major global cause of death. These illnesses start with mild symptoms that worsen over time. During the early stages of CVD, fatigue, dyspnea, oedema, fluid retention, and other indications are frequently seen. Among the most common CVDs are angina, mitral regurgitation, congenital heart defect (CHD), arrhythmia, cardiomyopathy, and coronary artery disease (CAD). The best tools for identifying CVDs include clinical techniques such as blood tests, electrocardiography (ECG), and medical imaging. Among them, cardiac computed tomography (CT) is increasingly used for CVD diagnosis, tracking, planning, and prediction. Healthcare practitioners find it difficult to diagnose CVD despite the benefits of CT imaging data because of a number of factors, including inadequate contrast and a large amount of data. The diagnosis of CVDs using CT imaging has incorporated machine learning (ML) and deep learning (DL) approaches, and research in this area is now underway. To better understand the potential applications of machine learning (ML) and deep learning (DL) techniques for chest CT image segmentation and classification—with an emphasis on CVD detection—this review presents a thorough overview of studies that have explored these areas. To that end, we have carefully reviewed 77 papers gathered from various databases, such as Google Scholar, PubMed, and Springer, among others. The research under consideration has undergone rigorous evaluation to highlight the key characteristics, advantages, and disadvantages of the numerous methods established to date for using CT scans to identify CVD.
Cardiovascular disease, CT Image, segmentation, classification, diagnosis, Machine learning, deep learning
Spatiotemporal Graph Neural Networks for Air Quality Forecasting: A Comprehensive Review and Future Direction
Prediction​‍​‌‍​‍‌ of the air that we breathe is the first step in the prevention of health risks and to avoid the negative effects of pollutants, i.e., PM2.5, O3, and NO2. These pollutants exhibit complex spatial and temporal patterns, and their behaviour is influenced by factors such as weather, land use, and emissions. Incorporation of Spatiotemporal Graph Neural Networks (STGNNs) has become a revolutionary solution by utilizing graph structures for representing spatial relationships and temporal mechanisms for capturing the changes. Here, this paper constitutes a major part of the research in this domain and extensively covers methodologies, experiments, and applications. Some of the outstanding features are extreme handling in E-STGCN, physics integration in DGM, and dynamic edges in DST_GNN. The performance terms illustrate MAE reductions from 13% to 57% relative to baselines. The issues, such as scalability and consistency, are acknowledged here, while subsequent ideas for hybrid systems and real-time forecasting are proposed.
Deep Learning, Graph Neural Networks, Spatiotemporal Graph Neural Networks (STGNNs), Air Quality Forecasting, Physics-Informed Learning, Spatio-Temporal Modeling.
An XML transformed method to Enhancement effectiveness of Password Authentication System using Graphical Images
A crucial part of the security infrastructure is now set up for user authentication and data security. Most users choose text passwords as a means of protecting the security and privacy of their computer system. However, there is always a potential that someone will enter their password in an unsafe method or choose a weak or insecure one. By leveraging the simplicity of image recall over word memory for authentication, graphic passwords are an effective alternative to conventional text passwords. This study presents an XML-transformed Method for improving the Effectiveness of a password authentication system utilising graphical images. The server analyzes the password image with the graphical pattern when a user loads it, using drift and stroke length to confirm its authenticity. Several graphical pattern types emerge when the input pattern is made vulnerable to various changes. When a user enters a password image, the password pattern is extracted and mapped from the recovered pixel values, which are stored in an XML pattern database. Performance analysis parameters include authentication, security, and accuracy. Compared to other image password mappers, this method works better, according to the comparative performance test.
Authentication System, Graphical password, XML (eXtensible Markup Language), Security, Textual password.
Design and Implementation of an Efficient Intrusion Detection System Using Deep Learning for Enhanced Cybersecurity: A Comprehensive Survey
The need to develop advanced DSS to identify both known and unknown threats has stemmed up due to the rising rate of cyber-attacks. Conventional IDS, such as signature and anomaly detection systems have a few shortcomings, such as large false-positive levels, inflexibility to zero- day attacks and low scalability. This paper surveys in detail the deep learning-based IDS techniques, and examines the contribution made by the deep learning-based IDS techniques to various environments, including Internet of Things, cloud computing and industrial control systems. It discusses the different architectures of CNN, RNN, LSTM, autoencoders and hybrid models in detail. The problems of the real-life implementation, benchmark datasets, and comparative studies with a focus on the gains of the detection accuracy and real-time response are outlined. Revenues of such improvement, there are still challenges that require improvement, such as the complexity of computation, the imbalance of the data, the vulnerability of adversarial and the limitations of deployment. The paper ends by recommending the trends of future investigation in the fields of lightweight model generation, federated learning and adversarial robust architecture, to next-generation IDS solutions.
Intrusion Detection System (IDS), Deep Learning, Cybersecurity, Anomaly Detection, Network Security, Machine Learning.
Enhancing Weather Prediction Accuracy Through AI and Computational Modeling
It is one of the most essential, though not an easy field in environmental science is weather forecasting. This is because the atmosphere is unpredictable which always poses a challenge to the scientists and researchers to accurately predict. Accurate projections are relevant in areas of agriculture, transportation, disaster preparedness, and safety of the people. The application of computational methods, Artificial Intelligence (AI) and Machine Learning (ML) to weather prediction significantly improved the accuracy and reliability of weather forecasts over the years, and the paper presents an application of weather prediction based on large-scale meteorological datasets, satellite images, and powerful ML algorithms, such as Long Short-Term Memory (LSTM) networks and Random Forest models. The system is run on an organized pipeline comprising of data collection, preprocessing, model training, and forecast delivery with the help of a user-friendly interface. According to experimental tests, AI-based techniques are more accurate, more adaptable and more efficient when compared to traditional statistical approaches, also exhibiting high level of scalability, which also makes it applicable to various climatic zones and geographical locations. Furthermore, the suggested system has physical advantages to the farmers, governmental agencies and industrial consumers. Future research is aimed at combining sensor data based on Internet of Things (IoT) with deep learning structures to facilitate hyper-local predictions and real-time warning during extreme weather conditions.
HTML, Python, Jscript, Machine Learning, Artificial Intelligence.
AI-POWERED MINDSHIELD MODEL FOR ANALYZING TOXICITY AND THREATS IN ONLINE INTERACTIONS
Cyberbullying, hate speech and harassment have been reinforced by the rise of internet platforms. speech, and harassment have been enhanced by the booming nature of internet platforms. These are very difficult roles psychologically, morally and socially. the roles that are very difficult psychologically, morally and socially. In this article, the authors present the system known as MindShield, an artificial intelligence-based system, whose goal is to identify, classify and study dangerous activities online. paper, the authors present the system known as the MindShield that is the artificial intelligencebased system, the purpose of the system is to identify, classify, and study dangerous online activities. Theprogram involves natural language processing (NLP), semantic integration as well as deep learning classifiers to identify the presence of toxic behaviors in text communication. natural language processing (NLP), semantic embeddings as well as deep learning classifiers in identifying presence of the toxic behaviors within the textbased communication. Regarding the mind shield, compared to its conventional moderation mechanisms, MindShield should prioritize the elements of explainability, risk identification and adaptive learning, thanks to which it is able to adapt to the reaction to new categories of cyber threats. as compared to its conventional moderation mechanisms, MindShield ought to prioritize the elements of explainability, risk identification and adaptive learning, whereby it is able to adjust to reaction to new categories of cyber threats. The experimental results are characterized by high performance compared to the benchmark datasets, as well as strong improvement in precision, recall and interpretability. comparison with benchmark datasets, as well as high improvement in precision, recall, and interpretability.
Bad online socialization, AI system, cyberbullying detection, natural language processing, semantic embeddings, transparent AI, internet safety.
AI-Enhanced Campus Recruitment Portal for Direct Student-Recruiter Interaction and Smart Resume Enhancement
This work provides an AI-powered campus recruitment tool designed to automate the engagement processes in university recruitment. It seeks to solve some of the major challenges found in recruitment processes, such as skill-role mismatches, delays from manual screening, scalability issues, and recruitment bias [1]. The tool employs ML generalisation for candidate-job matching and feedback loops, as well as credential parsing and improvement using NLP [2][7][11]. Due to the work’s cloud-native model, designed with Docker and Kubernetes, it provides concurrent cross-institutional recruitment at scale and with high performance [5]. Incorporating AI for resume boosting reduces the time spent reviewing resumes in the hiring process by almost 60%. Additionally, it increases match improvement and graduate employability rates. Thanks to the system’s modular structure, it is compatible with large company HR systems, making it applicable to hiring in both educational and corporate settings [9].
AI, machine learning, NLP, campus hiring, recruitment automation, resume enhancement, HR technology.
Empirical Benchmarking of Vision-Language Transformer Combinations for Visual Question Answering Tasks
The task of Visual Question Answering (VQA) also requires intelligent methods that bring together visual and textual information in order to compute accurate answers. An exhaustive empirical evaluation of six specialized vision language transformer models for VQA are conducted on the DAQUAR dataset. The experiments use a Vision Transformer (ViT), a Data-efficient Image Transformer (DeiT), and a Swin Transformer as image encoders, and BERT and RoBERTa as language models. Following the same experimental protocol, we track per-epoch validation accuracy, and consider a potential impact on computational efficiency. The results produced the following peak accuracies: ViT + BERT (65.50%), ViT + RoBERTa (68.47%), DeiT + BERT (61.15%), DeiT + RoBERTa (66.47%), Swin + BERT (73.42%) and Swin + RoBERTa (64.50%). The Swin+BERT combination produced the best accuracy among the combinations tested, achieving a peak accuracy of 73.42%. This illustrates the possible power of hierarchical vision transformers combined with powerful language comprehension on VQA tasks. We hope that this benchmark provides useful guidance to researchers when seeking effective vision-language models based on performance and resource limitations.
VQA, Multimodal, Vision-Language Models, NLP, CV, Transformer.
Edge-Based AI Framework for Anomaly Detection in IoT Networks
The growing deployment of the Internet of Things (IoT) technology increases the complexity and vulnerability of today's networks, making effective anomaly detection a demanding necessity. Conventional security solutions in the cloud are always either too slow, too bandwidth-intensive, or inflexible for IoT. In this context, this paper proposes an edge-based artificial intelligence (AI) framework for detecting anomalies in IoT networks. The framework introduced in this work leverages edge computing to provide real-time processing and analysis of data near IoT devices, enabling fast detection of anomalous network traffic. Lightweight AI models are installed on edge nodes to monitor traffic patterns and device activities with limited computation and energy. The model is sensitive to emerging attack patterns and varying network environments to maintain stable detection performance. It is successful in distinguishing anomalies such as attacks, hijacked devices and abnormal resource behavior. The experimental results show that the edge-based architecture can achieve a lower detection latency and network bandwidth usage than centralized cloud-based solutions, whereas it maintains high detection accuracy and low false positive rates. The results show that providing an AI intelligence network in conjunction with IoT devices will be highly beneficial for securing, scaling out, and operating resiliently against emerging threats in IoT networks. This architecture offers a feasible approach to securing massive IoT deployments, such as smart cities, industrial automation, and healthcare.
Internet of Things (IoT); Edge Computing; Artificial Intelligence (AI); Anomaly Detection; Network Security; Intrusion Detection System (IDS); Machine Learning; Edge Intelligence; Real-Time Monitoring and Detection; IoT Security Architecture.
Blockchain Based Electronic Health Record System
The rapid digitisation of healthcare has led to widespread adoption of Electronic Health Records (EHRs); however, the majority of currently available systems are based on centralised designs that have security vulnerabilities, lack interoperability, and fail to provide proper patient control. Centralised data repositories are likely to suffer from unauthorised access, data breaches, and single points of failure, while fragmented systems limit the smooth exchange of information between healthcare institutions. The alternatives provided by blockchain technology include a decentralised, tamper-resistant solution that encompasses immutable records, cryptographic security, and auditability. The paper proposes a Blockchain-Based Health Record System that integrates blockchain technology with smart contracts, AES-256 encryption, and the InterPlanetary File System (IPFS) to ensure security, scalability, and patient-centred healthcare data management. In the suggested design, IPFS, which can encrypt and store medical records off-chain, is used to handle access control, consent enforcement, and audit logs, while a blockchain smart contract handles these functions. The suggested system will increase data confidentiality, eliminate single points of failure, and enable data sharing among medical professionals. The system corresponds with the current data protection rules and ethical medical care, as it provides patients with complete ownership and control over their medical records. The paper shows that EHR systems with blockchain have the potential to enhance the security, transparency, and interoperability of digital healthcare ecosystems.
Access control, Blockchain, Decentralization, EHR, Encryption, Healthcare Security, IPFS, Smart contracts
Green Connect: Social Media Platform for Environment
The environment is the natural world, consisting of physical, biological, and chemical factors that influence the existence of life on Earth. The environment is critical in sustaining life and supplying the resources required for all organisms' existence and well-being. It is a complex system with interdependent components, in which changes in one component can affect others. Nowadays, the Environment is deteriorating rapidly, so to save it, we need to prioritise our goals for the Environment's welfare, as it is crucial to our survival. Various initiatives have been taken by several countries to protect and preserve nature. Green Connect is a small step toward implementing these initiatives on the ground. This paper is a study of a social media platform that can help spread awareness about the Environment, so that people can spend time with beautiful nature around them and share special memories through media. This study can promote new tree plantings in the surrounding areas for better Environmental health.
Environment, Deforestation, Green Connect, API, Android studio, Database.
Automated Student Document Analysis and Query System Using OCR and Conversational AI
Today’s educational requirements often involve analysing legal and academic documents, a tedious task. These documents, especially, contain lengthy content that people may fail to read or even understand due to language barriers and their complexity. For educational institutions, it may take a long time to manually parse documents. Even for non-literate or non-native English speakers, they are not aware of the structure or content of the files. To address these problems, we proposed a solution to save time. This project primarily focused on the education sector and developed an Automated Student Analysis and Query System that combines Optical Character Recognition and Conversational AI. It produces text summaries in both Telugu and English. Once a document or file is uploaded into the system, it is processed through Gemini AI. Users can ask the chatbot questions to get instant answers. This approach showcases how we can reduce manual efforts.
Optical character recognition (OCR), Conversational AI, Gemini AI, Bilingual Summarization, Legal and Academic Documents, Information Retrieval, Educational Document Management.
An Investigation of Millimeter Wave Generation Schemes for Future Networks
Optical generation of MMW is a viable solution for operation at higher frequencies. Integration of RoF transmission with existing cellular transmission offers a low-cost solution for futuristic high-speed communication networks. This study investigates various methods for the optical generation of Millimeter waves (MMW) and the effectiveness of the Radio over Fibre system for their transmission. This brief summary of the key research on photonic generation of MMW will motivate researchers to develop improved techniques for MMW generation and transmission with minimal system cost and complexity.
Millimeter wave, External modulation, Wavelength conversion, Optical heterodyne, MZM, OSSR, RFSSR
Enhancing Facial Emotion Recognition Using Convolutional Neural Networks: Addressing Challenges in Class Imbalance and Generalizability
FER is important in today's human-computer interfaces and is used in medicine, education, security and the entertainment world. A hybrid CNN-LSTM model is proposed here to classify facial expressions that show an-ger, disgust, fear, happiness, sadness, surprise or none of them. The dataset was corrected with normalization and improved using rotations, zooming and flipping to help reduce class imbalance. Both convolutional layers and LSTM layers play a part in the architecture of detecting features across time and space. Tests using accuracy, precision, recall and F1-score confirm that the model performs better than many others: 93.25% accuracy, 92.80% precision, 92.50% recall and 92.65% F1-score. These results show that the proposed method is better than CNN (87.10%) and ResNet-50 (89.30%). "Happy" and "Neutral" expressions are recognized with a high level of accuracy, while fewer "Disgust" and "Fear" images are accurately recognized because they are similar and there are not enough examples in the dataset. The good results from the model are not completely reliable since the dataset is too controlled for real-world outcomes. Going forward, it would be useful to add attention mechanisms, make the datasets more inclusive and develop automatic multisensory emotion recognition systems. The work developed a practical framework for FER that can be implemented in real situations.
Convolutional Neural Networks (CNN), Deep Learning, Emotion Classification, Human-Computer Interaction, Facial Expression Analysis, Image Processing, Feature Extraction, Confusion Matrix.
Smart Traffic Management and Accident Detection System: A Review
Abstract: This paper shows us how deep learning and computer vision are changing the way we can handle our traffic and prevent accidents. With the rapid growth of A.I. and deep learning becoming faster and more powerful, it helps monitor traffic, detect accidents, and even predict them. This technology can do this in real time with impressive accuracy. In conditions like bad weather or low visibility, older traffic systems that relied on fixed rules or basic sensors often struggled to keep up. To solve the problem, researchers have been exploring models such as YOLO11-AMF, RT-DETR-EVD, and hybrid GAN-based frameworks. These models are now using convolutional and transformer-based networks to identify and classify objects more precisely. In this review paper, we use 10 different recent studies and compare them. The selected models primarily use deep learning for accident and emergency vehicle recognition. Some of these approaches even combine audio and video data to give better results. We can evaluate them based on their datasets, mean Average Precision (mAP), and how quickly they can make predictions. This paper also discusses the challenges in this field, including limited training data, high computational costs, and models that don’t always perform well across different environments. Looking forward, we can clearly highlight trends such as federated learning, self-supervised learning, and model training, which are making it easier to run these systems on IoT and edge devices. Overall, this review shows just how powerful deep learning and AI models are in building safer, smart transportation systems and how close we are to fully automated, intelligent traffic management in the near future.
Smart Traffic Management, Accident Detection, Deep Learning, Computer Vision, YOLO, DETR, Emergency Vehicle Detection, GAN, Multimodal Fusion, Edge AI
Application of Convolutional Neural Networks (CNNs) in Agriculture
AI and machine learning applications are on the rise. Especially, deep learning is used very frequently in research these days. Convolutional Neural Networks (CNNs) are one such popularly used deep learning approach in the agricultural domain. In smart agriculture, CNNs find its use in identification, classification and mapping problems. It is used for disease & weed: identification and classification. It is also utilized for crop yield prediction. Its combination with IoT and Drone devices enhances its application perspectives. Hence, this paper provides insights into the CNN methodology. It reviews the available datasets and the effectiveness of existing CNN application in the agricultural domain.
Deep Learning, Convolutional Neural Networks, Smart Agriculture, Crop Disease, Weeds Detection, Yield Prediction.
The IDS Framework for Recognizing Unusual Activity in IoT Networks
Protecting against cyberattacks is becoming increasingly crucial as the Internet of Things (IoT) and its resources expand rapidly. The most recent sophisticated attacks leverage characteristics of ordinary traffic to launch novel, covert attacks, while more conventional perimeter-based defences (such as firewalls and authentication) struggle to identify them. The IoT Network Intrusion Dataset (IoTID20) is used to test a lightweight network intrusion detection system designed specifically for the Internet of Things. The approach is based on a workflow for Knowledge Discovery in Databases (KDD) that incorporates thorough data preprocessing. To optimise the trade-off between processing efficiency and efficacy, Artificial Neural Networks (ANNs), K-Nearest Neighbours (KNN), and Random Forests (RFs) are the classifier models created and evaluated. They can also be adjusted for edge implementation. The usual metrics mentioned in the literature are followed while implementing the performance measure. According to the experiment’s findings, RF outperformed the state of the art by 1.2% in accuracy, achieving the best performance in real-time intrusion detection for IoT scenarios.
IoT Security, Random Forest, Artificial Neural Network, K-Nearest Neighbors, Dataset IoTID20
End-to-End In-Silico Antibody Humanization Pipeline for Google Colab
Antibodies are Y-shaped proteins produced by the immune system to recognise and tag foreign substances for destruction. Therapeutic antibodies have become an important class of drugs for treating infectious diseases, autoimmune disorders, and cancers, but many promising candidates cannot be used directly in humans due to their immunogenicity. The immune system may recognise these non-human antibodies as foreign and attack them, rendering them unsafe for clinical use. Antibody humanization tries to solve this problem by modifying the amino acid sequence so that the antibody looks more human while still keeping its structure, binding function and basic developability properties. In this project, I built an end-to-end, fully in-silico antibody humanization pipeline in Google Colab. The workflow starts from raw antibody FASTA sequences, applies standardized numbering to define framework and CDR regions, and then uses protein language model embeddings and a supervised classifier to estimate how “human-like” each sequence is. Guided by this humanness score, the pipeline proposes small mutations mainly in the framework regions, keeping CDRs fixed, and accepts only those changes that improve the score. Finally, ColabFold is used to check that the humanized sequence still preserves a reasonable antibody fold at the structural level. By combining AI-based sequence representations with simple, transparent design rules in a Colab notebook, this pipeline aims to make antibody humanization faster and more accessible to students and small labs, while still keeping the steps understandable and easy to modify.
Antibody humanization, immunogenicity reduction, protein language models, sequence embeddings, humanness scoring, framework mutations, in-silico antibody design, ColabFold structure prediction