International Journal of Multidisciplinary Conference Proceedings (IJMCP)
The International Journal of Multidisciplinary Conference Proceedings is run by the registered research consultancy, Research Consultancy on Social and Management Development. This consultancy is officially registered with the Securities and Exchange Commission of Pakistan under Section 1 of the Companies Act, 2017 (XIX of 2017), with Registration Number 0220469. The journal adheres to the highest standards of publication ethics and quality, ensuring that all published works undergo a meticulous double-blind peer review process to maintain their academic and professional value.
Research Consultancy on Social and Management Development
Integrated Security for Smart Homes in IoT-Enabled Cities: Trust,Privacy, and Resilience – A Survey
The rapid growth of urban areas and advancements in information technology have paved the way for the development of smart cities and homes, leveraging the Internet of Things (IoT). Smart cities and homes represent the future of urban living, offering enhanced efficiency, convenience, and sustainability. This survey paper provides a comprehensive overview of the progress made in the domain of smart homes, focusing on the integration of IoT technologies. It explores the architectures, applications, devices, and communication technologies that have been implemented to achieve desired outcomes in smart home environments. Additionally, the paper highlights the key challenges researchers are addressing, particularly in the realm of security, to ensure the safe and reliable operation of smart homes within smart cities. By examining the current state of the art, this paper aims to provide insights into the future direction of secure smart homes and their role in the broader ecosystem of smart cities
Smart City, Smart Home, Iot, Iot Security, Home Automation, Communication Technologies, Iot Challenges, Secure Smart Homes.
Copyright (c) 2025 International Journal of Multidisciplinary Conference Proceedings (IJMCP)
NIOA: A Neuro-Inspired Multi-Objective Optimization Framework for Energy-Efficient Clustering in 3D Wireless Sensor Networks
Energy-efficient clustering remains a critical challenge in wireless sensor networks (WSNs), particularly in three-dimensional deployments where existing approaches fail to balance energy efficiency, network lifetime, and throughput. Current clustering algorithms exhibit significant limitations, including static configurations, inability to adapt to heterogeneous nodes, and vulnerability to network dynamics such as obstacles and node mobility. This paper introduces a Neuro-Inspired Optimization Algorithm (NIOA) for WSN clustering that addresses these gaps through an integrated approach combining artificial neural networks, reinforcement learning, and adaptive mechanisms. Our multi-objective framework dynamically balances energy conservation, network lifetime extension, and throughput optimization through a comprehensive fitness function and self-adaptive parameter tuning. Extensive simulations across diverse operational scenarios demonstrate NIOA's superior performance over existing approaches, with substantial improvements in network lifetime, energy efficiency, and packet delivery ratios. The algorithm exhibits remarkable resilience to node failures, environmental obstacles, and security threats while effectively integrating with energy harvesting technologies. These findings indicate that neural-inspired approaches with reinforcement learning can significantly enhance WSN performance in dynamic environments, suggesting promising directions for future intelligent network management systems
Wireless Sensor Networks, Energy Optimization, Neural Networks, Reinforcement Learning, Clustering Algorithms
Quantum Computing and Applications: Its Effects on Cybersecurity
The study examined final-year undergraduate business students’ perception regarding the relative importance of different employability skills for entry-level positions; the extent to which academic, personal management and teamwork skills are fully developed at the point of graduation; and the relative importance of different learning methods in aiding the acquisition of employability skills. Based on a structured survey questionnaire administered to final-year undergraduate business students, the four most important employability skills for recruitment to entry-level positions are communication skills, learning skills, positive attitudes and behaviours, and problem-solving skills. While the literature finds communication skills, problem-solving skills and interpersonal skills often appear among the highest ranked skills in terms of importance, the significance of this study is that ‘learning skills’ ranked as the second most important employability skill. In today’s fast-paced work environments that are characterised by rapid knowledge obsolescence and an unknown future, willingness to learn and proactive lifelong learning are key to sustaining long term employability. Based on students’ responses, the areas that ranked below expectation were numeracy skills, IT literacy skills, and ability to ‘resolve and manage conflicts’ within teams. The study finds that students used a combination of traditional and student-centred learning methods and pedagogies to acquire employability skills
Millions of people are affected by retinal abnormalities worldwide. Early detection and treatment of these abnormalities could arrest further progression, saving multitudes from avoidable blindness. Manual disease detection is time-consuming, tedious, and lacks repeatability. In this project, we work on deep learning models to detect eye disease using Retinal fundus photographs and OCT scans using cutting-edge computer vision techniques with deep neural networks. This project addresses the need for a user-friendly, Deep Learning based application that analyzes Optical Coherence Tomography (OCT) scans and Retinal fundus photographs for early detection of retinal diseases. Leveraging deep learning models, the application offers personalized health recommendations, tracks users' eye health history, and provides educational content to promote awareness and preventive care. To enhance user interaction and accessibility, we integrate a large language model, enabling users to prompt questions regarding their eye health and receive informative responses. This AI-driven assistant supports users by explaining diagnoses, suggesting preventive measures, and guiding them toward appropriate medical consultations. This solution aims for early detection and to bridge the gap between advanced eye care and accessibility, enabling proactive management of retinal health without needing immediate in-person visits to healthcare professionals
Design and Mathematical Modelling of Solar Powered Cardiac Pacemaker
Least physical activity and unhealthy lifestyle resulted in increased cardiac issues in past decade but the advancements in bioengineering addressed the cause by introducing photo-voltaic cells in medical industry. Hence, the battery less pacemakers evolved making the pacemakers cost effective (no battery replacement after ten years). In this paper a comprehensive mathematical modelling of a system including Heart beat block, the sensor block, the pace maker block and the circulatory system block is done. And their outputs on scope is analyzed for adjusting the parameters of mathematically equations designed with generalized equations. The resulting waveform of the system is then compared to the standard QRS wave of a human heart’s ECG. Our proposed cardiac solar pacemaker, uses solar power as source for charging the battery of pacemaker, hence eliminating the procedure of battery replacement and re-surgery after five to ten years. We started the mathematical modelling by deriving transfer functions of the blocks designed in Simulink, evaluating the characteristic equations in Laplace domain and frequency domains. The constants of transfer functions are retrieved via mathematical techniques and then the resulting system response is analyzed by the fractional analysis of the transfer functions
Mathematical Modelling, Cardiac Pacemaker, Sensor Block
Adaptive Behavioral Biometrics in Evolving Environments
Behavioral biometrics, particularly keystroke dynamics, offer a promising layer of continuous and user-specific authentication to enhance conventional password-based security systems. However, real-world deployments face significant challenges due to the evolving nature of user behavior and the presence of unlabeled, high-velocity data streams. Traditional data stream mining approaches fall short, as they often rely on static, labelled, and balanced datasets. Moreover, Extreme Verification Latency (EVL) the delay between prediction and ground-truth feedback—further limits the adaptability of models in dynamic environments. To address these challenges, this paper proposes an adaptive learning framework for keystroke-based behavioral biometrics that leverages online semi-supervised learning, concept drift detection, and progressive model updates without requiring continuous labelled data. The framework dynamically adapts to changes in user behavior over time while maintaining real-time responsiveness and robustness against spoofing attempts. Experimental results demonstrate that our solution significantly improves authentication accuracy and resilience in evolving, latency-prone environments, offering a viable pathway for next-generation secure password systems
Concept Drift, Data Stream Mining, Behavioral Biometrics, Extreme Verification Latency
Bizora: Automated System for Dynamic Pricing and Inventory Management
Small and medium-sized enterprises (SMEs), particularly those in the restaurant industry, often face challenges with inventory control, pricing strategies, and staff coordination. To address these issues, we have developed an AI-powered SaaS platform that integrates automated inventory management, dynamic pricing, and advanced analytics. The system offers real-time stock monitoring, spoilage tracking, and low stock warnings to reduce waste. It also facilitates order management and the creation of promotions that attract customers. Dynamic pricing algorithms adjust product prices based on factors such as costs, profit margins, and market trends to boost competitiveness. Additionally, AI sales analytics combined with demand forecasting provide valuable business insights, while sentiment analysis and an AI chatbot system ensure exceptional customer interactions and a deeper understanding of customer feedback. The platform also supports sustainability through three Sustainable Development Goals: economic growth (SDG 8), innovation promotion (SDG 9), and responsible consumption and production (SDG 12). By leveraging AI and automation, the platform empowers SMEs to optimize operations, reduce expenses, and make better decisions through streamlined processes
Small And Medium-Sized Enterprises (SMES), Inventory Management
A Comprehensive Analysis of Acne Classification through Deep Learning Algorithms
Acne vulgaris is a prevalent and chronic inflammatory skin condition, categorized by blackheads, whiteheads, pimples, nodules, and cysts, affecting up to 80% of adolescents and often extending into adulthood. The psychological, social, and economic effect of acne is profound, contributing to issues such as low self esteem, depression, and in severe cases, suicidal thoughts. Traditional methods for assessing acne rely on the manual expertise of dermatologists, which, while valuable, can be time-consuming, prone to observer inconsistency, and potentially partial. Accurate and timely diagnosis is essential for effective treatment, making the need for automated acne classification systems increasingly apparent. Advances in artificial intelligence, particularly Convolutional Neural Networks (CNNs), present promising opportunities to enhance the accuracy and consistency of acne classification. This study develops acne types classification system using a proposed CNN model and evaluate its performance against well-established pretrained models, including ResNet50, MobileNetV3, and InceptionV3. The proposed model achieved an accuracy of 71%, outperforming ResNet50 (62%), MobileNetV3 (49%), and InceptionV3 (46%). Implementing such automated systems has the potential to enhance treatment outcomes, reduce the burden on healthcare professionals, and provide a more consistent and objective approach to acne diagnosis
Acne classification, Convolutional Neural Network, Deep Learning, Dermatology, Image recognition, ResNet 50, MobileNet V3, Inception V3
Eczema Classification Using Deep Learning Models
Skin disorders are serious health issues that impact people of all ages, and many of them still lack a proven treatment. The diagnosis and classification of skin diseases could be greatly enhanced by recent developments in artificial intelligence (AI), especially through deep learning techniques. Early diagnosis made possible by these methods can improve patient outcomes and survival rates. Deep learning models are more effective and possibly more accurate than traditional machine learning techniques since they require less operator involvement for feature extraction. Common inflammatory skin conditions like eczema are typified by dry, itchy, and irritated skin areas. Although the precise etiology of eczema is still unknown, a mix of environmental and genetic factors are thought to be responsible. Convolutional Neural Networks (CNNs) are a specific kind of artificial neural network made to interpret structured grid data, like pictures. It is an essential part of contemporary artificial intelligence (AI) and has transformed a number of domains, including computer vision, by making precise and effective picture processing possible. A specially created dataset of eczema photos with two distinct classes—each of which shows how the condition varies from the other—was used to train the machine. Using the suggested methodology, this study successfully classified two forms of eczema with a 97% accuracy rate. In addition to dermatologists and primary care professionals, scientists in the relevant field can utilize this method to reliably classify eczema
Classification, AI, Deep Learning pre-trained models, CNN, Eczema
AI Enabled SIEM with Self-Healing Capabilities
Cyber threats are revolutionizing and evolving day by day. As these threats are increasing rapidly, all an organization need is ideal solution to monitor their system and notify them by keeping system up to date and updating it. Traditional SIEM collects and analyzes log data from various devices and monitor whole system to detect potential security issues within the system. Traditional SIEM systems generates lot of alerts which are false positive which are disturbing for user. By using Machine Learning and behavioral analysis, AI system can monitor data in real time and detect anomalies within the system. AI-enabled SIEM systems can integrate with real time threat intelligence feeds to instantly detect new malware signatures. For many cyber attacks, AI-enabled SIEM systems would have been able to rapidly detect unusual behaviors such as communication between trusted systems and external, previously unknown IP addresses. If an unusual IP is detected or any other kind of suspicious activity is detected within the system, the system could automatically isolate the affected systems from the network. This paper explores analysis of AI enabled SIEM with self healing capabilities, why is AI enabled SIEM important, why traditional SIEM needs to be replaced, what is self healing, how efficient is self healing, examples of how in past AI enabled SIEM systems would be used to keep data safe and avoid organization from data breach
Security Information and Event Management (SIEM), Machine Learning (ML), Behavioral Analysis, Self Healing, Threat Detection.
Different Variants of Pandemic and Prevention Strategies: A Prioritizing Framework in Fuzzy Environment
In this trying time for the world battling different variants of the COVID-19 pandemic, government intervention strategies are being taken to limit the spread of infection. Closing educational institutes, stay-at-home orders, campaigns for emphasis on vaccination, the usage of medical masks, and frequently sanitizing hands etc. are the endeavors made by the authorities to decrease the number of cases in the country. In this regard, the contribution aims to help the decision-makers identify a potential prevention strategy based on public acceptance and intervention effectiveness. To achieve this objective, feasible judgments of professionals from three different sectors are brought together through meetings. Opinions, based on ten criteria, are recorded in linguistic form to prioritize six alternatives. The linguistic terms are then evaluated and manipulated by entailing triangular fuzzy numbers and a group multi-criteria decision making (GMCDM) approach. Consequently, through the ranking orders of defuzzified scores, an intuitive preference for compromise solutions is suggested. This comparative study is carried out through the technique for order of preference by similarity to ideal solution (TOPSIS), which produces the same preference for alternatives. In addition, this strategy can be discovered to be an effective strategy adopted by different countries in successfully decreasing the number of cases
Consistency Ratio, Decision Making COVID’19 Triangular Fuzzy Number Defuzzification
Transforming Energy Grids with Smart IoT Technology
Today the world is pressing on the generation of clean and green energy resources, exploring new techniques and technologies in this sector to boost cost saving and to lessen the reliance on conventional energy resources, which are posing severe environmental hazards. On the same lines, focusing on the development of smart grids for improving the transmission and distribution infrastructures that is to maximize utilization of clean and green energy and reap the maximum benefits of this energy. In this endeavor of smartening the grid, adoption of IoT plays a crucial role. This paper explains an IoT based smart grid solution developed for distribution sites. This solution is a device placed at the pole mounted distribution transformer to smarten the grid by inducing automation and enhanced communication between the different sites of the electrical grid, while introducing a control center. This device is capable of detailed monitoring of the pole mounted transformer’s functioning and implementing timely preventive and corrective actions to reduce failures and breakdowns at the site. With its built-in intelligence it takes prompt actions to safeguard the site equipment against probable failure and communicate to the control center SCADA for future actions and improvement plans. Hence improving the user experience and saving the cost of maintenance to the utility
IoT Based Smart Grid Solution for Pakistani Market
The rapid advancement of Artificial Intelligence (AI) has transformed multiple domains, including education. This project proposes the development of CheckMate – The AI-Powered Student Portal, an intelligent system designed to automate and streamline the student assignment evaluation process. By leveraging AI-driven techniques such as Natural Language Processing (NLP) and Machine Learning (ML), the system evaluates student assignments, provides automated grading, and generates personalized feedback. CheckMate addresses the inefficiencies of traditional manual grading, which is time-consuming, prone to subjectivity, and lacks scalability. The platform ensures consistent, unbiased, and data-driven assessment, allowing educators to focus on student engagement and learning. CheckMate is designed to handle high-volume grading efficiently
Artificial Intelligence (AI). Manual Grading
AI-Powered Botnet Detection and Mitigation: Advancing Network Security Through ML and Federated Intelligence
Botnets have continued to grow as sophisticated cyber threats, deploying decentralized architectures and encrypted command-and-control (C&C) channels, as well as adaptive evasion techniques. Signature and heuristic methodologies of detection have always been reluctant to counter next-gen botnets, thus calling for the integration of AI-driven methodologies. The study discusses state-of-the-art AI techniques: deep learning (DL), graph neural networks (GNNs), and reinforcement learning (RL) concerning proactive detection and mitigation of botnets. Also, an ensemble learning framework is proposed, combining Federated learning (FL) which provides an interesting, decentralized approach whereby AI model training occurs in a distributed manner, remaining data-private. The research explores adversarial AI threats and countermeasures such as adversarial training and robust feature selection to increase model resilience against adversarial botnets. The experimental findings confirm that the AI-based detection systems successfully identify polymorphic botnets with greater precision and recall, evaluated over benchmark datasets and real-world network traffic. The current study extends the advancement of AI-based cybersecurity by coupling self-learning algorithms, adaptive defense strategies, and blockchain-based trust mechanisms for resilient botnet mitigation in dynamic network environments
C&C evasion, Botnet Detection, AIDriven Cybersecurity, Deep Learning.
AI-Powered Legal Assistance in Pakistani Law: A RAG-Based Framework for Context-Aware and Reliable Legal Answer
The complexity of legal frameworks and the limited availability of legal material for the population and legal experts pose substantial obstacles in working through Pakistan's law. This paper presents a framework that makes use of pre-trained large language models (LLMs) and Retrieval Augmented Generation (RAG) to provide scalable, accurate, and context-aware legal answers for legal questions based on Pakistani law. Our system utilizes a hybrid search method blending vector-based semantic search with keyword-based retrieval for searching key Pakistani legal documents in a vector database, such as the Code of Criminal Procedure 1898, Pakistan Penal Code, Muslim Family Laws Ordinance, and West Pakistan Family Court Act. Our system overcomes challenges of fact hallucination and subtle contextual understanding by injecting domain-specific knowledge of law into the LLM using RAG. We assess the framework's performance to capture pertinent legal provisions, produce precise explanations, and yield actionable insights for various legal queries. Experiments show that the hybrid search approach surpasses single-modality retrieval by combining semantic comprehension with accurate keyword matching to enhance precision and reliability This research showcases the promise of AI-powered legal aid tools to democratize access to justice in Pakistan, empowering citizens, legal experts, and policymakers with a fast, affordable means of navigating complex legal environments
Legal AI, Pretrained LLM, Retrieval Augmented Generation (RAG), Hybrid Search, Vector-based Semantic Search, Keyword-based Retrieval
CricketEye: A Deep Learning Approach for Automated Umpiring in Grassroots CricketCricketEye: A Deep Learning Approach for Automated Umpiring in Grassroots Cricket
Grassroots cricket lacks accurate umpiring due to human errors and the high cost of advanced technology. Existing solutions like Hawk-Eye are expensive and require specialized hardware, making them inaccessible. Cricket Eye offers an affordable AI-powered system for real-time no ball, wide ball, stumping, and LBW detection, ensuring fair play without costly infrastructure. Cricket Eye aims to provide accurate, affordable, and real-time AI-powered umpiring for grassroots cricket. It eliminates human errors in key decisions without expensive hardware, ensuring fair play and easy adoption in local tournaments. CricketEye is an AI-powered application designed to revolutionize street cricket by providing advanced umpiring assistance through computer vision and deep learning techniques. Our system utilizes pose estimation and object detection models to analyze key in-game events, including LBW decisions, wide ball detection, no-ball identification, and stump assessments. By leveraging both first umpire and third umpire perspectives, CricketEye ensures more accurate and fair decision-making in cricket matches. The primary focus of our application is street cricket, where the absence of professional umpiring often leads to disputes. Addressing this challenge, CricketEye brings cutting-edge AI-driven analysis to grassroots cricket, enhancing the playing experience and fostering a fairer, more engaging environment for players
AI-Powered Decision Making, Real-Time Analysis, Cost-Effective Solutions, Easy Integration
Two-Stage AI Pipeline for Liver Disease Detection and Classification Using Liver Function Tests
This paper presents a novel two-stage artificial intelligence approach for liver disease detection and classification using standard Liver Function Test (LFT) parameters. While numerous studies have addressed binary classification of liver disease presence, few have attempted to identify specific liver conditions using only LFT data. Our approach combines a Neural Network for initial disease detection (95.9% accuracy) with a Support Vector Machine for specific disease classification (79% accuracy), enabling identification of hepatitis, fibrosis, and cirrhosis. This practical approach utilizes commonly available blood test parameters, ensuring its suitability for healthcare settings with limited resources while maintaining strong diagnostic accuracy
Liver disease classification, Machine Learning, LFT parameters, SVM, dual-stage mode
Rainfall Prediction in Karachi, Pakistan: A Machine Learning & Hybrid (ML-HMWA), Approach for Climate Resilience
This study focuses on developing better rainfall forecast models because they support successful water resource methods and agricultural techniques and disaster response during emergencies including floods and dry spells. Reliable rainfall forecasting stands vital for sustainable growth since it reduces the harmful consequences of severe weather conditions such as flooding. This causes extensive loss of life along with health hazards and creates economic instability. Traditional forecasting models find it difficult to produce accurate predictions because rainfall patterns display both unpredictable behaviour and complexity. This study explores the use of machine learning models to develop reliable rainfall prediction systems based on meteorological data sourced from the Visual Crossing platform. The dataset comprises 4,018 records collected from the Defence Housing Authority (DHA) region in Pakistan, spanning the years 2011 to 2023, and includes 29 features 8 categorical and 21 numerical. The data preprocessing steps included eliminating visibility along with solar radiation and UV index because these attributes were not essential for improved dataset relevance and quality.These findings highlight the effectiveness of both Ridge Regression and hybrid ensemble strategies in delivering reliable rainfall forecasts. The study underscores the value of comprehensive preprocessing and hybrid modeling, laying a strong foundation for enhancing weather prediction systems in urban areas like DHA and other similar environments.
Forecasting, Machine learning, Regression, Ridge Regressor
Revolutionizing Brand Promotion Via Predictive Analytics & Forecasting
Introducing an innovative brand promotion platform, RBP, that seamlessly merges predictive analysis and intuitive e-commerce functionalities. This platform empowers local businesses with actionable insights derived from predictive analytics and forecasting, enabling strategic decision-making regarding product offerings and marketing strategies, while ensuring customers enjoy a frictionless shopping experience through a dedicated and user-friendly website. User-centered design principles guided the intuitive interface, featuring robust search capabilities and responsive web design for seamless accessibility. Advanced data analytics and predictive analysis drive personalized user experiences and optimize product recommendations, real-time communication tools, such as video calls, enhance transactional trust and user engagement. Feedback mechanisms and social sharing functionalities encourage community interaction and brand advocacy, fostering enduring customer loyalty and reducing cart abandonment rates
E-commerce Platforms Predictive Analytics, Market Trends
Immersive VR Training for Fire Safety and Crisis Response: A Risk-Free Learning Model
The current practice of crisis response training depends on conventional approaches which require substantial maintenance spending and create logistical risks and health safety concerns thus reducing the overall effectiveness during emergency preparation. This research investigates the development of a Crisis VR Simulator because it represents a virtual reality training platform which improves crisis response readiness by providing virtual interactive simulations. Real-world emergency situations such as fire incidents along with evacuation procedures and fire safety instructions are accurately replicated by this system inside a safe but realistic training environment. The customizable crisis scenarios addressed by the simulator enable service to diverse groups of users that span between students and professionals and emergency response teams. Virtual reality training provides substantial benefits by enhancing emergency preparedness as well as situational awareness and decision-making proficiency among trainees. The financial efficiency and secure conditions and flexible nature of VR-based instructions make this teaching approach a valuable replacement for traditional training approaches which propels emergency response education forward
Virtual Reality Training, Crisis Preparedness, Fire Safety, Emergency Response
ScriptoAI: Transforming Text, Images & Speech into Seamless Digital Handwriting
Inscriptive is an AI-powered web-based platform designed to convert typed text, PDFs, and assignments into lifelike handwritten styles. With a diverse collection of 30 to 40 handwriting styles, customization options for fonts and page colors, and advanced text processing capabilities, it serves as a comprehensive solution for students, educators, and professionals. A key feature of inscriptive is its AI-driven Optical Character Recognition (OCR) technology, which enables users to extract text from images, scanned documents, and handwritten notes with high accuracy. By leveraging machine learning algorithms, the OCR system enhances image quality, detects textual patterns, and converts them into an editable digital format, allowing seamless content modification and handwriting transformation. Additionally, Inscriptify integrates AI-based voice-to-text conversion, allowing users to dictate content that is automatically transcribed into digital text. This feature is particularly useful for students who prefer speaking their notes instead of typing, professionals needing hands-free document creation, and individuals requiring speech-to-text assistance for accessibility. Through AI-powered processing, rigorous research, iterative development, and continuous optimization, inscriptive aims to provide a user-friendly, scalable, and efficient solution that streamlines creative workflows
Image-to-Text Extraction , AI-based Content Customization, OCR-Based Document Processing
The golden hour is a critical period for saving lives during medical emergencies, yet it is often lost due to delays in transit and inefficient documentation at hospital emergency departments. These delays significantly reduce patient survival chances, as treatment often begins later than optimal. This paper proposes an advanced solution to bridge the communication gap between ambulances and hospital staff, optimizing the golden hour by integrating cutting-edge technologies. The proposed system upgrades ambulances with ICU-like capabilities and predictive analytics using machine learning. Equipped with a cardiac monitor to continuously track vital parameters and an HMI (Human Machine Interface) to enter patient’s information and initial conditions via touch or voice input, the system transmits real-time data to hospital Emergency Departments. This ensures that medical teams are well prepared before the patient’s arrival. Additionally, machine-learning al- gorithms analyze the obtained data and vitals in real time, predicting potential diseases and injuries. By leveraging real- time data sharing and predictive analytics, the system enhances emergency response efficiency, significantly improving patient outcomes
Artificial Intelligence (AI), Machine Learning (ML)
The rapid advancement of healthcare technology has led to the development of smart patient monitoring systems, utilizing the Internet of Things (IoT) to enhance real-time health tracking. These systems integrate biosensors, artificial intelligence (AI), cloud computing, and big data analytics to improve patient care, particularly for individuals with chronic conditions and critical care needs. IoT-enabled monitoring devices continuously measure vital health parameters, such as heart rate, blood pressure, oxygen saturation, and electrocardiogram (ECG) readings, transmitting real-time data to cloud-based platforms. AI-driven algorithms analyze this data to identify patterns, detect anomalies, and support clinical decision-making, enabling early intervention and personalized treatment plans. Key components of these systems include wearable and implantable biosensors, AI and machine learning integration, edge computing for real-time processing, 5G and cloud connectivity, and blockchain technology for data security. These innovations facilitate applications such as remote patient monitoring (RPM), ICU monitoring, elderly care, and maternal and neonatal health tracking, significantly enhancing healthcare accessibility and efficiency. Despite their benefits, challenges such as data security concerns, interoperability issues, high implementation costs, and regulatory compliance requirements hinder widespread adoption
Smart Patient Monitoring, Internet of Things (Iot).
An Analysis of Digital Emotional Intelligence: Can AI Accurately Interpret Human Emotions?
The way individuals look for emotional support has changed as a result of the growing number of AI-powered companions in digital environments; the emergence of AI-powered companions has revolutionized online communication by providing emotional support via virtual assistants and chatbots. Although these AI systems are intended to identify and react to emotions, but to the claims that these systems are capable of accurately interpreting and reacting to human emotions, questions remain about their accuracy, moral consequences, and psychological effects. The capacity of AI companions like Replika, Woebot, and Xiaoice to identify and comprehend human emotions is investigated in this study. The study employs a qualitative research technique, analyzing online user experiences through content analysis, examining AI emotional models through a case study, and thematically analyzing conversations about ethics and governance.. The results will highlight the need for stronger ethical frameworks and more dependable emotional identification algorithms, adding to the larger conversation on responsible AI research. More insight into how AI companions might support openness, equality, and confidence in human-AI interactions while promoting emotional well-being is the ultimate objective of this research
AI companions, AI Emotional Intelligence, Human-AI Connection, Digital Well-being, and Ethical AI
Enhancing Trust and Transparency in Online Marketplaces Using Generative AI for Product Validation and Verification
The rise of online classified platforms has made buying and selling assets more convenient but has also introduced challenges related to trust, product authenticity, and misleading listings. This paper introduces SnapTrade, a next-generation classified platform that leverages Generative AI (GenAI) to enhance transparency and reliability in digital marketplaces. Unlike traditional platforms where buyers often receive products that do not match their descriptions, SnapTrade integrates AI-powered image analysis and product validation to ensure authenticity. Our empirical analysis reveals significant discrepancies between seller-provided ratings and AI-generated assessments (average difference of 1.57 points), with 23% of listings showing critical rating differences exceeding 3 points. The similarity analysis between user and AI-generated descriptions demonstrates that most listings (50%) have low content match scores (0.2-0.4), indicating AI's ability to identify missing product information. Our cluster analysis further isolates high-risk transaction patterns, representing 27% of listings. By integrating Generative AI for rating verification and description enhancement, SnapTrade addresses core challenges of digital classifieds, providing a methodological framework for improving transparency and user trust in online marketplaces
Generative AI Product Validation, AI-Based Product Rating, Image-Based Authentication, Automated Ad Analysis
Number Plate Detection and Registration Data Integration
This project focuses on the development of a real-time number plate detection and registration data integration system using computer vision and deep learning techniques. The system is designed to capture live video streams, detect vehicle number plates, extract alphanumeric characters using Optical Character Recognition (OCR), and retrieve vehicle registration details from a pre-existing database. The system operates in multiple stages: 1. Video Capture: A live feed is obtained from surveillance cameras or other video input sources. 2. Number Plate Detection: Using deep learning-based object detection models such as YOLO (You Only Look Once), the system identifies and extracts the region of interest (ROI) containing the number plate. 3. Character Recognition: OCR techniques, such as Tesseract OCR or deep learning-based text recognition models, are applied to extract alphanumeric characters from the detected number plate. 4. Database Integration: The extracted number is matched against a vehicle registration database to retrieve relevant details, such as the owner's information, vehicle model, registration validity, and law enforcement records. The primary objective of this system is to enhance security, traffic monitoring, and law enforcement operations. By automating vehicle identification, it can assist in detecting stolen vehicles, traffic violations, unauthorized access, and criminal activities
Licence Plate Recognition System, Data Integration
A Holistic Framework for Detecting and Mitigating Fileless Malware in Operational Technology Environments
Fileless malware exists as a highly intricate and dominant security threat that shows increasing presence most strongly in Operational Technology environments. These malicious intrusions work in system memory space exclusively so they do not produce disk-based artifacts that signature-based detection methods would recognize. This document explores the essential challenge of discovering and reducing fileless malware within essential Operational Technology infrastructure that powers vital systems such as power networks and water treatment plants and industrial production facilities. Modern detection methods should focus on memory forensics along with behavioural analytics and endpoint detection response (EDR) and network traffic analysis since file-based artifacts cannot be relied upon. A new framework specifically created for operating technology environments integrates advanced detection methods which target the needs of limited resources alongside real-time operational integrity in legacy systems. The framework uses machine learning algorithms and threat intelligence integration along with anomaly detection capabilities to identify indicators of compromise (IOCs) in advance. The solution includes multiple strong protection measures which include application authorisation together with zero trust architecture and document macro restriction methods for securing users against attacks
Fileless Malware Operational Technology (OT), Security Memory Forensics, Behavioral Analytics, Endpoint Detection and Response (EDR)
IoT Sensor Networks Optimization for Ultra-Low Latency in 6G URLLC Environment
Internet of Things (IoT) is rapidly expanding with billions of connected devices. The demand for wireless sensor connections that work well and are effective has increased as a result of this growth. Within the framework of 6G Ultra-Reliable Low Latency Communications (URLLC) standards, the goal of this study is to look at how to improve wireless sensor networks (WSNs), with a focus on Internet of Things applications. Leveraging advanced 6G features like ultra-low latency and high reliability, our study addresses real-time IoT demands in smart infrastructure, healthcare, and automation. We propose an enhanced hybrid gray wolf optimization (IHGWO) method to optimize WSN performance by reducing latency and improving reliability. This paper demonstrates that our method significantly reduces connection delay in high speed 6G URLLC scenarios. This makes communication in IoT networks faster and more responsive
Internet of Things, Wireless Sensor Networks, 6G, URLLC, Grey Wolf Optimization
Vision Transformers Deep Learning models for Image Processing. A Survey
Transformers have emerged as a novel and transformative technology in deep learning, providing an alternative to image processing which was historically based on Convolutional Neural Network. The present study demonstrates the adaptability of Vision Transformers from natural Language processing to a variety of image analysis applications. VIT incorporates elements from the architecture, such as the self-attention mechanism, that permits the model to capture global dependencies in the images. The performance of ViT is also evaluated compared to CNN. The researchers emphasize the advantages that ViT have concerning the processing of complex visual information and the disadvantages they face because of their computational and data requirements. The paper also briefly considers the wide spectrum of ViT applications in medical imaging, object detection, and others. Thus, the paper discusses the current trends and expected developments and states that Vision Transformers may be the key to the intensification of image processing technologies
Vision Transformers, Deep Learning Image Processing, Architectural Innovations, Performance Analysis
Hybrid Power Generation System through Renewable Energy Sources Using Machine Learning Methods on an IoT-Based Platform
This research explores the benefits of integrating intermittent energy sources with energy storage for the electrical system. A hybrid energy system, combining multiple sources, has been established at an Electrical Power System Lab in Karachi, Pakistan. The study introduces a monitoring and control mechanism using the ESP8266 microcontroller and Raspberry Pi, enabling both manual and remote system operation. Increasing the efficiency of the system by applying machine learning methods Central to this work is the application of the Internet of Things (IoT) for tracking and managing the hybrid system. The proposed solution is efficient, cost-effective, and user-friendly, emphasizing the seamless transition between primary energy sources like wind and solar power
Wind Energy, Wind Turbines, Fossil Fuels, Energy Utilization