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22nd International learning and Technology conference, L and T 2025

作者： Abas, Ashardi Ibrahim, Mohd Mokhzani Bin Azmi, N.H. University Pendidikan Sultan Idris Faculty of Computing Meta Technology Perak Malaysia University Pendidikan Sultan Idris Faculty of Science and Mathematics Perak Malaysia

ISBN: (纸本)9798331532215

The persistent skills mismatch between higher education outcomes and labour market needs presents a significant challenge for graduate employability, particularly in fast-evolving sectors such as artificial intelligence (AI) and data science. This paper introduces a data-driven internship framework designed to bridge this gap by optimising student placements, providing real-time feedback, and ensuring academic curricula remain aligned with labour market demands. The framework integrates three systems: the Student Industry Placement System (SIPS), which uses machine learning to match students with relevant internships;the Intern learning Management System (ILMS), which tracks skill development through continuous feedback loops;and the Industry Profiling System (IPS), which leverages predictive analytics to provide real-time labour market insights. The framework was evaluated through a mixed-methods approach, incorporating pilot program data from five universities and 15 industry partners and simulated data based on historical internship trends. The SIPS algorithm achieved a placement accuracy of 89%, significantly improving over traditional methods (60-65%). 78% of students received continuous feedback through the ILMS, resulting in measurable improvements in technical skills (30% increase in coding proficiency) and problem-solving abilities (25% improvement). The IPS enabled universities to update 15% of their curricula in response to real-time labour market data, particularly in high-growth sectors such as AI and digital transformation. 68% of the students secured full-time employment within six months of graduation, with 65% in roles directly aligned with their internships. These findings demonstrate the framework's scalability and potential for reducing skills mismatch, making it a viable model for other emerging economies. © 2025 ieee.

关键词： Commerce

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An AI-driven Approach for Tailoring Java Programming Instructions 3

An AI-driven Approach for Tailoring Java Programming Instruc...

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3rd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331527549

AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java ***'s framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes. © 2025 ieee.

关键词： Java programming language

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Machine learning Algorithm for Sensitive data Classification on Cloud Environment

Machine Learning Algorithm for Sensitive Data Classification...

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2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Osari, Ranjeet Singhai, Rahul Devi Ahilya Vishwavidyalaya Computer Science SCSIT M.P. Indore India Devi Ahilya University Computer Science IIPS M.P. Indore India

ISBN: (纸本)9798331512965

This electronic Digital information, computer systems, networks, and data are highly in need of protection from damage, unauthorized access, and internal and external threats. Cyber security involves the implementation of strong procedures to protect digital resources by ensuring confidentiality, integrity, and availability. In the dynamic landscape of cyberspace, Cyber security is the umbrella term for tactics, tools, and procedures used to fend off a wide variety of threats, such as ransomware, phishing, malware, and other unwanted activity. This field continually evolves to counter emerging risks and vulnerabilities, requiring a proactive approach to identify, assess, and moderate potential security breaches. Cyber security is fundamental in preserving trust and reliability in digital interactions, serving as a critical component in our interconnected and technology-driven world. Here we focused on the large amount of data set to identify the level of confidentiality required and categories them using machine learning approaches. The results will be carryied forward for the high computational encryption and decryption to provide high security and provide access as per the need of the users. © 2025 ieee.

关键词： Cloud security

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Deep learning Framework with Explainable AI for Accurate and Interpretable Brain Tumor Segmentation 1

Deep Learning Framework with Explainable AI for Accurate and...

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1st International conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Dushyantha, N.D. Naik, Kavya R. Chaithanya, H.G. Amrutha, L. Dinesh, C. Hkbk College of Engineering Bangalore India

ISBN: (纸本)9798331529246

Brain tumor detection by MRI scan is an imperative medical concern that necessitates state-of-the-art techniques for accurate and timely detection. This paper proposes an approach integrating Explainable AI, Federated learning, and deep learning to improve accuracy, privacy, and trust. Federated learning allows collaborative learning for the model without accessing each other's data. Explanation using SHAP and LIME techniques results in interpretable predictions from an AI system, enabling greater clinician trust in these AI systems. In accuracy, the proposed system could achieve an accuracy of 96.8%, with sensitivity at 96.5%, specificity at 96.4%, and an F1-score at 96.6% more than the traditional CNN model. The future direction in this approach will be enhanced in interpretability, robustness, scalability, and in dataset diversity to further boost generalization. This research methodology is bridging AI research to clinical application that offers reliable diagnoses through practices of privacy preservation, moves healthcare through innovation, and evokes trust in AI-driven solutions. © 2025 ieee.

关键词： Federated learning

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Artificial Intelligence for Automated Support in Educational systems

Artificial Intelligence for Automated Support in Educational...

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2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthik, R. Maha Vishnu, S. Saran, R. Shashidharan, S. K.S.Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Educational institutions frequently suffer from inefficiencies in decision-making, interdepartmental communication, and administrative procedures. In order to optimize resource allocation, automate procurement workflows, and streamline helpdesk operations, this research study proposes an AI-powered platform that combines machine learning (ML) with Natural Language Processing (NLP). The platform acts as a central location for handling enquiries, automating repetitive processes, and enhancing decision-making by utilizing insights derived from data. The proposed system improves query response, fosters interdepartmental communication, and provides predictive analytics to predict resource requirements by continuously learning from user interactions. Through process automation, real-time support, and smooth departmental communication, this AI-driven system aims to improve institutional efficiency, reduce delays, and raise employee satisfaction. © 2025 ieee.

关键词： Efficiency

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8th International conference on Intelligent Transport systems, INTSYS 2024

8th International Conference on Intelligent Transport System...

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8th International conference on Intelligent Transport systems, INTSYS 2024

ISBN: (纸本)9783031863691

The proceedings contain 25 papers. The special focus in this conference is on Intelligent Transport systems. The topics include: Interoperable Traceability in Supply Chains: A Use Case in Agritech;treemob: Expressive Mobility data Representation Through Tree-Based Structures;From GPS Traces to Individual Emission Exposure: A data-driven Four-Step Process;capacity Vehicle Routing Problem with Time Windows: Simulation Tool for Footprint Network Design;chain of Portable Health Folders: A Systematic Literature Review;integrating Metro Infrastructure in Circular Food Supply Chains: A Model for Decentralized Quito’s Food Bank Network Redesign;An AutoML Approach for Bike Demand Forecasting and Redistribution;adaptive Stop-Skipping Scheduling Approach Using Reinforcement learning;machine learning Approach for Labeling Undetected Planned Trips in Public Transport Operators;reinforcement learning Algorithms with Graph Convolution Networks for Traffic Signal control;optimizing Intelligent Transportation systems with Multi-agent Reinforcement learning: A Socio-economic Impact Assessment;a Mobile Application to Secure Pedestrians Interacting with Automated Vehicles;A Simulation-Based Security Benchmarking Approach for Assessing Cooperative Driving Automation (CDA) Applications;wireless Interference and Regulatory Frameworks for Frequency Allocation in V2X Communication systems;runtime Norms Regulation Framework for Drones’ Smart Cities Applications;a data-driven Integrated Framework for Virtual Testing of Autonomous Vehicles in Mixed Traffic Scenarios;federated learning for Lane-Change Prediction;Evaluating Traffic control Strategies for Autonomous Shuttle in Different AV Penetration, Using SUMO Traffic Simulation;Adaptive Video Bitrate Allocation for Remotely Operated Vehicles (ROV);experimental Evaluation of Road-Crossing Decisions by Autonomous Wheelchairs Against Environmental Factors;impact of Network Delays on Edge-Assisted Platooning systems in 5G Networks: Addressing Lat

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Traffic and Pollution control Using IoT-Enabled Smart Routing Algorithm 2

Traffic and Pollution Control Using IoT-Enabled Smart Routin...

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2nd International conference on Machine learning and Autonomous systems, ICMLAS 2025

作者： Hammed, S Shahul Preethi, C. Haripriya, K. Pavalarajan, S. Gowtham, M.R. Krithick, N. Akshaay PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331505745

Urbanization has led to increased traffic congestion and air pollution, primarily from vehicle emissions, posing risks to public health and the environment. Existing traffic management systems are inefficient in integrating real-time pollution data, leading to reactive control measures. This project proposes an IoT-based smart traffic management system that integrates real-time air quality monitoring with dynamic traffic control. IoT sensors collect pollution and vehicle data, which is processed using cloud computing and machine learning to optimize traffic flow and reduce emissions. AI techniques provide transparency, helping city authorities understand and trust the system's decisions. The system also offers predictive analytics for proactive pollution management and a user-friendly dashboard for real-time visualization. The solution aims to reduce urban emissions, and enhance traffic efficiency, offering a sustainable approach for modern cities. © 2025 ieee.

关键词： Traffic congestion

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DAMPNN: Dynamic Adaptive Message Passing Neural Network for Industrial Soft Sensor

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1190-1199页

作者： Yan, Feng Yang, Chunjie Kong, Liyuan Yang, Chong Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310000 Peoples R China

data-driven soft sensor modeling has received much attention in industrial processes. Most of the existing soft sensor approaches have not considered the complex dynamic spatial coupling characteristics between process variables. Recently, graph-based soft sensor modeling methods have started to show powerful expressive ability in capturing relational dependencies. However, existing graph-based soft sensor models still confront several limitations: 1) these models usually depend on predefined graph structures or local dynamic graph;2) they fail to study dynamic message passing mechanism;3) they have not considered the importance of extracted features from the entire graph. To handle these problems, in this study, we develop a dynamic adaptive message passing neural network (DAMPNN) for industrial soft sensor. The main novelty lies in an integration of our designed three modules into DAMPNN. First, we propose an adaptive graph learning module to automatically capture mutual relationships between process variables instead of a predefined adjacency matrix. Then, we design a dynamic message passing module to aggregate neighborhood information and update graph representation. In addition, a dual self-attention module is embedded into the top layer to concurrently emphasize informative features and time points for fine-grained soft sensor modeling. Finally, comprehensive comparison results on two real-world industrial cases demonstrate that DAMPNN outperforms the existing graph-based soft sensor methods.

关键词： Soft sensors Message passing Couplings Adaptation models Feature extraction Adaptive systems Training Temperature sensors Microwave integrated circuits Market research Dual self-attention dynamic message passing graph neural network (GNN) soft sensor

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Comparative Analysis of Federated learning Techniques in Big data-driven Intelligent Transportation systems 8

Comparative Analysis of Federated Learning Techniques in Big...

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8th International conference on data Science and Machine learning Applications, CDMA 2025

作者： Kaleem, Sarah Babar, Muhammad Qureshi, Basit Ei-Affendi, Mohammed Koubaa, Anis Prince Sultan University Riyadh Saudi Arabia lqra University EIAS Lab Islamabad Pakistan CClS Prince Sultan University Robotics and Internet of Things Lab Riyadh Saudi Arabia CCIS Prince Sultan University KSA RIOTU Lab Saudi Arabia CCIS Prince Sultan University KSA EIAS Data Science Lab Saudi Arabia

ISBN: (纸本)9798331539696

The advancement of Intelligent Transportation systems (ITS) represents a pivotal development in IoT technology, improving traffic management, reducing accidents, and optimizing transport networks. ITS generates vast data streams from interconnected devices, offering valuable insights into travel and road conditions. However, leveraging this extensive data poses significant performance, privacy, and scalability challenges. In this context, Federated learning (FL) emerges as a crucial solution. This paper contributes a comprehensive comparative analysis of various FL algorithms in processing ITS Big data, focusing on their effectiveness in managing and analyzing data while preserving user privacy and ensuring scalability. Using a dataset from Udacity, provided by Roboflow, consisting of 15,000 high-quality images across 11 categories formatted for YOLO, we conduct a comparative evaluation of FedSGD, FedAvg, and FedProx. Through this comparative evaluation, we demonstrate the potential of FL to manage and analyze ITS data while preserving user privacy and ensuring scalability. Our results highlight that FedAvg achieves the highest accuracy at 79.01%, outperforming FedSGD at 78.10% and FedProx at 78.61%. © 2025 ieee.

关键词： Federated learning

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A Review on the Applications of Machine learning and Deep learning Techniques for Skin Cancer Detection 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine learning (ML) and Deep learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 ieee.

关键词： Adversarial machine learning

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