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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Dhawde, Navya Chakraborty, Nilotpal Husain Baig, Mirza Galib Anwarul Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India Indian Institute of Technology School of Mathematical and Statistical Sciences Mandi India

ISBN: (纸本)9798331531195

Unmanned aerial vehicles (UAVs), or drones, are transforming surveillance strategies in various fields, including border security and wildlife conservation. Their ability to monitor large, challenging areas in real time has established them as a valuable tool, particularly in scenarios where traditional surveillance methods face operational and logistical limitations. This research focuses on optimizing drone-based surveillance along the International borders, and in wildlife conservation areas, where challenges like illegal crossings, cattle smuggling, and poaching present serious security and economic threats. Leveraging UAVs provides a cost-effective, flexible, and highly deployable solution that overcomes the limitations of static surveillance systems and limited human patrols. We propose an optimized drone-based system designed to enhance operational efficiency, coverage, and adaptability in these complex environments, with potential applications in various sectors requiring comprehensive, real-time surveillance. We propose modeling the problem using the Eternal Vertex Cover problem, a dynamic extension of the classical Vertex Cover problem. Given the NP-hard nature of determining this cover number, our research explores heuristic and algorithmic approaches to achieve an efficient and scalable solution. We also propose an intelligent charging mechanism for the drones for their continuous operation. All proposed approaches are evaluated using randomly generated datasets as long as benchmark datasets. © 2025 IEEE.

关键词： Drones

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Data Processing and Pattern Recognition for Electronic Nose 3

Data Processing and Pattern Recognition for Electronic Nose

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Raymond, David Chaudhri, Shivnath Shaikh, Kahkasha Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Department of Computer Science and Medical Engineering Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Depertment of Computer Science and Design Wardha India

ISBN: (纸本)9798331509675

Electronic nose (e-nose) technology has become a powerful tool for identifying and evaluating complex scents in a variety of contexts, such as environmental monitoring, medical diagnostics, and food quality control. This review study looks at how important pattern recognition and data processing are to improving e-nose system performance. This study covered challenges, including data complexity, cross-sensitivity and interference, sensor drift, and a lack of standardization. The article covers several future directions, such as the use of artificial intelligence (AI) and machine learning to advance pattern recognition and predictive analytics, integration with the Internet of Things (IoT) and big data for real-time monitoring, and advancements in sensor technology for improved sensitivity and *** research work is underway towards the development of low power flexible sensors, improved multi sensor fusion Methodologies, smart adaptive algorithms, Application for personalized health monitoring and diagnostics as well to make it green technologies. To move forward with emerging technologies and for e-nose technology to be applicable, it will have to overcome these weaknesses. © 2025 IEEE.

关键词： Electronic nose

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AI-Driven Transcription and Analysis for Medical Conversations 2

AI-Driven Transcription and Analysis for Medical Conversatio...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Pisal, Tejas Hajare, Rasika Verma, Prateek Gote, Pradnyawant M. Jiet, Mosses Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare communication. The system is designed to transcribe audio files of doctor-patient conversations accurately and generate contextually relevant responses using AI. A user-friendly interface, developed with Tkinter, allows seamless uploading and processing of these audio files. The integration of these technologies aims to enhance the efficiency and effectiveness of automated response systems, thereby improving patient-provider communication. The system's capabilities were evaluated using real-world healthcare datasets, demonstrating its potential to streamline the workflow from audio input to meaningful textual output. This work not only showcases the practical application of AI in healthcare but also lays the groundwork for future innovations in automated medical communication systems. © 2025 IEEE.

关键词： Natural language processing systems

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Modified simulated annealing for university teacher course assignment considering socio-cultural constraints

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International Journal of information technology (Singapore) 2025年第3期17卷 1533-1549页

作者： Brahimi, Samiha Sabba, Sara Elhussein, Mariam Alqahtani, Mohammed Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of New Technologies of Information and Communication Laboratory of Data Science and Artificial Intelligence (LISIA) Abdelhamid Mahri Constantine-2 University Constantine 25016 Algeria Department of Mathematics and Computing Mount Royal University Calgary AB Canada

This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between students and faculty. To tackle this challenge, a combinatorial optimization model is proposed, which includes a binary representation of the problem, constraints reflecting gender segregation, and an objective function designed to maximize faculty satisfaction. The model employs a Simulated Annealing (SA) algorithm, supplemented with two neighborhood search heuristics (H1 and H2), to explore diverse combinations. Additionally, a greedy randomized adaptive search procedure Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is implemented for comparative analysis, utilizing heuristic 1 for local search. Through the application of simulated annealing alongside both heuristics (SA_H1_H2), the optimal solution is achieved, resulting in a notable equilibrium of courses among faculty members. Objective 1 demonstrates the standardized deviation in workload distribution among faculty members, yielding a deviation value of 0.08. This study contributes to the field by proposing a novel approach to course assignment that explicitly addresses the gender-based segregation prevalent in Saudi universities. By integrating socio-cultural constraints into the optimization model and employing advanced algorithms, the study offers a pioneering solution to a previously unexplored problem domain. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： GRASP Mathematical model Operations Research Optimization Simulated annealing Teacher_Course assignment

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Intelligent Elderly Care: Practicing Ambiguity Neutrosophic Theory for Optimization Contemporary Machine Learning Techniques in Elderly Care

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Neutrosophic Sets and systems 2025年 79卷 436-453页

作者： Ismail, Mahmoud M. Metwaly, Ahmed A. ElKomy, Osama Al-Ghamry, Alaa Mohamed, Mona Decision Support Department Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Higher Technological Institute 10th of Ramadan City44629 Egypt

One of the biggest challenges facing healthcare systems throughout the world is the aging population. The growing number of elderly citizens in need of specialized care is severely straining the available resources and care methods. It is sometimes difficult for traditional care techniques to address the complex and varied requirements of this expanding population. To fix these challenges, inclusion of contemporary technology is imperative and pragmatic solutions such as Internet of Thing (IoT), cloud computing (CC), and artificial intelligence (AI) techniques such as machine learning (ML), deep learning (DL). Such AI has potential role to make elderly care services (ECSs) to be intelligent ECSs (IECSs) through providing proactivity and earlier detection based on smart IoT sensors. Therefore, this study seeks to achieve two objectives. Firstly, leveraging the capabilities of ML techniques to revolutionize care delivery to be intelligent, optimize resource allocation, and proactive. Secondly, evaluating the robustness of utilized ML techniques in serving study's objectives. Accordingly, utilized ML Techniques consider alternatives (MLTs) that evaluate based on CRiteria Importance Through Inter-criteria Correlation (CRITIC) to obtain weights for criteria which alternatives evaluated based on. These weights are leveraging in Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank MLTs alternatives. For bolstering the evaluation process, we are integrating uncertainty theory of Probabilistic Simplified Neutrosophic Set (PSNS) which effectively captures the inherent uncertainty and imprecision. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Adaptive Queue Dynamics (AQD): A Novel Scheduling Algorithm for Response Time Minimization in Baseband Unit (BBU) systems 2nd

Adaptive Queue Dynamics (AQD): A Novel Scheduling Algorithm ...

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2nd International Conference on Cyber Intelligence and information Retrieval, CIIR 2023

作者： Malviya, Abhishek Pandey, Sudhakar Tiwari, Amit Kumar Department of Information Technology NIT-Raipur Chattisgarh Raipur492010 India Department of Computer Science and Engineering United Institute of Technology Uttar Pradesh Prayagraj211010 India

ISBN: (纸本)9789819776023

In the complex world of modern telecommunication networks, Baseband Unit (BBU) pools play a crucial role in optimizing data processing and minimizing response time. This paper presents the Adaptive Queue Dynamics (AQD) scheduling algorithm, a cutting-edge approach specifically tailored for BBU systems. By combining traditional queuing principles with a feedback control mechanism, AQD allows for real-time adaptation to changing network conditions. Through constant monitoring of important system metrics and the use of a real-time feedback loop, AQD is able to intelligently adapt queuing parameters for optimal scheduling. With its ability to dynamically tune and continuously adapt parameters, AQD stands out by enabling the system to swiftly respond to unexpected events or sudden changes, ensuring minimal response time. Mathematical modeling and extensive simulations confirm the effectiveness of AQD in achieving this objective when compared to traditional methods. The AQD approach represents a significant leap forward in telecommunication network efficiency, providing a strong and adaptable solution designed for the demands of modern BBU systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Mobile Edge Computing for Efficient Vehicle Management in Smart City 14

Mobile Edge Computing for Efficient Vehicle Management in Sm...

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14th IEEE International Conference on Communication systems and Network Technologies, CSNT 2025

作者： Chanu, Asem Debala Shelar, Saylee Nath, Shubha Brata Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Engineering Ajeenkya DY Patil University Pune India

ISBN: (纸本)9798331531935

Mobile edge computing (MEC) promises to be a useful technology to enable applications in a smart city. However, the task offloading becomes challenging as the MEC infrastructure has limited resources. To solve the challenges of MEC, we have proposed a system architecture with an optimization algorithm to minimize the task delay in MEC. A Genetic Algorithm based task allocation algorithm finds the optimal set of edge servers for task deployment and processing. The proposed system efficiently optimizes the task delay in MEC as analyzed from the experiments performed by taking virtual machines as MEC servers. We show the usefulness of the proposed system by taking an application scenario for efficient vehicle management in a smart city. © 2025 IEEE.

关键词： Genetic algorithms

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Ihoya: knowledge management system based on community using User-Centered Design (UCD) method

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International Journal of information technology (Singapore) 2025年第4期17卷 2067-2074页

作者： Rafi, Muhammad Puspitodjati, Sulistyo Apriani, Niken Fitria Hakim, Shidiq Al Siagian, Al Hafiz Akbar Maulana Riyanto, Slamet Rodiah, Rodiah Kushadiani, Siti Kania Faculty of Computer Science and Information Technology Universitas Gunadarma West Java Depok Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency West Java Bogor Indonesia

Hoya represents a significant component of Indonesia’s biodiversity with considerable potential;however, its utilization remains suboptimal. There exists a keen interest among researchers, collectors, and horticultural enthusiasts to delve into this fascinating plant. The wide variety of Hoya species in Indonesia substantially challenges their identification. Consequently, there is a pressing need for a system that facilitates the identification of the Hoya plant and its various species. In this study, we propose developing a knowledge management system (KMS) to identify Hoya and its species. This proposal is anchored in the user-friendly nature of mobile-based applications, which offer accessibility at any time. To ensure the creation of a high-quality application that meets user satisfaction, a User Interface and User Experience (UI/UX) design process is implemented, employing the User Centered Design (UCD) methodology that concentrates on the preferences and requirements of users. Subsequently, the system Usability Scale (SUS) method is adopted to assess the design and verify its alignment with user needs. This research culminates in our developed KMS prototype, namely iHoya, encompassing mockup pages tailored to users’ preferences, requirements, and navigational flows. An evaluation of the prototype design using the SUS method, conducted through a survey of targeted users, categorizes the design as acceptable while indicating the necessity for certain enhancements. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Hoya Knowledge management system SUS UCD UI/UX

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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