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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bhavya, G. Bhanot, Shreya Srinidhi, M. Sidhu, Samreen Krishnan, Sajitha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Today, educational tools have become necessary for learners to learn well and improve their knowledge. These tools complement knowledge as they can make it more vivid and add some simulation or game into the learning process. It is self-learning since the students can learn from any place and it also enables the students to go further on content that may interest them, and which may help them in the understanding of the concept. This paper takes the idea of developing SynchroLearn, which is an educational visualization tool developed with the help of HTML, CSS, and JavaScript that enables the teaching of deadlock handling techniques effectively for both the developers and the students. To expound on techniques such as the Producer-Consumer problem, Peterson's method, Test-and-Set Lock, and the Dining Philosophers problem, an online tool known as SynchroLearn helps in providing the learner an actual time visualization. These strategies present several ways of handling resources, and with that, the management of their utilization process, so that the processes could be controlled in terms of what resources they require, which in turn, helps to solve and prevent such cases of deadlocks. This approach helps to learn complex relationships as well as to understand the interactions between the different processes, identify the deadlock situations, and use the essential methods for their resolution. They bring out the idea linking theoretical proficiencies and practical application by demonstrating them as simple-to-follow graphics which are therefore easier to implement. © 2024 IEEE.

关键词： Students

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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Drone-Assisted Load Distribution Framework for Traffic Optimization in IoT Networks 17

Drone-Assisted Load Distribution Framework for Traffic Optim...

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

作者： Soni, Priyanka Mense, Omkar Manohar Kanti Addya, Sourav National Institute of Technology Cloud and Smart System Services Lab Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798331531195

Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms. © 2025 IEEE.

关键词： Drones

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A Variational Inference-Based LSTM-enhanced Deep Neural Model for Sequential Recommendations

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IEEE Access 2025年 13卷 95945-95962页

作者： Ngaffo, Armielle Noulapeu Kamgang, Ines Raissa Djouela Maka, Ebenezer Maka Malong, Yannick University of Douala Laboratory of Computer Science Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala8580 Cameroon Department of computer engineering University of Buea Faculty of Engineering and Technology Cameroon

Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM models capture sequential dependencies and temporal dynamics, making them ideal for personalized, session-based, and sequential recommendation tasks. However, traditional LSTM-based recommender systems do not model uncertainty since they perform deterministic predictions. Moreover, those recommender models exhibit poor generalization on small datasets, and poor-handle cold-start and data sparsity problems. To overcome those limitations, this paper presents an LSTM-based recommender model that leverages an improved deep matrix factorization to accurately address the data sparsity problem. In addition, an enhanced variational inference coupled to the Evidence Lower Bound Optimization is applied on LSTM unit layers to perform uncertainty-aware predictions. Thereafter, latent vectors obtained from LSTM networks are effectively involved in the sequential recommendation process. Experiments have been conducted on several real-world datasets to evaluate our proposal and the results show the proposed model presents significant performances compared to state-of-the-art models. © 2013 IEEE.

关键词： Deep neural networks

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Mood Melody Matchmaker system Using Deep Learning Model 9

Mood Melody Matchmaker System Using Deep Learning Model

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mishra, Susmita Pradhan, Chittaranjan Jananee, V. Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India School of Computer Engineering KIlT Deemed To Be University Bhubaneswar India Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

Accessing a wide variety of music is made simple by modern music services. Many users rely on recommendation algorithms to select the ideal song for any given circumstance. Due to its frequent use for relaxation, mood regulation, stress, and illness management, as well as support for mental and physical activities, music has a profound influence on individuals. Music therapy employs a range of therapeutic environments and methods to enhance overall well-being. The article suggests a mood melody matching related to the listener's feelings. Machine learning and deep learning methods are employed to create a recommendation system designed to assist individuals in choosing music suitable for various life circumstances. Efficiently recognizing the ideal track for the user's current mood, the system delivers songs tailored to their emotional state, eliminating the need for users to spend time searching or seeking out music themselves. © 2024 IEEE.

关键词： Python

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Diversified deep hierarchical kernel ensemble regression

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Multimedia Tools and Applications 2025年第15期84卷 15553-15573页

作者： Liu, Zhifeng Xu, Zhengqin Ebhohimhen Abhadiomhen, Stanley Qian, Xiaoqin Shen, Xiang-Jun School of Computer Science and Communication Engineering JiangSu University Zhenjiang212013 China Department of Computer Science University of Nigeria Nsukka410105 Nigeria Department of Ultrasound Affiliated People’s Hospital of Jiangsu University Zhenjiang212002 China

Deep ensemble learning models that combine multiple independent deep learning models with multi-layer processing architectures have proven to be effective techniques for improving the accuracy and robustness of deep learning models. In this paper, we propose a diversified kernel ensemble regression method, which is developed from the well-known kernel ridge regression methods. Motivated by multi-view data modeling ideas, we treat each individual kernel as one view of original data in kernel representation space. Therefore, we develop a deep kernel ensemble ridge regression method in Neural Tangent Kernel (NTK) to address the problem faced by traditional kernel ridge regression methods in finding appropriate types of kernels and their parameters. Specifically, as multiple deep kernel regressors share common information from the multi-view kernel representations, our proposed method is built through a hierarchical modeling method, where deep kernel regressors share a common parameter and also have model-specific parameters in individual regressors, which is further helpful in improving our model’s performance. Furthermore, to achieve better diversified deep kernel representations in our proposed method, the Hilbert-Schmidt Independence Criterion (HSIC) is used to regularize our proposed model. In this way, we can find more diversified kernel representations among multiple kernel ensemble regressors to achieve better generalization performance. Experiments on several classification and regression datasets, such as MNIST, TinyImageNet-200, ORL, Credit and YearPredictionMSD demonstrate that our proposed method can achieve best regression performance than other state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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Cost-aware cloud workflow scheduling using DRL and simulated annealing

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Digital Communications and Networks 2024年第6期10卷 1590-1599页

作者： Yan Gu Feng Cheng Lijie Yang Junhui Xu Xiaomin Chen Long Cheng School of Control and Computer Engineering North China Electric Power UniversityBeijing 100026China School of Mathematics Southwest Jiaotong UniversityChengdu 610032China Department of Computer Science University of ReadingReading RG66AHUK

Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time *** address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related *** this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud *** approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)*** SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to *** provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.

关键词： Cloud computing Deep reinforcement learning Simulated annealing algorithm Job scheduling Workflow

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Two-Dimensional Golay Complementary Array Sets with Arbitrary Lengths for Omnidirectional MIMO Transmission

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IEEE Transactions on Communications 2024年第2期72卷 1135-1145页

作者： Zhao, You-Qi Pai, Cheng-Yu Huang, Zhen-Ming Liu, Zilong Chen, Chao-Yu National Cheng Kung University Department of Engineering Science Tainan701 Taiwan National Cheng Kung University Department of Engineering Science and the Institute of Computer and Communication Engineering Tainan701 Taiwan University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom National Cheng Kung University Department of Electrical Engineering and the Institute of Computer and Communication Engineering Tainan701 Taiwan

This paper presents a coding approach for achieving omnidirectional transmission of certain common signals in massive multi-input multi-output (MIMO) networks such that the received power at any direction in a cell remains constant for any given distance. Specifically, two-dimensional (2D) Golay complementary array set (GCAS) can be used to design the massive MIMO precoding matrix so as to achieve omnidirectional transmission due to its complementary autocorrelation property. In this paper, novel constructions of new 2D GCASs with arbitrary array lengths are proposed. Our key idea is to carefully truncate the columns of certain larger arrays generated by 2D generalized Boolean functions. Finally, the power radiation patterns and numerical results are provided to verify the omnidirectional property of the GCAS-based precoding. The error performances of the proposed precoding scheme are presented to validate its superiority over the existing alternatives. © 1972-2012 IEEE.

关键词： Peak to average power ratio

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A Multi-Agent Garage Service Search and Recommendation with Hybrid MLs and LLMs

A Multi-Agent Garage Service Search and Recommendation with ...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Shuvo, Mahfuzur Rahman Rahman, Ashifur Akuthota, Vishwanath Paul, Tanay Islam, Mahfujul Ashraf, Md Sadi Roy, Prosenjit Reza, Md Tanzim International American University Department of Management Information System United States Chittagong University of Engineering and Technology Department of Computer Science and Engineering Bangladesh TechOptima Hyderabad India Brac University Department of Computer Science and Engineering Bangladesh Prairie View A & M University Department of Computer Science TX United States

ISBN: (纸本)9798331513894

The automobile service industry's explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance system, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of 85%, recall of 70.8%, and an F1-score of 77.2%, demonstrating the efficacy of integrating classical ML with generative AI. The system's average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system's Propose a Multi-Agent system With high accuracy,

关键词： Inverse problems

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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