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15th International Conference on Contemporary Computing, IC3 2023

作者： Murtuza, Shariq Asawa, Krishna Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798400700224

The Software-Defined Networking (SDN) paradigm empowers network operators to manage and coordinate network activities, allowing for greater flexibility and dynamic updating of switching tables. Over time, more and more service providers are integrating SDN into their core network infrastructure, recognizing the benefits of its robustness and adaptability. In the face of the rapid expansion of high-speed consumer Internet, data centers are increasingly migrating their internal networks to SDN-based backbones, which offer enhanced performance and scalability. SDN is widely projected to become the technology of the future, with many of the top Fortune 500 companies already implementing SDN in some form or another to meet their core network requirements. As a result, network devices, Internet service providers, content delivery networks, and data centers will increasingly be linked to high-performing SDNs and will communicate via them. The ubiquitous penetration of SDN-based networks, coupled with the multitude of user devices connected to them, is critical for the consumers of these services. However, like traditional networks, Software Defined networks are vulnerable to Denial of Service attacks, which target the availability of a service connected to the network. Thus, it is essential to develop effective strategies for identifying and mitigating such attacks to ensure the integrity and reliability of SDN-based networks. © 2023 ACM.

关键词： Denial-of-service attack

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Land Use—Classification by Machine Learning Classifiers Using Landsat 8 Imagery

Land Use—Classification by Machine Learning Classifiers Usi...

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International Conference on Recent Advancements in Civil Engineering, RACE 2022

作者： Thakur, Reena Panse, Prashant Department of Computer Science and Engineering Jhulelal Institute of Technology Nagpur India Department of Information Technology Medi-Caps University Indore India

ISBN: (纸本)9789819926756

Through the twentieth and first decades of the twenty-first century, quick and abandoned population growth, in combination with industrial and economic development, enhanced the frequency of land-use-land-cover change (LULCC) for several times, particularly in evolving nations. Moreover, the most effective ways to manage and analyze land transformation are the quantifiable valuation of changes in LULC, it is important to evaluate the comparison of the accuracy of different Land Use-Land Cover mapping algorithms in order to choose the good classifier for future earth observation and its applications. The QGIS tool is applied to consider minimal distance, maximum likelihood, and spectral angle mapping in this paper. Various classes have been explored, and training is carried out based on these to determine the accuracy of each method. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Landsat

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A Review of AI-Enhanced Navigation Strategies for Mobile Robots in Dynamic Environments

A Review of AI-Enhanced Navigation Strategies for Mobile Rob...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Nasti, Shoaib Mohd Chishti, Mohammad Ahsan Central University of Kashmir Department of Information Technology Ganderbal India National Institute of Technology Department of Computer Science and Engineering Srinagar India

ISBN: (纸本)9798350372229

A mobile robot is a software-controlled machinery that uses sensors and advanced technologies to perceive obstacles and navigate its surroundings to reach its desired destination. The prevalence of mobile robots has increased in recent years due to their ability to autonomously navigate any environment without the need for direction or human intervention. Mobile robots enhance services and daily operations in diverse sectors, including healthcare, enterprises, security, agriculture, education, industries, and defense. The need for mobile robots has surged due to technology improvements and their diverse duties and services, including lifting heavy things, search and rescue operations, lunar exploration, underwater exploration, surveillance, etc. This paper reviews AI-enhanced navigation strategies for mobile robots in dynamic environments, emphasizing the distinction between various approaches. This paper also covers navigation components and types of mobile robots. This paper concludes by tabulating the research gaps and future scope of the recently reviewed literature, providing valuable insights into the state-of-the-art in the field. © 2024 IEEE.

关键词： Autonomous Robots Dynamic Environment Locomotion Navigation Static Environment

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis 4

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Selvam, L. Dhamotharan, R. Krishnan, R. Santhana Francis Raj, J. Relin Sankar, G. Ram Yamini, G. Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Pottapalayam Sivaganga India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of English Chennai India

ISBN: (纸本)9798331523923

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies. © 2025 IEEE.

关键词： Recurrent neural networks

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LightGBM empowered by whale optimization for thyroid disease detection

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International Journal of information technology (Singapore) 2023年第4期15卷 2053-2062页

作者： Sinha, Bam Bahadur Ahsan, Mohammad Dhanalakshmi, R. Department of Computer Science Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi 834010 India Informatics Cluster School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Department of Computer Science Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620009 India

Thyroid disorder is a significant source of formulation in medical classification and prognosis, with onset being a challenging assumption in medical study. The thyroid gland is a vital organ of our body. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are two of the most prevalent thyroid disorders that affect the thyroid’s ability to regulate the pace of the body’s metabolism. Data cleansing procedures were used to prepare the data to the point where analytics could be performed to determine the risk of individuals developing thyroid disease. In this paper, an optimal framework design employing LightGBM (Light Gradient Boosting Machine), Sequential Backward Selection (SBS), and a metaheuristic technique termed Whale Optimization (WO) plays a decisive role in detecting Thyroid disease. The objective of this research is to provide an impeccably clean and rational way to detecting thyroid disorder in humans. Numerous processes have been applied to thyroid data sets with astounding results, yet the data used for thyroid disease detection is redundant, inconsistent and contains missing feature values, as per our literature survey. The proposed effort would develop an expert advisory system based on the Opti-LightGBM architecture for the purpose of detecting thyroid disorders and initiating necessary medication promptly. The proposed Opti-LightGBM model yields an accuracy of 99.75% on Thyroid dataset and outperforms several state-of-art comparative models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Classification Feature selection LightGBM Optimization Thyroid

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PointBi-FPN: An Extention to Pointpillars for LiDAR 3D Object Detection in Autonomous Vehicles Using Bi-Directional Feature Pyramid Network

PointBi-FPN: An Extention to Pointpillars for LiDAR 3D Objec...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Soumya, A. Krishna Mohan, C. Cenkeramaddi, Linga Reddy Babu, Sobhan Indian Institute of Technology Department of Computer Science and Engineering Hyderabad India University of Agder Department of Information and Communication Technology Grimstad Norway

ISBN: (纸本)9798350350821

Autonomous vehicles increasingly rely on accurate three-dimensional (3D) object detection for safe navigation. While two-dimensional (2D) methods offer computational efficiency, the shift to 3D detection enhances precision in understanding environments. Point-based and voxel-based approaches are accurate but computationally intensive for onboard deployment. Pillar-based methods like PointPillars provide efficiency but may lack detection accuracy compared to voxel-based approaches. In this paper, we focus on enhancing the performance of PointPillars, a popular pillar-based detector, using LiDAR data. The proposed approach (PointBi-FPN) employs a bi-directional feature pyramid network (Bi-FPN) as a backbone, that aggregates multiscale features in the input data, providing a holistic view of the environment, which is crucial for detecting objects of varying sizes and distances accurately. Bi-FPN improves the model's understanding of complex scenes, making it more robust to occlusions. Through extensive experimentation on the KITTI dataset, the PointBi-FPN approach demonstrates better performance across all three detection benchmarks: BEV (Bird's Eye View), 3D (Three-Dimensional), and AOS (Average Orientation Similarity). Notably, the proposed approach exhibits significant improvements, particularly in accurately detecting objects labeled as 'hard' difficulty. © 2024 IEEE.

关键词： Object detection

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Exploring the Potential of Machine Learning in Automated Real-Time Data Analysis Systems 15

Exploring the Potential of Machine Learning in Automated Rea...

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

作者： Chandrakala, P. Meshram, Vishal A. Meena, Mansingh Nithya, M.S. Alam, Intekhab Kumar, V. Mathan Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

This paper explores the current country of gadget learning and its capability to allow real-time records analysis. Automated actual-time statistics analysis structures enable business specialists to reply to situations by extracting complicated patterns faithfully. Gadget learning techniques can assist these units by allowing algorithms to analyze previously amassed information and generalize these consequences to novel scenarios. This paper examines the latest developments in device learning algorithms that will likely be leveraged in automatic actual-time facts evaluation structures. The report also considers challenges associated with data education and the mission's generation selection. ultimately, the paper discusses numerous processes for integrating gadget getting to know into real-time records analytics systems that could permit agencies to take complete advantage of the significant ability of machine mastering. © 2024 IEEE.

关键词： Adversarial machine learning

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Large-Scale Expensive Optimization with a Switching Strategy

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Complex System Modeling and Simulation 2022年第3期2卷 253-263页

作者： Mai Sun Chaoli Sun Xiaobo Li Guochen Zhang Farooq Akhtar School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Department of Computer Sciences and Information Technology University of Kotli Azad Jammu and KashmirKotli 11100Pakistan

Some optimization problems in scientific research,such as the robustness optimization for the Internet of Things and the neural architecture search,are large-scale in decision space and expensive for objective *** order to get a good solution in a limited budget for the large-scale expensive optimization,a random grouping strategy is adopted to divide the problem into some low-dimensional sub-problems.A surrogate model is then trained for each sub-problem using different strategies to select training data *** that,a dynamic infill criterion is proposed corresponding to the models currently used in the surrogate-assisted sub-problem ***,an escape mechanism is proposed to keep the diversity of the *** performance of the method is evaluated on CEC’2013 benchmark *** results show that the algorithm has better performance in solving expensive large-scale optimization problems.

关键词： large-scale optimization problems computationally expensive problems random grouping surrogate models

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Project-based Learning in Software Engineering Education: Integrating Blockchain-Oriented Repositories in SE Curriculum and Coursework 22

Project-based Learning in Software Engineering Education: In...

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22nd IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2024

作者： Hossain Faruk, Md Jobair Tasnim, Masrura Cheng, Jerry Q. New York Institute of Technology Department of Computer Science United States Kennesaw State University Department of Information Technology United States

ISBN: (纸本)9798350391343

Software Engineering (SE) education aims to seamlessly blend theoretical knowledge with practical exposure, ensuring students the grasp of SE's foundational principles, design methodologies, implementation, testing, and maintenance capabilities with communication paradigms and problem-solving strategies. Under rapid evolvement and advancement, new technologies such as blockchain and artificial intelligence are poised to redefine the SE landscape. However, traditional SE curricula often lag behind in incorporating cutting-edge technologies, particularly in student class projects. This paper underscores the significance of blockchain within the SE pedagogical framework. Specifically, we introduce a project-based learning approach with blockchain technology to create an open-source software repository for students to engage in requirement analysis, design, implementation, and maintenance of blockchain-oriented software development. These activities will allow students to understand the nature and architecture of blockchain applications that have long been ignored in both computer science and SE education. Practicing these projects will enable learners to become experts in blockchain software development and rethink the need for improving requirements elicitation, design, development, and testing strategies. The repository consists of ten modules, each of which contains real-world topics. We have developed one of ten modules so far, which focuses on a blockchain-based student record database. A preliminary survey, both pre and post-exposure, was conducted with 24 university students of both undergraduate and graduate levels pursuing degrees in either computer science or software engineering programs. The feedback was overwhelmingly positive, highlighting the value of our approach in enhancing the learning experience of novel technologies. The participants also emphasized the need for adopting blockchain-based ideas in course projects. Our future endeavors will focus on

关键词： Students

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Optimal Fuzzy Logic Enabled Intrusion Detection for Secure IoT-Cloud Environment

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computers, Materials & Continua 2023年第3期74卷 6737-6753页

作者： Fatma S.Alrayes Nuha Alshuqayran Mohamed K Nour Mesfer Al Duhayyim Abdullah Mohamed Amgad Atta Abdelmageed Mohammed Gouse Pasha Mohammed Ishfaq Yaseen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Internet of Things(IoT)devices have developed at a faster rate and utilization of devices gets considerably increased in day to day *** the benefits of IoT devices,security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security ***,IoT devices are vulnerable to different kinds of attacks.A single attack on networking system/device could result in considerable data to data security and *** the emergence of artificial intelligence(AI)techniques can be exploited for attack detection and classification in the IoT *** this view,this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system(MFSFL-IDS)in the IoT *** presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT *** achieve this,the MFSFL-IDS model employs data pre-processing to transform the data into useful ***,henry gas solubility optimization(HGSO)algorithm is applied as a feature selection approach to derive useful feature ***,adaptive neuro fuzzy inference system(ANFIS)technique was utilized for the recognition and classification of intrusions in the ***,binary bat algorithm(BBA)is exploited for adjusting parameters involved in the ANFIS model.A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct *** experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recentapproaches with maximum accuracy of 99.80%.

关键词： Cloud computing security fuzzy logic intrusion detection internet of things metaheuristics

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