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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Desai, Jaynesh H Bagle, Neha S Rami, Khyati Verma, Shanti Bhagwan Mahavir University Department of Computer Applications Gujarat Surat India Sal Institute of Technology & Engineering Research Department of Computer Science Gujarat India L J University Department of Computer Applications Gujarat Ahmedabad India

ISBN: (纸本)9798350350357

With the development of heterogeneous sensory networks in various applications of domains for ensuring the security against denial based service (DoS) attacks will become a paramount. In this research study, a proposal of a novel system named SecureSenseGuard(SSG) for effectively detecting DoS attacks in heterogeneous sensor networks. SecureSenseGuard(SSG) leveraging the Decision Tree (DT) algorithm augmented with Recursive type Features Elimination (RFE) for achievinga robust and efficient attack detection. The proposed system may operate by first Collection of data from diverged sensors within the network. The data, representing various network attributes, are then preprocessed and fed into the DT algorithm for classifications process. RFE will be employed as a feature selection techniques to enhance the performance of DT algorithm by iterative process for removing less relevant features, thus improving in detection accuracy and reducing computational overhead. The proposed system SecureSenseGuard(SSG) will offer several advantages over existing approaches. By employing DT, it will provide interpretable and transparent decision rules for facilitating understanding and trust in the detection process. Moreover, the integration of RFE will enhance the system's scalability and adaptability for dynamic network environments. To evaluate the effectiveness of SecureSenseGuard(SSG) an extensive experiment may be conducted by using various ranges of datasets and synthetic attack scenarios. The Results demonstrate that SecureSenseGuard(SSG) achieved high detection rates. while maintaining a low false alarm rate, outperforming traditional approaches. Overall, SecureSenseGuard(SSG) may present a promising solution in mitigating DoS attacks in heterogeneous sensory networks by offering a balance between detection accuracy, interpretability, and computational efficiency. © 2024 IEEE.

关键词： Heterogeneous networks

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Constructing the ChatGPT for PDF Files with Langchain - AI 7

Constructing the ChatGPT for PDF Files with Langchain - AI

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prem Jacob, T. Bizotto, Beatriz Lucia Salvador Sathiyanarayanan, Mithileysh Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India University Center Unifacvest Department of Social And Applied Sciences Lages Brazil Research & Innovation MIT Square London United Kingdom

ISBN: (纸本)9798350359299

Queries in PDFs can be time-consuming and labor-intensive because of the unstructured nature of the PDF document type and the need for accurate and relevant search results. By applying cutting-edge algorithms for natural language processing to examine PDF documents and extract relevant data, LangChain solves these difficulties. It makes use of an easy search interface, adjustable filters, and efficient indexing and retrieval mechanisms to enhance the search experience. To efficiently retrieve relevant information from PDF documents, users can annotate critical portions, store queries, and create bookmarks with LangChain. The characteristics of LangChain improve overall productivity and greatly simplify PDF querying. Semantic search, driven by the latest Transformer language models, represents a significant evolution in information retrieval systems. This research work explores the capabilities of semantic search to efficiently retrieve documents from large collections in response to natural language queries. Unlike traditional keyword-based approaches, semantic search connects the power of Transformer models to discern meaning, providing users with more contextually relevant and accurate results within seconds. This technology not only enhances the user experience by delivering superior matches from document collections but also lays the foundation for tackling more intricate tasks, like text summarization and question-answering. The research investigates the impact of semantic search on information retrieval efficiency and accuracy, comparing its performance with conventional methods. The findings presented herein not only showcase the immediate benefits of semantic search but also open paths for future research and development in natural language processing and its applications. © 2024 IEEE.

关键词： Information retrieval systems

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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computer Systems science & engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： computer networks machine learning security malware detection feature selection ransomware

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd International Conference on Intelligent Systems and Sustainable computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM Machine learning

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Generative AI-Enabled Quantum Encryption Algorithm for Securing IoT-Based Healthcare Application Using Blockchain

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IEEE Internet of Things Journal 2025年第13期12卷 24541-24551页

作者： Prajapat, Sunil Kumar, Pankaj Das, Ashok Kumar Muhammad, Ghulam Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics Dharamshala176215 India International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has transformed numerous domains through the AI of Things (AIoT). Nonetheless, AIoT encounters issues related to energy usage and carbon emissions as mobile technology continues to progress. Generative AI (GAI) possesses significant potential to mitigate carbon emissions associated with AIoT, owing to its higher reasoning and generative powers. Conventional security protocols frequently encounter issues with computational efficiency, latency, and overall security comprehensiveness. Blockchain technology, characterized by its decentralized and immutable properties, is a viable approach for improving electronic healthcare data transmission and node authentication in IoT networks. This research examines secure data transmission and node encryption in IoT systems, with a particular emphasis on data management. Conventional approaches encounter constraints in computational efficiency, latency, and comprehensive security. This study presents a novel protocol that combines GAI and blockchain technology with quantum encryption to enhance authentication and ensure secure data transmission. The algorithm comprises multiple consecutive processes, including the encoding and transmission of node requests, followed by the authentication process utilizing hash functions and digital signatures. The authentication approach utilizes a challenge-response technique, guaranteeing that only nodes with authentic credentials can advance. Thereafter, a dynamic key exchange protocol and quantum encryption method provide secure data delivery. The results indicate the procedure’s effectiveness in ensuring secure and regulated access to patient data, underscoring its significance in medical facilities. The system’s functionalities are augmented by a thorough evaluation employing machine learning. The findings indicate that the system exhibits an accuracy of 99.4%, precision of 99.10%, recall of 98.66%, F1-score of

关键词： Quantum computers

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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Low-Complexity Channel Estimation for RIS-Assisted ISAC System

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China Communications 2025年第4期22卷 296-308页

作者： Chen Zhen Li Jianqing Zhang Haijun Zhang Wei The Institute of Microelectronics University of MacaoMacao 999078China School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing 100083China Henan Academy of Science Applied Physics Institute Co. LtdZhengzhou 450008China

Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless ***,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability *** address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is *** approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift *** previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC *** effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.

关键词： channel estimation element grouping integrated sensing and communication(ISAC) RIS

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Enhancing Student Outcomes with LSTM-CNN and Data Analytics in Higher Education

Enhancing Student Outcomes with LSTM-CNN and Data Analytics ...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sangeetha, S.K.B. Maheswari, S. Rajendran, Sukumar Mathivanan, Sandeep Kumar Balusamy, Balamurugan Verma, Meenakshi SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India VIT Bhopal University School of Computing Science Engineering and Artificial Intelligence Madhya Pradesh India Galgotias University School of Computer Science and Engineering Greater Noida203201 India India Bengaluru Campus Pune India

ISBN: (纸本)9798350390049

With a focus on the use of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) approaches to predict students' academic performance, the study provides an extensive review of the literature on the application of artificial neural networks (ANNs) and machine learning (ML) techniques. The study highlights the possible advantages of implementing cutting-edge technology innovations like analytics and data mining in learning environments. The analysis reveals that the reviewed literature primarily focuses on higher education. The results show that LSTM-CNN and data mining methodologies are consistently integrated, suggesting a trend in using these combined approaches to evaluate academic progress. No observable patterns were found in spite of the variability in the input variables, which was dictated by the study's context and the availability of data. It is acknowledged, nevertheless, that there is a dearth of hard data regarding the effective application of these strategies for raising student achievement and outcomes. The study emphasizes how critical it is to close the theoretical and practical divides regarding the limited applicability of LSTM-only CNN models in practical educational contexts. It highlights the need for more research to address this discrepancy and suggests putting more effort into creating and putting into practice strategies that directly improve student performance and help schools achieve their goals. © 2024 IEEE.

关键词： Convolutional neural networks

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Curved Road Lanes Detection Using Fully Convolutional Neural Network 11th

Curved Road Lanes Detection Using Fully Convolutional Neural...

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11th International Conference on Intelligent computing and Applications, ICRTC 2023

作者： Paramarthalingam, Arjun Arivunambi, Amirthasaravanan Janarthanan, Ashokkumar Department of Computer Science and Engineering University College of Engineering Villupuram Tamil Nadu Villupuram605103 India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

ISBN: (纸本)9789819717231

The ever-growing expansion of highway transportation infrastructure leads to significant role in development of every nation. The number of people owning four-wheeler vehicle has increased every year with automatic driving cars attracts people, further it will make a great impact in short to long distance travel. The autonomous vehicles generally use lane and curve detection approaches to automatically discover the way using deep learning algorithms. To tackle the problem of inadequate robustness for extracting multi-lane marking, a multi-lane identification strategy relying on a deep neural network based on FCN network is proposed in this paper. The ResNet-152 based fully convolutional network is used for lane and curve detection. The ResNet-152 model is chosen in this work because the existing models viz. VGG-16, AlexNet performs worse when number of layers increased, further error rate also get increased, but in ResNet-152 error rate decreases with increasing layers. The ResNet-152 model used skip connections. Finally, the fit interval is determined using the Hough Transform, and the lane marker is fitted using the least square method. The experiments are carried out on the TuSimple dataset. The suggested FCN-based lane and curve detection approach performs well on the detection of curved lane roads. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hough transforms

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