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Integrating Convolutional and Recurrent Networks for Image Caption Generation: A Unified Approach

Integrating Convolutional and Recurrent Networks for Image C...

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International Conference on computing and Networking Technology (ICCNT)

作者： I Nandhini L Leo Prasanth T Nagalakshmi D Manjula Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Research Scholar Department of Information Science and Technology Anna University Chennai India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering VIT University Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Within the realm of natural language processing and computer vision, the synergy between Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) systems has emerged as some powerful paradigm for image captioning. this paper presents a comprehensive solution for visual and temporal understanding in the image captioning domain by presenting a unified framework that efficiently blends the strengths of CNN and LSTM architectures. Our proposed model takes advantage of the spatial hierarchies captured by CNNs in images and the sequential dependencies modeled by LSTMs in natural language. the CNN component efficiently retrieves high-degree features to result in coherent and contextually rich image captions. the hybridization of both of these networks creates a symbiotic relationship, addressing the challenges associated with both spatial and temporal aspects of image interpretation. We carried out in-depth tests on benchmark datasets, demonstrating the superiority of our hybrid CNN-LSTM architecture over standalone models. Our results showcase improved captioning exactness, fluency, and contextual relevance. Our proposed model achieved highest BLEU score $\mathbf{0. 8 1}$ when compared with existing methods. In addition, we investigated the interpretability of our model, shedding light on the visual and temporal cues learned by the hybrid architecture.

关键词： Symbiosis Visualization Fitting computer architecture Search engines User experience Natural language processing Convolutional neural networks Long short term memory Optimization

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Efficient DDoS Attack Detection through Lightweight Deep Learning Model in Cloud computing Environment

Efficient DDoS Attack Detection through Lightweight Deep Lea...

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Cluster, Cloud and Internet computing Workshops (CCGridW), IEEE/ACM International symposium on

作者： Brij B. Gupta Akshat Gaurav Varsha Arya Kwok Tai Chui Department of Computer Science and Information Engineering Asia University Taichung Taiwan Lebanese American University Beirut Lebanon Ronin Institute Montclair NJ USA Asia University Taichung Taiwan Hong Kong Metropolitan University (HKMU) Hong Kong

ISBN: (数字)9798350377514

ISBN: (纸本)9798350377521

In the era of cybersecurity, Distributed Denial of Service (DDoS) attacks continue to pose a substantial threat to network availability and reliability. To counter these attacks, sophisticated detection mechanisms are imperative. this paper presents an innovative approach to DDoS attack detection using a lightweight deep learning model. Leveraging the KDDCup dataset, a well-established benchmark in intrusion detection research, we have achieved remarkable results with a training accuracy of 99.61% and a testing accuracy of 99.56%. Our proposed model emphasizes efficiency without compromising accuracy, addressing the resource constraints often associated with real-time attack detection. By employing a carefully designed architecture, we reduce computational complexity while maintaining a high level of discriminatory power between normal and attack traffic. this lightweight deep learning model not only demonstrates superior performance but also showcases its potential for rapid deployment in scenarios demanding swift and reliable attack detection.

关键词： Deep learning Training Adaptation models Accuracy Computational modeling Intrusion detection Denial-of-service attack Data models Reliability computer crime

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Virtual Private Network (VPN) with Post-Quantum Cryptography (PQC)

Virtual Private Network (VPN) with Post-Quantum Cryptography...

引用

International Conference on Information and Communication Technology Convergence (ICTC)

作者： Hyoungsub Shim Bongho Kang Haejung Im Duhyun Jeon Seok-min Kim Department of Mechanical Engineering Chung-Ang University Seoul Seoul Korea Research and Development Center ICTK. LTD. Seoul Korea Department of Computer Scicne and Engineering Department of Mechanical Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

this paper introduces qTrustNet VPN, a next-generation Virtual Private Network (VPN) designed to enhance security in the quantum era. With the rapid advancement of quantum computing, traditional encryption methods face obsolescence, necessitating more robust security solutions. qTrustNet VPN addresses this need by integrating the WireGuard protocol, known for its simplicity and high performance, with Post-Quantum Cryptography (PQC) algorithms developed by the National Institute of Standards and Technology (NIST). these PQC algorithms are designed to withstand the decryption capabilities of quantum computers, providing long-term security.

关键词： Quantum computing Protocols NIST Virtual private networks Information and communication technology Encryption Low latency communication Next generation networking Faces Drones

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Multi-Class SVM & Random Forest Based Intrusion Detection Using UNSW-NB15 Dataset

Multi-Class SVM & Random Forest Based Intrusion Detection Us...

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International Conference on computing and Networking Technology (ICCNT)

作者： Anish Mathew Jose Avirup Mukherjee Joydeep Saha Anubhab Bhaumik Kamalesh Datta Radhashyam Patra Aradhana Behura School of Computer Engineering KIIT University BBSR Odisha Department of Electronics and Telecommunication Engineering VSSUT Burla Sambalpur Odisha School of Computer Engineering Kalinga Institute of Industrial Technology(KIIT) Universit BBSR Odisha

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Intrusion Detection or malicious node detection (IDS) show a important role in safeguarding computer networks system from security threats. However, traditional IDS methods often struggle with high false positive rates and limited detection accuracy. this paper presents an advanced IDS that harnesses the power of a multi-class support vector machine (SVM) and integrates random forest technique for feature selection, specifically targeting the UNSW-NB15 dataset. the motivation behind this research is to enhance IDS performance by leveraging robust machine learning techniques. the research problem centers on improving the accuracy and efficiency of detecting various network attacks while reducing false positives. Our approach involves meticulously preprocessing network traffic data to extract relevant features, utilizing random forest to identify the most informative ones, and then employing these features to train a multi-class SVM classifier. this classifier effectively categorizes network traffic into different attack types. the results demonstrate a significant improvement in prediction accuracy and a decrease in rate of false positives, thereby enhancing the overall effectiveness of IDS in protecting computer networks against security threats.

关键词： Support vector machines Accuracy Intrusion detection Null value Telecommunication traffic Forestry Feature extraction computer networks Security Random forests

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Research on computer Vision-Based Environment Perception and Decision-Making System for Autonomous Vehicles

Research on Computer Vision-Based Environment Perception and...

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IEEE International symposium on Information (IT) in Medicine and Education, ITME

作者： Ye Sun Li Shen Shuangnan Wu Hao Yan Feng Sheng Hunan Automotive Engineering Vocational University Zhuzhou China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

this paper delves into the design and implementation of autonomous vehicle systems, with a focus on the overall architecture, hardware platforms, software design, and system integration. the autonomous driving system captures environmental information through high-precision sensors, which is then processed in real-time and decisions are made by powerful computing units, leading to vehicle control actions. throughout the design and implementation process, emphasis is placed on real-time performance, robustness, and safety considerations. System performance is continuously enhanced through algorithm optimizations, hardware advancements, and improvements in system integration strategies. the objective of this research is to provide a robust foundation for the advancement of autonomous vehicles, thereby propelling progress in smart transportation systems.

关键词： Software design System performance System integration Real-time systems Hardware Safety Smart transportation Vehicle dynamics Autonomous vehicles Testing

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Identity Management Dapp using Ethereum Blockchain and IPFS

Identity Management Dapp using Ethereum Blockchain and IPFS

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Ananya Avvaru Aaditi Aryan Tandon Bm Beena Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa VidyapeethamIndia Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this paper explores the integration of a verification and document management DApp into the core of the Ethereum blockchain and the InterPlanetary File System (IPFS). this way, the multi-signature wallets and intelligent contracts introduced help restore many traditional problems based on the secure handling of documents and ensure increased security, data integrity, and easily accessible documents. this DApp will allow such safe, transparent interaction that mandates consensus with multi-sig functionalities during document transactions to ensure the immutability and retrievability of those documents. this implies that the solution would scale using Ethereum and IPFS while offloading pressure off the blockchain to assure high data availability and reliability. Recent performance reviews indicate relevant improvements in the processes related to document management, and this improvement may even have broader implications for the filling implementation in essential sectors that need both a secure and efficient document-handling process.

关键词： Training Reviews Data integrity Scalability Document handling Decentralized applications Filling InterPlanetary File System Security Reliability

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A Comprehensive Comparison of Machine Learning Techniques for Heart Disease Prediction

A Comprehensive Comparison of Machine Learning Techniques fo...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Devika Madhusoodanan R Vismaya Hridyalakshmi Santhosh Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Heart disease, affecting millions worldwide, poses an alarming threat to global health due to its high prevalence and significant impact on mortality rates. Despite advances in medical technology, early detection remains crucial for effective intervention and prevention strategies. Machine learning aids in heart disease detection by analyzing patient data to identify patterns and predict risk factors, facilitating early intervention and personalized treatment. In the proposed work, a comprehensive range of classifiers, including AdaBoost, SVM, Naïve Bayes, CatBoost, Random Forest, Perceptron, Decision Tree, XGBoost, kNN, Bagging, Extra Trees, Logistic Regression, Stacking, and Multi-layer Perceptron, were extensively employed to evaluate their effectiveness. Notably, Random Forest exhibited superior performance with an F1 measure of 0.98, endorsing its selection as the primary classifier in our model.

关键词： Heart Support vector machines Logistic regression Prevention and mitigation Stacking Forestry Bayes methods Random forests Regression tree analysis Diseases

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Energy-Efficient Hardware architecture for VVC TZS Using Approximate Subtractors

Energy-Efficient Hardware Architecture for VVC TZS Using App...

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IEEE Latin American symposium on Circuits and Systems (LASCAS)

作者： Rafael Ferreira Luciano Agostini Cláudio M. Diniz Bruno Zatt Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Pelotas RS Brazil Microelectronics Group (GME) Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

this paper investigates the impact of approximate subtractors in the hardware architecture of the Sum of Absolute Differences (SAD) computation within the Test Zone Search (TZS) algorithm, commonly used in current video encoders like the Versatile Video Coding (VVC). Four state-of-the-art imprecise subtractors $(AppS, AXSC1, AXSC2, AXSC3)$ were analyzed across various video resolutions to assess their influence on computational effort, energy consumption, and coding efficiency. the results show that subtractors like $AppS_{4}$ and $AXCS1_{4}$ provide significant reductions in energy consumption with minimal impact on video coding quality. these findings are especially relevant for low-power devices and embedded systems, where energy efficiency is critical. the use of imprecise subtractors offers a promising tradeoff between computational efficiency and energy savings, making them a viable solution for high-performance video encoders.

关键词： Video coding Energy consumption Embedded systems Energy conservation Energy resolution computer architecture Energy efficiency Hardware Computational efficiency Power dissipation

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An Improvised Genetic Algorithm Technique on Task Scheduling in Edge and Cloud computing Systems

An Improvised Genetic Algorithm Technique on Task Scheduling...

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International Conference on computing and Networking Technology (ICCNT)

作者： Nagarjuna Pitty N. Hemalatha K Venkata Ramana L.P Sajitha P Ramesh Babu M.Dinesh Babu Indian Institute of Science Bengaluru Karnataka India Saveetha School of Engineering (SIMATS) Chennai Tamilnadu India Department of CSE QIS College of Engineering and Technology Ongole Andrapradesh India Department of Computer Science and Business System R.M.K Engineering College Kavaraipettai Tamilnadu India Department of Computer Science College of Engineering and Technology Wollega University Nekemte Oromia Region Ethiopia Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In current years, part cloud computing systems have received huge adoption because of their capability to provide low latency and high bandwidth offerings to users. however, one of the primary challenges in these structures is green undertaking scheduling in the presence of dynamic and heterogeneous sources. To deal with this trouble, genetic set of rules (GA) optimization strategies were proposed as a potential solution. this approach utilizes genetic operators, which include mutation and crossover, to conform a populace of potential solutions and choose the best one. by way of considering various constraints and objectives, consisting of strength consumption and make span, the GA-primarily based project scheduling technique can efficaciously optimize useful resource allocation in area cloud computing structures, at the end improving their performance and resource utilization.

关键词： Cloud computing Processor scheduling Dynamic scheduling Genetics Resource management Low latency communication Biological cells Optimization Genetic algorithms Genetic operators

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HIDE: A Hyperdimensional In-DRAM Encoder for Fast and Energy-Efficient Classification

HIDE: A Hyperdimensional In-DRAM Encoder for Fast and Energy...

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IEEE International symposium on Quality Electronic Design

作者： Ahmed Mamdouh Dayane Reis Computer Science and Engineering University of South Florida Tampa USA

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

Hyperdimensional computing (HDC) processes information in a high-dimensional space, offering advantages over deep neural networks (DNNs), such as smaller model sizes, robustness to soft errors, and the capability for one/few-shot learning without significant accuracy drops in some applications. Although HDC shows great potential, encoding data into a hyperdimensional space poses challenges due to the significant data movement between processing units and memory in conventional architectures. Processing-in-memory (PIM) embeds computation directly within memory, reducing latency and energy consumption. Among PIM approaches, in-DRAM computation leverages the parallelism of DRAM to perform bulk bit-wise operations with high throughput. In this paper, we present HIDE, the first hyperdimensional in-DRAM encoder for fast and energy-efficient classification. HIDE integrates H DC encoding directly within commodity DRAM, significantly reducing energy consumption and improving system performance. HIDE achieves up to $272.8\times (23.3\times)$ encoding speedup over a CPU (GPU) during training, with energy reductions of 94.8% (95.0%) during training (inference). Integrating HIDE with the GPU achieves an overall system speedup of $15\times(7.2\times)$ during training (inference) compared to a GPU-only system, along with energy savings of 92.3% and 82.7%, respectively, for classification tasks with the MNIST, ISOLET, and UCHAR datasets.

关键词： Training Energy consumption Memory architecture Energy conservation Graphics processing units Random access memory throughput Encoding Energy efficiency Central Processing Unit

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