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Backpropagation Shuffled Leaping Neural Network Implementation in Classification of DDoS Packet Flow Traffic in Data Mining

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SN computer Science 2024年第8期5卷 1131页

作者： Umamaheswari, N. Kumutha, K. Kalaivani, M.S. Ramya, R. Tagore College of Arts and Science Tamilnadu Chennai India Department of Computer Application- PG Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Computer science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India

All web applications remember the ultimate goal of storage services, Distributed Denial of Service (DDoS), to achieve high security from various attacks. The client-server application reduces fees and runs the elite registration gadget, while the notable part assumes that it is manufactured using a grant application. Analysis of the traceback data is difficult in the previous system, and then it containsa common DDoS attack *** DDoS attacks involving different network layers such as (SIDDoS (SQL (Structured Query Language) injection), HTTP (HyperText Transfer Protocol) flood, TCP (Transmission Control Protocol) are recommended here as there are signs that new datasets of common datasets are not being collected in the proposed system. The DDoS attack classification consists of three main steps: preprocessing, trace out the source IP address, Backpropagation Shuffled Leaping Neural Network (BSLNN) based on maintaining the specifications’ integrity. The preprocessing is the first step based on the Gabor Filter used to remove the data’s noise and the second Adaboost Random Optimal Selection method to analyzethe packet flow’s relative frequency. The third step is the Backpropagation Shuffled Leaping Neural Network (BSLNN) classification of traceback data maintaining the specifications analysis’s integrity. The proposed system achieves good periodic study of packet flow accuracy and time complexity. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaboost random optimal selection Backpropagation Shuffled Leaping Neural Network (BSLNN) Gabor filter Preprocessing Trace out the source IP address

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Energy-aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

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Software - Practice and Experience 2024年第2期54卷 109-148页

作者： Hashemi, Sayed Mohsen Sahafi, Amir Rahmani, Amir Masoud Bohlouli, Mahdi Department of Computer Engineering Qeshm Branch Islamic Azad university Qeshm Iran Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences Zanjan Iran Research and Innovation Department Petanux GmbH Bonn Germany Zanjan Iran

The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location-aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study. © 2023 John Wiley & Sons Ltd.

关键词： Quality of service

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A Comprehensive Review on the Prediction of Video Frame and Motion Contents

A Comprehensive Review on the Prediction of Video Frame and ...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Mohana Priya, P. Ulagapriya, K. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

The capability of predicting, or analyzing the future outcomes of motion pictures using intelligent systems is the video frame prediction process. Video frame prediction is a significant and challenging aspect of computer Vision (CV) applications. Video prediction plays a vital role in terms of robotics and Artificial Intelligence (AI) applications in detecting the future frames of a video, depending on which, the decision-making process can be carried out. The existing video frame prediction processes include pixel law based and motion-based predictions, which have been proven to be ambiguous. This leads to inaccurate predictions and indirectly deteriorates the performance of the real time applications. This paper performs a detailed review of the existing methodologies, their merits, pitfalls and challenges that create havoc in the video frame prediction process. In addition, this paper performs a detailed analysis on the existing datasets that assist in the video prediction process. This review summarizes various methodologies and datasets along with their performance analysis, which will open research possibilities in the field of video frame prediction. © 2024 IEEE.

关键词： Artificial Intelligence computer Vision Datasets Robotics Video frame prediction

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Design and Development of a Mobile-Based Data Acquisition System for Aerial Bridge Inspection 15

Design and Development of a Mobile-Based Data Acquisition Sy...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Himongala, Ryan P. Aleluya, Earl Ryan M. Clar, Steve E. Pao, Jeanette C. Alagon, Francis Jann A. Salaan, Carl John O. Lavilles, Rabby Q. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Philippines College of Computer Studies Mindanao State University - Iligan Institute of Technology Department of Information Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Infrastructures such as bridges are ubiquitous in today's world, but over time, corrosion, continuous vibrations, heavy loads, natural disasters, and aging have actively weakened their structural integrity. Hence, it is crucial to periodically assess the bridge's condition to maintain its structural soundness. Nevertheless, conventional bridge inspection techniques involve inspectors to visually assess a bridge's condition and manually capture images of its defects. UAVs for bridge inspections typically improve the conventional approach. This method provides a visual system by mounting a camera on the UAV equipped with a software application dedicated to data acquisition. However, the application operates primarily on desktop computers or laptops while incompatible with smartphones and tablets, limiting its portability and making it inconvenient for inspectors. In this study, the researchers developed an application for mobile devices to further improve data acquisition. The application is designed to display a real-time video feed from the shelled UAV, capture visual data of bridge defects, and allow users to review the data after the inspection. The researchers tested and evaluated the application's performance regarding its functionality. Results show that the application has successfully improved the portability and efficiency of the inspection process, achieving its intended features and functionalities while adhering to the requirements. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Identify The Potential Instance of Copy Forgery Using Similar Algorithm 9

Identify The Potential Instance of Copy Forgery Using Simila...

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9th International Conference on advanced Computing and Communication Systems, ICACCS 2023

作者： Manikandan, A. Kumar, N. Thirumal, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350397376

In a similar fashion one of the most popular types of digital art, counterfeit detection necessitates knowledge of picture structures and the ability to generate visual diversions for many purposes, including entertainment. The use of color is very crucial for creating city features. Previous studies have examined the interrelationships of color within homogenous metropolitan zones, looking for connections between neighboring areas and culturally comparable peoples. Color, acclaim, the dissemination of one's color, and satisfaction. When communicating with a foreign audience, it is common practice to have the message reviewed and edited to conform to the local language and cultural norms. © 2023 IEEE.

关键词： Color

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BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization

BAPO: Base-Anchored Preference Optimization for Overcoming F...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lee, Gihun Jeong, Minchan Kim, Yujin Jung, Hojung Oh, Jaehoon Kim, Sangmook Yun, Se-Young Graduate School of AI KAIST Korea Republic of Samsung Advanced Institute of Technology Korea Republic of Department of Electrical and Computer Engineering UBC Canada

ISBN: (纸本)9798891761681

While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents further challenges in preserving previous knowledge. This paper examines the impact of personalized preference optimization on LLMs, revealing that the extent of knowledge loss varies significantly with preference heterogeneity. Although previous approaches have utilized the KL constraint between the reference model and the policy model, we observe that they fail to maintain global knowledge and general alignment when facing personalized preferences. To this end, we introduce Base-Anchored Preference Optimization (BAPO), a simple yet effective approach that utilizes the initial responses of reference model to mitigate forgetting while accommodating personalized alignment. BAPO effectively adapts to diverse user preferences while minimally affecting global knowledge or general alignment. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Dual-logic-in-memory implementation with orthogonal polarization of van der Waals ferroelectric heterostructure

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InfoMat 2024年第2期6卷 107-117页

作者： Jingjie Niu Sumin Jeon Donggyu Kim Sungpyo Baek Hyun Ho Yoo Jie Li Ji-Sang Park Yoonmyung Lee Sungjoo Lee SKKU Advanced Institute of Nanotechnology(SAINT) Sungkyunkwan UniversitySuwonRepublic of Korea Department of Nano Science and Technology Sungkyunkwan UniversitySuwonRepublic of Korea Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonRepublic of Korea IMEC HeverleeBelgium Department of Nano Engineering Sungkyunkwan UniversitySuwonRepublic of Korea

The rapid advancement of AI-enabled applications has resulted in an increasing need for energy-efficient computing ***-in-memory is a promising approach for processing the data stored in memory,wherein fast and efficient computations are possible owing to the parallel execution of reconfigurable logic *** this study,a dual-logic-in-memory device,which can simultaneously perform two logic operations in four states,is demonstrated using van der Waals ferroelectric field-effect transistors(vdW FeFETs).The proposed dual-logic-in-memory device,which also acts as a twobit storage device,is a single bidirectional polarization-integrated ferroelectric field-effect transistor(BPI-FeFET).It is fabricated by integrating an in-plane vdW ferroelectric semiconductor SnS and an out-of-plane vdW ferroelectric gate dielectric material—CuInP_(2)S_(6).Four reliable resistance states with excellent endurance and retention characteristics were *** two-bit storage mechanism in a BPI-FeFET was analyzed from two perspectives:carrier density and carrier injection controls,which originated from the out-of-plane polarization of the gate dielectric and in-plane polarization of the semiconductor,*** conventional multilevel FeFETs,the proposed BPIFeFET does not require additional pre-examination or erasing steps to switch from/to an intermediate polarization,enabling direct switching between the four memory *** utilize the fabricated BPI-FeFET as a dual-logic-inmemory device,two logical operations were selected(XOR and AND),and their parallel execution was *** types of logic operations could be implemented by selecting different initial states,demonstrating various types of functions required for numerous neural network *** flexibility and efficiency of the proposed dual-logic-in-memory device appear promising in the realization of next-generation low-power computing systems.

关键词： ferroelectric field-effect transistor in-plane ferroelectricity logic-in-memory out-of-plane ferroelectricity

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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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Real-Time Digital RF Emulation - Part I: The Direct Path Computational Model

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 2卷 866-879页

作者： DeLude, C. Driscoll, J. Mukherjee, M. Rahman, N. Mao, X. Kamal, U. Khan, S. Sivaraman, H. Huang, E. Mcharg, J. Swaminathan, M. Pande, S. Mukhopadhyay, S. Romberg, J. Georgia Institute of Technology School of Computer Science AtlantaGA30332 United States MIT Lincoln Laboratory Advanced Concepts and Technologies Group LexingtonMA02421 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Department of Electrical Engineering State CollegePA16802 United States

In this article, we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line (TDL) model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of M objects all interacting with one another, the TDL model's computational requirements scale as O(M3) per sample: there are O(M2) channels, each with O(M) complexity. In this article, we develop a new "direct path"model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an O(M2) per-sample scaling of the computational requirements. The impact of this is drastic, a 200-object scenario sees about a 100× reduction in the number of per-sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle-level C++ simulator of a hardware implementation of the model. © 2023 IEEE.

关键词： C++ (programming language)

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Adaptive Fine-Grained Feature Mining and RoI Feature Interaction Network for Small Object Detection in Aerial Images

Adaptive Fine-Grained Feature Mining and RoI Feature Interac...

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

作者： Xie, Fangmin Xiong, Zhan Wang, Zhuoyue Yang, Kun Xiao, Guoqiang College of Computer and Information Science Southwest University Chongqing China Institute of Advanced Computer Science Leiden University Netherlands Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States

ISBN: (纸本)9798350368741

Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects. To address these issues, we propose a small object detector called Adaptive Fine-grained Feature Mining Network (AFMNet) based on an anchor-free framework. Firstly, an Adaptive Fine-grained Feature Mining (AFFM) module is designed to generate Regions of Interest (RoI) on high-resolution feature maps and mine fine-grained information that benefits the representation of small objects. Secondly, an Inter-RoI Feature Interaction (IRFI) block is developed to efficiently integrate complementary cues-namely, deep layer semantic information and shallow layer spatial details-and enhance RoI features. Experiments on the VisDrone dataset demonstrate that our method achieves state-of-the-art performance. The source code of our AFMNet is available at https://***/xiefangmin/AFMNet. © 2025 IEEE.

关键词： Drone-View Image Feature Fusion Fine-grained Features Region of Interest Small object detection

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