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Proceedings of the 7th International Conference on Advances in computing and Data Sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on Computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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Multiobjective Sequential Transfer Optimization: Benchmark Problems and Preliminary Results 13

Multiobjective Sequential Transfer Optimization: Benchmark P...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Xue, Xiaoming Feng, Liang Yang, Cuie Liu, Songbai Song, Linqi Tan, Kay Chen City University of Hong Kong Department of Computer Science Hong Kong College of Computer Science Chongqing University Chongqing China Northeastern University State Key Lab of Synthetic Automation for Process Industries Shenyang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

In cases of frequent problem-solving of multiobjective optimization tasks from a domain due to changing conditions or problem features, a growing number of individual tasks will be solved and stored in a database, providing an opportunity for a target task at hand to achieve better optimization performance through knowledge transfer from the previously-solved tasks, which is also known as sequential transfer optimization. Despite a variety of transfer algorithms that have been developed over the years, the research on the design of benchmark problems for evaluating such algorithms received far less attention. Oftentimes, the source and target tasks in existing test problems are manually assembled or extended from specific practical problems, limiting their ability to represent the diverse yet complex source-target similarity relationships in real-world problems. In light of this, we propose design methods to generate multiobjective sequential transfer optimization problems (MSTOPs) systematically in this work, wherein the Pareto manifolds of individual tasks and the manifold-based similarity between the tasks can be customized with ease, enabling a broad spectrum of representation of the diverse similarity relationships between the source-target Pareto manifolds of MSTOPs. Lastly, a benchmark suite with 12 test problems is developed using the proposed methods, which would serve as an arena for electing superior multiobjective sequential transfer optimization algorithms. The source code is available at https://***/XmingHsueh/MSTOP. © 2024 IEEE.

关键词： Optimization algorithms

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Joint Channel and Multi-User Detection Empowered with Machine Learning

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Computers, Materials & Continua 2022年第1期70卷 109-121页

作者： Mohammad Sh.Daoud Areej Fatima Waseem Ahmad Khan Muhammad Adnan Khan Sagheer Abbas Baha Ihnaini Munir Ahmad Muhammad Sheraz Javeid Shabib Aftab College of Engineering Al Ain UniversityAbu Dhabi112612UAE Department of Computer Science Lahore Garrison UniversityLahore54792Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversitySeongnam13557South Korea Department of Computer Science College of Science and TechnologyWenzhou Kean University325060USA Department of Computer Science Hameeda Rasheed Institute of Science and TechnologyMultan66000Pakistan

The numbers of multimedia applications and their users increase with each passing *** multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network *** this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two *** first stage estimates the channel parameters,and the second performsmulti-user *** proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural *** study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO *** FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

关键词： Channel and multi-user detection minimum mean square error multiple-input and multiple-output minimum mean channel error bit error rate

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A Disambiguation of Security-based software Testing

A Disambiguation of Security-based Software Testing

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International Conference on Information and Computer Technologies (ICICT)

作者： Robert Dilworth Charan Gudla Md Shohel Rana Feroz Ahmed Department of Computer Science and Engineering Mississippi State University Starkville Mississippi USA Department of Computing and Software Engineering Florida Gulf Coast University Fort Myers Florida USA Prediction 3D

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

This paper reviews security testing, covering the concepts and notions essential to the discipline. The following analysis uses an inverted funnel approach to reduce the broad topic of software testing into its constituent parts. With this framework in mind, the paper will attempt to answer the following questions. Why is software testing significant? What are the roles of users and developers as they relate to software? What techniques are commonly used when testing software? Is the discipline of software testing unified; if not, what controversies exist, and what do they concern? What is security testing? What is the purpose of security testing? What metrics are used to guide security testing? What are common threats that hamper or impede security testing? How can developers mitigate the risk posed by security threats; what defensive options are available? What improvements can be made to software testing? What can occur in the absence of software/security testing?

关键词： software testing Measurement Reviews software Security Computer security

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Smart Energy Management System Using Machine Learning

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Computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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MDAU-Net: A Multi-Scale U-Net with Dual Attention Module for Pavement Crack Segmentation 18

MDAU-Net: A Multi-Scale U-Net with Dual Attention Module for...

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18th International Conference on Intelligent Systems and Knowledge engineering, ISKE 2023

作者： Al-Huda, Zaid Peng, Bo Al-Antari, Mugahed A. Algburi, Riyadh Nazar Ali Saleh, Radhwan A. A. Moghalles, Khaled Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China Sejong University College of Software & Convergence Technology Department of Artificial Intelligence Seoul Korea Republic of College of Technical Engineering Aeronautical Technical Engineering Al-Farahidi University Baghdad Iraq Kocaeli University Department of Mechatronics Engineering Kocaeli Turkey Southwest Jiaotong University School of Information Science and Technology Chengdu China

ISBN: (纸本)9798350318401

Crack segmentation is a critical and demanding task in pavement engineering. Recent studies using Convolutional Neural Networks (CNNs) or vision Transformer (ViT) for crack segmentation have shown promising results, but there are still challenges due to the inhomogeneous crack intensity, complex pavement environments, limited labeled training datasets, and noise in most crack images. These challenges make it difficult to distinguish cracks from noise. To address these challenges, we propose MDAU-Net, a multi-scale U-Net with dual attention module based on an encoder-decoder architecture for pavement crack segmentation. MDAU-Net exploits multi-scale input to preserve spatial awareness in all encoder layers, and the dual attention module (DAM) combines global average pooling (GAP) attention module and position attention (PC) module to extract more comprehensive and discriminative features for crack boundary recognition and improve crack segmentation effectiveness. For optimizing the segmentation network and capturing detailed crack information, a hybrid weighted segmentation loss function is developed. Comprehensive experimental evaluations on two benchmark datasets (DeepCrack and Crack500) demonstrate that MDAU-Net achieves state-of-the-art segmentation performance. These results show that MDAU-Net is a promising approach for pavement crack segmentation and can be used to improve the safety and durability of pavement infrastructure. © 2023 IEEE.

关键词： Semantic Segmentation

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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MMCo-Clus - An Evolutionary Co-clustering Algorithm for Gene Selection (Extended abstract) 39

MMCo-Clus - An Evolutionary Co-clustering Algorithm for Gene...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Cui, Laizhong Acharya, Sudipta Mishra, Sumit Pan, Yi Huang, Joshua Zhexue Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Queen's University School of Computing ON Canada Indian Institute of Information Technology Department of Cse Assam Guwahati India Chinese Academy of Sciences Shenzhen Institute of Advanced Technology China

ISBN: (纸本)9798350322279

Dimensionality reduction through feature selection becomes inevitable to overcome the problem of the Curse of dimensionality. In this article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a Multi-objective optimization-based Multi-view Co-Clustering algorithm (named MMCo-Clus). A thorough comparative analysis with existing feature selection algorithms using external/internal evaluation metrics supports our proposed method's potency. © 2023 IEEE.

关键词： Clustering algorithms

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Enhancing Autonomous Intrusion Detection System with Generative Adversarial Networks

Enhancing Autonomous Intrusion Detection System with Generat...

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IEEE International Conference on e-Science and Grid computing

作者： Kevin Kostage David West Tim Meinert Chengyi Qu Prasad Calyam Luca Mazzola Department of Computing and Software Engineering Florida Gulf Coast University Florida USA Department of Electrical Engineering and Computer Science University of Missouri Columbia USA Lucerne University of Applied Sciences and Arts Lucerne Switzerland

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Effective training in Machine Learning and Deep Learning models necessitates datasets that provide sufficient patterns and contextual information, particularly crucial in IoT networks. Imbalanced datasets, however, significantly challenge the performance of Autonomous Intrusion Detection Systems (IDS), leading to suboptimal detection rates for minority classes. In this paper, we address this issue by utilizing various Generative Adversarial Network (GAN) models, including WGANGP, CGAN, CTGAN, and CWGANGP, to generate synthetic data that balance these imbalanced datasets. We evaluate the performance of IDS models trained on GAN-augmented datasets against those trained on unbalanced datasets, considering metrics such as fitting duration, generation duration, accuracy, precision, recall, and F1 score. Our findings reveal substantial improvements in IDS performance with the application of GANs across binary, general, and specific attack classifications. Additionally, we compare the effectiveness of GANs with classical sampling algorithms, such as SMOTE and Random Oversampling. This comprehensive evaluation underscores the potential of GANs as a sophisticated solution for improving IDS accuracy and reliability in handling complex and highly imbalanced datasets.

关键词： Training Measurement Deep learning Accuracy Fitting Intrusion detection Generative adversarial networks

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