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1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Rahul Saxena, Urvashi Alam, Taj Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Noida India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Cloud computing is foreseen as the lineage framework model for Distributed Computing Environment and has escalated itself in due course of time owing to the advantages it provides to its service users, and also due to the unfolding ease, it renders to its customers. In recent times, especially because of pandemics, there has been a tremendous acceleration and dependency on the cloud environment and a sudden outburst in the increase of cloud users. However, it is significant to mention that the Cloud environment is quite susceptible to numerous attacks from intruders, thereby resulting in jeopardizing the system security and user privacy. Hence, one of the biggest challenges encountered by the researchers in the cloud domain is to propose algorithmic approaches or frameworks that ensure cloud security and maintain data integrity. Service users often hesitate to synchronize and store their data to cloud since they are not sure if their data privacy and confidentiality on the cloud environment will be preserved or compromised. This vulnerability of cloud systems eventually hinders cloud storage development. The objective of this paper is to project the trajectory towards maintaining privacy of the user data by assigning a restricted grant of permission to the user data stored on the cloud using cryptographic techniques. This work demonstrates an approach through which Identity validation along with Broadcast based Encryption mechanism aims to secure cloud data. This work depicts the functionality of various modules like set-up, encryption, and decryption processes, and the work is then finally compared with its peer research works based on the revocation rate. © 2022 IEEE.

关键词： Access control

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An Ensemble Learning-Based DDoS Attack Detection Considering the Impact of ICA 14

An Ensemble Learning-Based DDoS Attack Detection Considering...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Jana, Anique Mahato, Dipesh Karmakar, Raja Ghosal, Sandipan Chatterjee, Twinkle Techno International NewTown Computer Science and Engineering Kolkata700156 India Heritage Institute of Technology Computer Science and Engineering Kolkata700107 India Techno International NewTown Information Technology Kolkata700156 India

ISBN: (纸本)9798350335095

Distributed denial of service (DDoS) attacks can cause considerable financial harm and operational concerns in the internet business era. Detecting such attacks necessitates overcoming two challenges: distinguishing normal packets from attack packets and consistently and effectively identifying distinct attack categories. As a result, an ideal detection system should be simple, reliable, and effective, with low false alarm rates. The goal of this paper is to give an analysis of machine learning (ML) methods for the detection of DDoS attacks using Ensemble Learning with and without implementing Independent Component Analysis (ICA). It improves the feature extraction, source separation, noise reduction, and data pre-processing, resulting in better performance and insights. The KDD-99 dataset is used to train and test ML models, using Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Gaussian Naive Bayes. They are used to extract data while categorizing the different types of input features during the training. The performance of the proposed mechanism is analyzed with and without the use of ICA. Results show that the DDoS attack is most successfully detected using the Random Forest classifier with an accuracy of 99.9670%. Moreover, ICA improves the performance of all ML models when compared to unsupervised learning techniques. © 2023 IEEE.

关键词： Decision trees

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A review of intelligent optimization for group scheduling problems in cellular manufacturing

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Frontiers of engineering Management 2023年第3期10卷 406-426页

作者： Yuting WANG Yuyan HAN Dunwei GONG Huan LI School of Computer Science Liaocheng UniversityLiaocheng 252059China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Given that group technology can reduce the changeover time of equipment,broaden the productivity,and enhance the flexibility of manufacturing,especially cellular manufacturing,group scheduling problems(GSPs)have elicited considerable attention in the academic and industry practical *** are two issues to be solved in GSPs:One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each *** a number of studies on GSPs have been published,few integrated reviews have been conducted so far on considered problems with different constraints and their optimization *** this end,this study hopes to shorten the gap by reviewing the development of research and analyzing these *** literature is classified according to the number of objective functions,number of machines,and optimization *** classical mathematical models of single-machine,permutation,and distributed flowshop GSPs based on adjacent and position-based modeling methods,respectively,are also *** but not least,outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.

关键词： cellular manufacturing group scheduling flowshop literature review

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Design Of A Framework For Optimizing Bandwidth Allocation Of Video In Wireless Networks Using Hybrid Meta Heuristic Algorithm 5

Design Of A Framework For Optimizing Bandwidth Allocation Of...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Akinyokun, Oluwole Adegoke, Abejide Ashade, Bidemi Achas, Joy Anuoluwapo, Olagunju Ogunbiyi, Temitope Federal University of Technology Department of Software Engineering Ondo State Akure Nigeria Bells University of Technology Department of Computer Science and Information Technology Ogun State Ota Nigeria

ISBN: (纸本)9798331542559

Video transmission in wireless network poses several challenges such as latency, delayed throughput, slow convergence rate, high error rates, packet losses and many others which make it impossible for users to enjoy Quality of Service (QoS). Video in wireless network presents additional challenges due to unavailability of needed bandwidth. Bandwidth in any organization is a limited resource that is being competed for by the different users. Efficient and effective bandwidth allocation is a vital role in satisfying the QoS requirements for multimedia applications such as video streaming. This research work examined conventional and current techniques for bandwidth optimization. For better bandwidth use, conventional bandwidth allocation techniques are gradually being replaced with optimization algorithms using Genetic Algorithm (GA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), Tabu Search (TS), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and so on. This work specifically addressed bandwidth challenges using hybrid algorithms of PSO+TS algorithms in optimizing the bandwidth allocation of Bells University of technology, Ota, Ogun State, Nigeria. The PSO algorithm explores bandwidth by giving high priority to video traffic and low priority to other types of traffic (data traffic) while the TS algorithm exploits the bandwidth. The results were evaluated using CPU time and Worst Case Time Complexity of the algorithms. Results showed that with the proposed system, PSO+TS algorithm outperformed other individual algorithms of PSO and TS. Video traffics depending on resolution had higher priority levels and were assigned higher bandwidth while data traffic were assigned low bandwidth and as such video users will be able to stream conveniently with good QoS. © 2024 IEEE.

关键词： Ant colony optimization

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Autonomous Search and Rescue Agent Using Deep Reinforcement Learning

Autonomous Search and Rescue Agent Using Deep Reinforcement ...

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2023 International Conference on Recent Advances in science and engineering technology, ICRASET 2023

作者： Rampriya, R.S. Alam, Mohammed Junaid Jayan, Sri Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Madras Institute of Technology Department of Computer Technology Tamilnadu Chennai India

ISBN: (纸本)9798350306927

Disaster management is one of the areas of paramount importance for any government or organization. This process gets very tedious and tricky for some specific type of disasters. Deploying manpower for mitigating the disaster and saving the victims is a risky decision because the life of the people in the disaster management team is put to risk, which is a very unfortunate thing. So instead of using manpower, we can deploy intelligent machine agents, which are equivalent or sometimes more efficient than human beings for such highly risky tasks and avert the loss of lives. In our case, we have specifically considered the disaster wherein fire breaks out in a building and people get trapped inside due to the dangerous situation. In this case, where deploying manpower to rescue them requires a high quality protective gear and can even put their lives on risk, we have proposed to deploy intelligent machine agents that are trained to figure out the path on their own and find their way to the victims after which a rescue operation can be performed by safely escorting the victims to a nearby exit in the building. The agent in the proposed application has been evaluated by the metrics such as the time taken to reach the victims and come back to the exit, the total rewards gathered by the agent during its exploration in the environment and the number of correct decisions it made during its journey. The agent takes an average time of 145.6 seconds to complete its operation in the real-time environment. It gathered an average peak cumulative reward value of -0.2 in the real time environment. The agent on an average, records 67.3% chance of making a correct decision (that gets a positive reward) given a state vector. © 2023 IEEE.

关键词： Autonomous agents

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A Novel Approach for Human Activity Recognition Using Vision Based Method 1

A Novel Approach for Human Activity Recognition Using Vision...

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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Parida, Lomashradhha Parida, Bivasa Ranjan Mishra, Manas Ranjan Jayasingh, Suvendra Kumar Samal, Tusharkanta Ray, Sujata Institute of Management and Information Technology Department of Computer Science and Engineering Cuttack India Silicon Institute of Technology Department of Computer Science and Engineering Bhubaneswar India C. V. Raman Global University Department of Computer Science and Engineering Bhubaneswar India Dhaneswar Rath Institute of Engineering and Management Studies Department of Computer Science and Engineering Cuttack India

ISBN: (纸本)9798350315769

Human Activity Recognition (HAR) is a fascinating process that involves identifying and categorizing human activities based on observations of subject behavior and environmental factors. Out of the major phases of HAR, preprocessing and feature extraction, in particular, require a lot of attention because they constitute the foundation for the training phase. The current evaluation effectively examines the trends in vision-based research approaches. Many models based on the artificial intelligence are suggested for the activity recognition;however, they couldn't perform satisfactorily on real world long-term HAR due to their lacuna in the extraction of spatial and temporal data. In light of these drawbacks, we propose a hybrid methodology for the human activity recognition which integrates Convolutional Neural Network (CNN) with the Long Short-Term Memory (LSTM), where CNN works for extracting the spatial characteristics and LSTM is used for the learning of the temporal information. A complete evaluation of the substantial work done in HAR domain and its vision-based techniques would help the readers. © 2023 IEEE.

关键词： Long short-term memory

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Arabic Fake News Detection Using Deep Learning

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computers, Materials & Continua 2022年第5期71卷 3647-3665页

作者： Khaled M.Fouad Sahar F.Sabbeh Walaa Medhat Faculty of Computers&Artificial Intelligence Benha UniversityEgypt University of Jeddah College of Computer Science and EngineeringJeddah21493Saudi Arabia Information Technology and Computer Science Nile UniversityEgypt

Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online ***,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment *** identification of fake news and rumors and their dissemination on social media has become a critical *** have adverse effects on users,businesses,enterprises,and even political regimes and *** of the art has tackled the English language for news and used feature-based *** paper proposes a model architecture to detect fake news in the Arabic language by using only textual *** learning and deep learning algorithms were *** deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+*** datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife *** results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.

关键词： Fake news detection deep learning machine learning natural language processing

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Corpus Generation for Multi-class Bangla Email Classification and Its Evaluation

Corpus Generation for Multi-class Bangla Email Classificatio...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Islam, Md. Tariqul Azharul Hasan, K.M. Institute of Information and Communication Technology Khulna University of Engineering and Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh

ISBN: (纸本)9798350316001

Email is an important communication tool used worldwide, but it also exposes people to various threats such as spam emails, which can result in theft, blackmailing, and phishing. Unfortunately, there is no spam email detection tool available for the Bangla language, despite it being widely used on the internet and email This makes Bengali people are vulnerable to serious threats as spammers send emails in Bangla to avoid filters. There is a huge number of emails sent in Bangla and these Bangla emails suffer from such problems. Though three is no dataset available for Multi-class Bangla Email Classiflcation, T'his paper explores generating a corpus for Bangla emails using important features that help to filter out Bangla emails into multi-classes. We generate a collection of 7000 Bangla emails to address the problem. We also evaluate the features selected of the emails for multi class classification. © 2023 IEEE.

关键词： Support vector machines

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Dates Fruit Recognition: From Classical Fusion to Deep Learning

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computer Systems science & engineering 2022年第1期40卷 151-166页

作者： Khaled Marji Alresheedi Suliman Aladhadh Rehan Ullah Khan Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

There are over 200 different varieties of dates fruit in the ***,every single type has some very specific features that differ from the *** recent years,sorting,separating,and arranging in automated industries,in fruits businesses,and more specifically in dates businesses have inspired many research *** this regard,this paper focuses on the detection and recognition of dates using computer vision and machine *** experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates *** machine learning includes the Bayesian network,Support Vector Machine,Random Forest,and Multi-Layer Perceptron(MLP),while the Convolutional Neural Network is used for the deep learning *** feature set includes Color Layout features,Fuzzy Color and Texture Histogram,Gabor filtering,and the Pyramid Histogram of the Oriented *** fusion of various features is also extensively explored in this *** MLP achieves the highest detection performance with an F-measure of ***,deep learning shows better accuracy than the classical machine learning *** fact,deep learning got 2%more accurate results as compared to the MLP and the Random *** also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.

关键词： features Deep separating

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