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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Bhatnagar, Gunjan Kumar Rajoria, Yogendra Sakeel, Md. Vigenesh, M. Premananthan, G. Dongre, Deepika School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India School of Basic & Applied Sciences K. R. Mangalam University Department of Mathematics Gurugram India Invertis University Department of Computer Applications Bareilly India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India Vishwakarma Institute of Information Technology AI & Ds Pune India

ISBN: (纸本)9798350304480

Since the number of linked devices continues to grow quickly, the Web of Everything (IoT) is being employed change today in more industries. More issues in regards to safety, stability, and supportability are brought on by an increase in smart devices, particularly as it relates to 5G technology. We are concentrating on the safety component of IoT systems in just this article because it is a relatively new field. For varied purposes, such as cost savings or a desire to forego the use of generation system, many Internet device manufacturers need not take security into account when designing their products. The opponent may use those potentially dangerous technologies to carry out several destructive assaults. As a result, we created a device capable of recognizing malicious activity from a particular IoT networks node. They are capable to offer detection methods for IoT utilizing a primary key that may be put inside the system using a multilayer brain networks for identification and surveillance. The accomplishment dispels any misconceptions about methods for deep training while demonstrating how that systems can be simply adapted and deployed to every system. © 2023 IEEE.

关键词： Classification CNN IoT Machine Learning Malware Multicategories

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Discovering High Utility Episodes in Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 473-486页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Chao, Han-Chieh Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen5063 Norway National Dong Hwa University Department of Electrical Engineering Hualien974 Taiwan University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability. © 2020 IEEE.

关键词： Artificial intelligence

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Face Identification with Feature Learning Using Quasilinear Partial Differential Equation

Face Identification with Feature Learning Using Quasilinear ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Muthukumaran, V. Satheesh Kumar, S. Radhika, E.K. Arpana, G.K. Nalini, R. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Computer Science Sindhi College Bangalore India Department of ECE East West College of Engineering Bangalore India

ISBN: (纸本)9783031810855

This study suggests a novel soft computing approach for enhancing face recognition performance using the feature learning methodology. The suggested system of Quasi-linear Partial Differential Equations (QPDE) based face recognition model is used to increase the accuracy of facial identification. The webcam first catches the user's image and saves it to the user's computer. The inference model receives the captured image. The input image is pre-processed to enhance the image, and then the image is split into numerous integral images at the inference model. Using speed-up robust features, an intriguing point is constructed for each integral image. The computed interest point is converted into a QPDE based weight matrix by representing the strength of features at all the regions of the face. With the generated weighted matrix of features extracted from the facial image, the method generates a Fast Artificial intelligence (AI) network with many layers. Each layer of the neural network has many neurons with dedicated functions to compute the similarity and feature weight using QPDE. The proposed approach has produced efficient results compared to earlier methods for the Flickr-Faces-HQ database (FFHQ) datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Soft computing

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Multi-Layered Deep Learning Features Fusion for Human Action Recognition

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computers, Materials & Continua 2021年第12期69卷 4061-4075页

作者： Sadia Kiran Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Yunyoung Nam Robertas Damaševicius Muhammad Sharif Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Mathematics Silesian University of TechnologyGliwicePoland Department of Computer Science COMSATS University IslamabadWah CampusPakistan

Human Action Recognition(HAR)is an active research topic in machine learning for the last few *** surveillance,robotics,and pedestrian detection are the main applications for action *** vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of *** this article,we proposed a new method for the use of deep learning for *** the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer *** Resnet-50 Pre-Trained Model is used as a deep learning model in this *** are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold *** selected features are finally passed to multiple classifiers for final *** are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and *** accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,*** with existing techniques has shown that the proposed method provides improved accuracy for ***,the proposed method is computationally fast based on the time of execution.

关键词： Action recognition transfer learning features fusion features selection classification

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Artificial Intelligence Methods to Support People Management in Organizations

Artificial Intelligence Methods to Support People Management...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Sonawane, Prashant Ashok Mishra, Krishna Nand Raman, P. Kumar, Kishor Jawarkar, Nilesh G. Kandavalli, Sunanda Ratna. Gondwana University Department of Commerce Gadchiroli442605 India Radha Govind University Department of Computer Science Jharkhand Ramgarh829122 India College Poonamallee Department of Management Studies Panimalar Engineering Tamil Nadu Chennai600123 India Delhi Technological University Department of Applied Mathematics Delhi India JSPM' Rajarshi Shahu College of Engineering Department of Mechanical Engineering Pune India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics & Communication Engineering Andhra Pradesh Tirupati517502 India

ISBN: (纸本)9798350309140

The research paper showcases in-depth research on AI technology and its direct impact on organizations and people. Along with this research paper also discussed how AI technology can support organizations in streamlining their business processes, improving their decision-making skills, enhancing staff engagement, and raising the rate of production with the help of machine learning and natural language processing. Besides this, it is examined in this paper that AI technology can also be applied to automate the entire screening method, shorten the timetable, and manage the available resources needed for new staff recruitment. © 2024 IEEE.

关键词： Adversarial machine learning

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SSABA:Search Step Adjustment Based Algorithm

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computers, Materials & Continua 2022年第6期71卷 4237-4256页

作者： Fatemeh Ahmadi Zeidabadi Ali Dehghani Mohammad Dehghani Zeinab Montazeri Stepán Hubálovsky Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Civil Engineering Islamic Azad UniversityEstahban BranchEstahbanIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding the suitable solution to optimization problems is a fundamental challenge in various *** algorithms are one of the effective stochastic methods in solving optimization *** this paper,a new stochastic optimization algorithm called Search StepAdjustment Based Algorithm(SSABA)is presented to provide quasi-optimal solutions to various optimization *** the initial iterations of the algorithm,the step index is set to the highest value for a comprehensive search of the search ***,with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal,the step index is reduced to reach the minimum value at the end of the algorithm *** is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal *** results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed *** addition,the performance of the proposed SSABA is compared with the performance of eight well-known algorithms,including Particle Swarm Optimization(PSO),Genetic Algorithm(GA),Teaching-Learning Based Optimization(TLBO),Gravitational Search Algorithm(GSA),Grey Wolf Optimization(GWO),Whale Optimization Algorithm(WOA),Marine Predators Algorithm(MPA),and Tunicate Swarm Algorithm(TSA).The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.

关键词： Optimization population-based optimization problem search step optimization algorithm minimization maximization

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Multi-objective VNF Placement Optimization with NSGA-III 1st

Multi-objective VNF Placement Optimization with NSGA-III

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1st International Conference on Advances in Computing Research, ACR’23

作者： Bekhit, Mahmoud Fathalla, Ahmed Eldesouky, Esraa Salah, Ahmad Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Kent Institute Australia Melbourne Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia College of Computers and Informatics Zagazig University Zagazig Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9783031337420

With the rising acceptance of virtual network functions (VNFs) as a replacement for traditional network functions, the optimal placement of VNFs has become a crucial task for ensuring constant performance within constrained resources. This research investigates the challenge of mapping and scheduling VNFs as a multi-objective optimization problem. The state-of-the-art methods have focused on optimizing the mapping or scheduling of VNFs while considering one or two objectives continuously. In this study, we aim to optimize two objectives: minimizing the link capacity cost and maximizing the average resource utilization of each virtual machine. To address this problem, an adaptation of the NSGA-III optimization algorithm is proposed and thoroughly evaluated through a set of experiments. In terms of objective values, the results outlined that the proposed method outperformed the other methods of comparison by a significant margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mapping

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A Self-Expressive Feature Selection Algorithm Based on Self-Representation of Data

A Self-Expressive Feature Selection Algorithm Based on Self-...

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International Conference on Digital Home (ICDH)

作者： Jie Qiu Zuoyun Chen Jianmei Chen Wei Lan Xiaojun Ding Center for Applied Mathematics of Guangxi Computer Science and Engineering College Yulin Normal University Yulin Guangxi China Guangxi Key Laboratory of Multimedia Communications and Network Technology Center for Applied Mathematics of Guangxi Guangxi University Nanning Guangxi China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

Many biological data exhibit characteristics such as high dimensionality, large scale, high complexity, and high noise. In many cases, it is necessary to eliminate noise and identify truly useful features for better data analysis and modeling. In labeled data, algorithms such as correlation analysis or embedded selection methods can be used to select features most relevant to the labels. However, these algorithms are not effective for unlabeled data. Currently, common methods involve selecting features with higher variance or, based on feature correlation, identifying central features from a group of related features. This paper proposes a new method for feature selection using self-representation of data. The idea is that features in high-dimensional biological big data exhibit some redundancy, and redundant features can be represented by other features. Therefore, it is only necessary to find a subset of features, from which the entire dataset can be generated. This subset may represent more essential and important features. Using this subset for subsequent work can resist certain noise and reduce complexity, thereby improving algorithm performance. The paper presents a framework that can use linear models and non-linear neural network models to select feature subsets. Experiments compared the proposed method with popular unsupervised feature selection methods, using KMeans, KNN, and SVM as classification methods to classify the original data and the data after feature selection, and analyzed the results. The experiments ultimately demonstrated that the proposed method in this paper outperforms other popular unsupervised feature selection methods on multiple biological datasets.

关键词： Support vector machines Correlation Noise Neural networks Linear regression Termination of employment Resists Nearest neighbor methods Feature extraction Classification algorithms

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A Comprehensive Evaluation of Driver Drowsiness Identification System using Camera based Improved Deep Learning Methodology 9

A Comprehensive Evaluation of Driver Drowsiness Identificati...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Malathi, P. Sivakami Sundari, M. Karkuzhali, S. Umamaheswari, B. Thenmozhi, A. Aiyyasamy, Velu Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India OMR Chennai Agni College of Technology Department of Mathematics Tamil Nadu Chennai India R.M.K. Engineering College Department of General Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Kings Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi Sundararajan Engineering College Department of M.E.- Applied Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350384208

Particularly on highways, drowsy driving causes a large number of collisions while driving. With the goal to identify driver sleepiness while enhancing roadway security, it is now vital to grasp the situation and take early remedial steps. Using a proposed Image based Learning Strategy for Drowsiness Identification (ILSDI) and cross-validation with the conventional model called Convolutional Neural Network (CNN), the suggested framework offers an approach to assess the degree of fatigue among drivers according to modifications in a driver eyeballs motion. This will assist in deal with the problem concerning roadway protection. In addition, four types of expressions on the face were identified and categorized - open, closed, blinking, and no gazing using ILSDI and CNN models, indicating levels of tiredness. Finding, following, and analyzing the driver's face and eyes in real-time to calculate a sleepiness index is the goal of this technology, which operates in different lighting circumstances. Avoiding these kinds of accidents is possible with the help of a driver sleepiness monitoring structure, which uses a digital camera and accompanying software to measure the rate of blinking as well as the dimension of the driver's eyes. The driver sleepiness recognition system can identify when the driver is getting sleepy and sound an alarm if necessary. It is based on an offline implementation of a deep learning algorithm that uses ILSDI and CNN. © 2023 IEEE.

关键词： Convolutional neural networks

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