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4th International Conference on Advancements in Computing, ICAC 2022

作者： Eishan Dinuka, W.H.A. Wickramasinghe, A.Y.S.W. Weerasinghe, W.S.H. Karunaratne, K.P.G. Liyanapathirana, Chethana Rupasinghe, Lakmal Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users' feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users' preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly. © 2022 IEEE.

关键词： Sales

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Multi Model Approach to Evaluate and Enhance Student - Teacher Interactions 5

Multi Model Approach to Evaluate and Enhance Student - Teach...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Daksith, Nisal Wanaguru, Sathsara Kolonne, Uresha Dolawatta, Amaya Abeywardhana, Lakmini Kasthurirathna, Dharshana Sri Lanka Institute of Inoformation Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Inoformation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Advancements in artificial intelligence (AI) and machine learning (ML) have enabled the development of tools to address issues in Virtual Classrooms. This research focuses on utilizing AI and ML to monitor student behavior in virtual classrooms by analyzing facial expressions, background noise, and keystroke patterns. An AI based approach is proposed to analyze real-time webcam data, detecting patterns of inattention, and notifying teachers accordingly. The application also monitors background noise levels, identifying non-conducive learning environments, and analyzes keystroke patterns to detect unrelated activities. The research draws from academic literature to evaluate the effectiveness of the application while considering ethical implications such as potential bias and student privacy protection. Overall, this study contributes to the existing literature on AI and ML-based applications in education, demonstrating how these technologies can revolutionize student behavior monitoring, leading to enhanced outcomes for virtual classroom learning. © 2023 IEEE.

关键词： Machine learning

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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Analyzing Tools and Techniques for Evaluating Requirements Traceability 25

Analyzing Tools and Techniques for Evaluating Requirements T...

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25th International Multi Topic Conference, INMIC 2023

作者： Hassan, Shoaib Li, Qianmu Yasin, Affan Zubair, Muhammad Nanjing University of Science and Technology School of Computer Science and Engineering Jiangsu Nanjing210094 China Northwestern Polytechnical University School of Software Shaanxi Xian710072 China University of Central Punjab Faculty of Information Technology & Computer Science Lahore Pakistan

ISBN: (纸本)9798350317701

Requirements change throughout the software development lifecycle, from requirement elicitation and analysis to software operation. software requirements can be traced back to their source and shown to depend on one another. According to studies, the tools and techniques for the present traceability methodologies need to be revised to prevent the practical application of traceability. We analyzed evaluation techniques and requirements traceability tools in this article to determine whether evaluation techniques are truly being used and supported by software tools. As a result, we noted no connection between the approaches and tools examined and that some criteria, such as stakeholder communication, difficulty of tools, and requirement stability, needed to be better considered in the evaluated tools. © 2023 IEEE.

关键词： Requirements engineering

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer Systems science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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A Classification Technique for Online Shoppers' Purchasing Intention 1

A Classification Technique for Online Shoppers' Purchasing I...

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1st International Conference on Logistics, ICL 2024

作者： Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Fadel, Ali Hussein Abbas, Ali Hashim Jaber, Mustafa Musa Ali, Rabei Raad Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor86400 Malaysia School of Information Technologies and Engineering ADA University BakuAZ1008 Azerbaijan University of Diyala Department of Computer Science Diyala32001 Iraq Imam Ja'afar Al-Sadiq University Department of Computer Engineering and Information Technology AI-Muthanna Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10021 Iraq Northern Technical University Department of Computer Engineering Technology Mosul41000 Iraq

ISBN: (纸本)9798331518844

Online shopping has become a recent outburst using electronic devices that allow consumers to shop online, as there are factors that influence shopping intention. The factors include consumers' attitudes, subject norms, behavior control, perception of usefulness, considered risks, and trust. This study aims to classify the intent of online consumers' shopping. Subsequently, based on these measures, this classification can provide fundamental and prioritized suggestions for performance improvements in online shopping. The classification is performed by using four data mining algorithms, which are random forest (RF), decision tree (DT), support vector machines (SVMs), and k-nearest neighbors (k-NN). Classification of online shoppers has been implemented based on the online shopping behavior dataset and critical performance metrics, including accuracy, precision, recall, and F1-measure. Essential factors have been considered in the online shopping behavior dataset that was used. The dataset has been preprocessed to check missing values and change the data to an appropriate numerical type. Then, the classification algorithms are applied to extract the required results. The results of detecting consumer shopping behavior showed that RF has outperformed other algorithms and achieved an average accuracy of 90.24%. The average classification accuracies of the DT, SVM, and k-NN are 89.59%, 89.38%, and 87.06%, respectively. The result of this work is hoped to assist in identifying online shoppers' behavior categories in practical aspects. © 2024 IEEE.

关键词： Support vector machines

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Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

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computers, Materials & Continua 2022年第3期70卷 5039-5058页

作者： Khuram Nawaz Khayam Zahid Mehmood Hassan Nazeer Chaudhry Muhammad Usman Ashraf Usman Tariq Mohammed Nawaf Altouri Khalid Alsubhi Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electronics Information and BioengineeringPolitecnico di MilanoMilano20122Italy Department of Computer Science University of Management and TechnologySialkotPakistan College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia Department of Computer Science Imam Mohammad Ibn Saud Islamic UniversityRiyadh13318Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah21589Saudi Arabia

Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and *** a single solution to solve all these problems at a time is a challenging *** have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid *** technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching ***,the input image is recognized using a robust kernel representation method using extracted *** qualitative and quantitative analysis of the proposed method is carried on benchmark image *** results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.

关键词： Face recognition local tetra patterns spatial pyramid matching robust kernel representation max-pooling

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Heart Disease Prediction Using Machine Learning 2

Heart Disease Prediction Using Machine Learning

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Kamal, Haram Bakhtawar Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Mustafa, Muzzamil Yaqub, Muhammad Atif Umar, Hooria Fatima, Hoor Nasir, Usama Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Department of Computer Science Lahore Campus Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan University of Management & Technology Department of Artificial Intelligence Lahore Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Pakistan Bahria University Pakistan

ISBN: (纸本)9798331528713

The heart plays a pivotal role in the functioning of living organisms, making its diagnosis and prediction of related diseases a matter of utmost importance. Approximately 17.9 million individuals succumb to cardiovascular disease, accounting for 32% of worldwide fatalities. This global concern highlights the importance of early detection, as timely treatment can significantly reduce heart disease-related mortality. Errors in diagnosis can lead to severe consequences, including fatigue or even death. The increasing prevalence of heart-related diseases necessitates the development of precise prediction systems to enhance awareness. Machine learning, a subset of Artificial Intelligence (AI), offers robust tools for predicting various events based on patterns learned from natural occurrences. This research paper evaluates the accuracy of machine learning algorithms, specifically k- nearest neighbor, decision tree, linear regression, and support vector machine (SVM), in predicting heart disease. We utilize the UCI repository dataset for training and testing and employ the Python programming language through the Anaconda Jupyter notebook for implementation and the experimental findings indicate an accuracy rate of 95.7% using the heart disease prediction model, leveraging its extensive libraries and header files to ensure accuracy and precision in our analysis. © 2024 IEEE.

关键词： Random forests

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

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Intelligent Automation & Soft Computing 2023年第4期36卷 169-181页

作者： Muhammad Irfan Khalil Ullah Fazal Muhammad Salman Khan Faisal Althobiani Muhammad Usman Mohammed Alshareef Shadi Alghaffari Saifur Rahman Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Software Engineering University of MalakandDir LowerPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG *** an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare *** this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modiﬁed version of mel fre-quency cepstral coefﬁcients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ﬁcient,hurst exponent,and root mean *** state-of-the-art classiﬁers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted ***-based analyses of these classiﬁers have also been considered based on total classi-ﬁcation accuracy,prediction speed(observations/sec),and processing *** proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG *** is used as our main classiﬁer for the detection of noisy channels which gives a total classiﬁcation accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

关键词： Machine learning surface electromyography support vector machine classiﬁcation features

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