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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Hammed, S Shahul Preethi, C. Haripriya, K. Pavalarajan, S. Gowtham, M.R. Krithick, N. Akshaay PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331505745

Urbanization has led to increased traffic congestion and air pollution, primarily from vehicle emissions, posing risks to public health and the environment. Existing traffic management systems are inefficient in integrating real-time pollution data, leading to reactive control measures. This project proposes an IoT-based smart traffic management system that integrates real-time air quality monitoring with dynamic traffic control. IoT sensors collect pollution and vehicle data, which is processed using cloud computing and machine learning to optimize traffic flow and reduce emissions. AI techniques provide transparency, helping city authorities understand and trust the system's decisions. The system also offers predictive analytics for proactive pollution management and a user-friendly dashboard for real-time visualization. The solution aims to reduce urban emissions, and enhance traffic efficiency, offering a sustainable approach for modern cities. © 2025 IEEE.

关键词： Traffic congestion

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On Eye Tracking in Software Engineering

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SN computer science 2024年第6期5卷 729页

作者： Grabinger, Lisa Hauser, Florian Wolff, Christian Mottok, Jürgen Laboratory for Safe and Secure Systems Technical University of Applied Sciences Regensburg Faculty of Computer Science and Data Science University of Regensburg

Eye tracking is becoming more and more important as a research method within the field of software engineering (SE). Existing meta-analyses focus on the design or conduct of SE eye tracking studies rather than the analysis phase. This article attempts to fill this gap;it presents a systematic literature review of eye tracking studies in the field of SE—focusing mainly on the data analysis methods used. From the IEEE Xplore and ACM digital libraries we gather 125 papers up to the first quarter of 2024. Detailed evaluation provides information on the number of papers that use specific methods of analysis (i.e., descriptive or inferential statistics, and gaze visualization) or settings (e.g., sample size, technical setup, and selected aspects of research design). With the data obtained we can infer the popularity of specific analysis methods in the field. Those results enable efficient work on data analysis tools or education of aspiring researchers and can serve as basis for standardization or guidelines within the community—providing for methods to include as well as current inconsistencies. © The Author(s) 2024.

关键词： Empirical Eye tracking Software engineering Systematic literature review

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Competing with autonomous model vehicles:a software stack for driving in smart city environments

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Autonomous Intelligent systems 2024年第1期4卷 166-178页

作者： Julius Bächle Jakob Häringer Noah Köhler Kadir-KaanÖzer Markus Enzweiler Reiner Marchthaler Institute for Intelligent Systems Faculty of Computer Science and EngineeringEsslingen University of Applied SciencesFlandernstraße 101Esslingen am Neckar73732Germany

This article introduces an open-source software stack designed for autonomous 1:10 scale model *** developed for the Bosch Future Mobility Challenge(BFMC)student competition,this versatile software stack is applicable to a variety of autonomous driving *** stack comprises perception,planning,and control modules,each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of *** the limited computing power of model vehicles and the necessity for low-latency real-time applications,the stack is implemented in C++,employs YOLO Version 5 s for environmental perception,and leverages the state-of-the-art Robot Operating System(ROS)for inter-process *** believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student *** work can serve as a foundational tool for novice teams and a reference for more experienced *** code and data are publicly available on GitHub.

关键词： Autonomous model vehicle Software architecture Embedded real-time systems Bosch Future Mobility Challenge Autonomous driving

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Recommendation systems for Supermarket 3

Recommendation Systems for Supermarket

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Bhagampriyal, M. Gowtham, R. Johnson, Jeril Lilian, J. Felicia Suganthi, P. Thiagarajar College of Engineering Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350336238

Numerous recommender systems offer recommendations to users depending on their interests. Several applications including those in e-commerce, healthcare and markets have adopted recommendation systems. This paper's major goal is to demonstrate various difficulties with the methods utilized for producing suggestions. The aim is to explore and develop recommendation algorithms using past sales data. It covers the ideas behind content-based filtering, hybrid model recommendation, collaborative filtering and association rules for recommendation systems. For Product recommendation, three algorithms are used: the popularity product recommendation algorithm, the frequent pattern growth algorithm and Apriori algorithm. To increase sales and market response, the proposed recommendation model yields good recommendation outcomes. Additionally, it suggests particular products to potential clients. This paper describes the research environment for recommendation systems in grocery stores. From the observed recommendation models, Popular Product Recommendation does not require customer analysis. Among the models that compare the customer behavior, FP Growth serves better than Apriori model due to its faster execution time. This paper provided a very relevant and practical business transformation scenario that helps businesses in comparable circumstances change their business models. © 2023 IEEE.

关键词： Recommender systems

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Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

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computers, Materials & Continua 2023年第5期75卷 3065-3089页

作者： Ali Arshad Muhammad Nadeem Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Zaid Alzaid Rana Alabdan Badr Almutairi Sultan Almotairi Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic university of MadinahMedinah42351Saudi Arabia Department of Information Systems Faculty of Computer and Information Sciences CollegeMajmaah University11952Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah 11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an *** cryptography is the best way to transmit data in a secure and reliable *** researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data *** algorithm has some data security *** some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original *** this paper,various data security techniques are developed,which can be used to protect the data from attackers ***,a customized American Standard Code for Information Interchange(ASCII)table is *** value of each Index is defined in a customized ASCII *** an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the *** that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original *** the number of cipher values is double the original data,the attacker tries to decrypt each *** of getting the original data,the attacker gets such data that has no relation to the original *** that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain *** boundaries of each Hill text work up to that *** techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorit

关键词： Cryptography symmetric cipher text encryption matrix cipher encoding decoding hill matrix 64-bit encryption

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

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computers, Materials & Continua 2023年第2期74卷 3333-3350页

作者： Akram Ali Alhadad Omar Tarawneh Reham R.Mostafa Hazem M.El-Bakry Information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Computer Science Department Ibb UniversityIbbYemen SE Department Faculty of Computer Sciences and InformaticsAmman Arab UniversityAmman11953Jordan

COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in *** the infected people is the most important factor in the fight against the *** gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative *** Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent *** this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose *** is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT *** the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of *** attention with DSVDD naively may cause model *** in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing *** approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as *** models learn in an end-to-end *** proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 *** evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced *** proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.

关键词： Deep learning deep SVDD residual attention anomaly detection COVID-19 Coronavirus

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Sell and Buy Homegrown Vegetables and Fruits Online Using E-Commerce UML Algorithm

Sell and Buy Homegrown Vegetables and Fruits Online Using E-...

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2023 International Conference on Intelligent systems for Communication, IoT and Security, ICISCoIS 2023

作者： Preethi, C. Saran, V.S. Shree Meikannan, M. Hammed, S.Shahul Haripriya, K. Kumar, B.Arun PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350335835

E-commerce, also known as electronic commerce or internet commerce, refers to the exchange of money and data for the purpose of business operations through the internet. The term 'ecommerce' is commonly used to refer to the online sale of tangible goods, but it may also refer to any type of commercial transaction made possible via the internet. It is nowadays one of the most important components of the internet. Electronic commerce is the process of conducting business using computer networks. An individual sitting in front of a computer may use all of the Internet's resources to purchase or sell things. E-commerce, which began in the early 1990s, has made enormous strides in the world of computers. B2B e-commerce is used to increase the usage of e-commerce in developing nations by enhancing access to global markets for enterprises in developing countries. Regardless of the rapid growth of technology, e-commerce has reached its apex. This article proposes a novel application concept. It describes the public's needs for M-Commerce, as well as the analysis and literacy survey of essential components of mobile devices that use such apps. The design and security of the application are both carefully studied. This study examines the characteristics and possibilities of a mobile E-App for selling and purchasing fresh vegetables. The outcomes demonstrate how the application has impacted the public, employment, and long-Term growth. © 2023 IEEE.

关键词： Developing countries

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