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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Thalapala, Vidya Sagar Guravaiah, Koppala Department of Computer Science & Engineering Indian Institute of Information Technology Kerala Kottayam686635 India

The established SDN multi-controller architecture faces the challenge of locating controllers to boost the scalability, reliability, and security of the network. Reliability is one of the important metric to handle when a failure event occurs in a network. In this paper, a Whale Optimization technique is used for controller placement and proposed Whale Optimization based Controller Placement (WOCP), that places the controllers in an optimized and efficient manner. The adequate placement of controllers minimizes the overall average latency with a high reliability index. The WOCP method resemblances with Minimizing Latency for Controller Placement (MLCP), resulting in an 18% reduction in latency in the Geant network topology. © 2025 The Authors. Published by Elsevier B.V.

关键词： Software reliability

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computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

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computer Systems science & engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： Precision agriculture smart farming weed detection computer vision deep learning

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Establishing a Secure Mobile Online Banking System using AES 3

Establishing a Secure Mobile Online Banking System using AES

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Karpagalakshmi, R.C. Lenin, J. Robin Rohit, V. Saravanan, K. Rajesh Sharma, R. Banshal, Sumit Kumar Alliance College of Engineering and Design Alliance University Department of Computer Science and Information Technology Bangalore India School of Computer Science & Engineering and Information Science Presidency University Bangalore India New Horizon College of Engineering Department of Information Science Engineering Bangalore India

ISBN: (纸本)9798331518592

Using a mobile phone or other portable device to conduct financial transactions online is known as 'mobile banking.' Activities such as verifying one's account balance, transferring funds to another account, making bill payments, and finding a local automated teller machine (ATM) are included under this classification. Due to the significant expenses associated with implementing this technology, Authentication of users, data privacy, non-repudiation, data integrity, and obtainability of services are all areas where people have voiced concerns. The analysis described in this paper introduces a more advantageous and convenient framework for addressing these issues. The proposed approach effectively accomplishes its intended goals via the use of a widely adopted technique known as salting, which involves the application of the Secure Hash Algorithm (SHA512) to obfuscate sensitive information such as user IDs and passwords. The encoding and decoding procedures used the Advanced Encoding Standard (AES) technique while implementing one-time passwords (OTPs) was implemented to enhance user authentication. The design approach used PHP, JScript, CSS, and a MySQL database. The password recovery technique was used to assess the model's credibility and reliability and acquire the outcomes. This program enhances the process of recovering passwords across various scenarios. It intercepts network traffic using dictionary attacks, brute-force assaults, and cryptanalytics attacks to decrypt encrypted passwords. Additionally, it uncovers password bins, retrieves secreted passwords and analyses routing protocols. The gathered findings support the idea's feasibility since the encrypted and hashed data demonstrates resistance to unauthorized access. © 2024 IEEE.

关键词： Automatic teller machines

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach 6

Enhancing Soft Skills in Autistic Children: The Next Generat...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Peiris, Kalna Nelaka, Ruchira Manathunga, Kalpani Department of Computer Systems Engineering Sri Lanka Institute of Information and Technology Sri Lanka Department of Information Technology Sri Lanka Institute of Information and Technology Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information and Technology Sri Lanka

ISBN: (纸本)9798331517878

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively. © 2024 IEEE.

关键词： Economic and social effects

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A Secure Multi-factor Authentication Protocol for Healthcare Services Using Cloud-based SDN

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computers, Materials & Continua 2023年第2期74卷 3711-3726页

作者： Sugandhi Midha Sahil Verma Kavita Mohit Mittal Nz Jhanjhi Mehedi Masud Mohammed A.AlZain Department of Computer Science and Engineering Chandigarh UniversityMohali140413India Chandigarh Engineering College Jhanjeri Mohali140307India School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500Malaysia FITTaylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the *** and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application *** integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care *** has improved the real-time monitoring of patients by medical ***’data get stored at the central server on the cloud from where it is available to medical practitioners in no *** centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients’*** recent days,several schemes have been proposed to ensure the safety of patients’*** most of the techniques still lack the practical implementation and safety of *** this paper,a secure multi-factor authentication protocol using a hash function has been ***(Body Area Network)logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient *** Protocol Animator(SPAN)–Automated Validation of Internet Security Protocols and Applications(AVISPA)tool has been used for *** results prove that the proposed scheme ensures secure access to the database in terms of spoofing and *** comparisons of the proposed scheme with other related historical schemes regarding time complexity,computation cost which accounts to only 423 ms in proposed,and security parameters such as identification and spoofing prove its efficiency.

关键词： Multi-factor authentication hash function BAN logic SPANAVISPA

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Enhancing real time object detection for autonomous driving using YOLO-NAS algorithm with CLEO optimizer

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International Journal of information technology (Singapore) 2025年第3期17卷 1321-1328页

作者： Nandal, Priyanka Pahal, Sudesh Malik, Sunesh Sehrawat, Neetu Mamta Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Electronics and Communication Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Maharaja Surajmal Institute of Technology New Delhi India Department of Computer Science and Engineering Panipat Institute of Engineering & Technology New Delhi India

For autonomous driving to operate in a safe and effective manner, efficient and precise object detection is essential. The efficacy of the network model is heavily challenged because of the high-speed movement of vehicles and the dynamic nature of the surrounding environment that need the detection of objects of various scales. With notable improvements over earlier iterations, the You Only Look Once-Neural Architecture Search (YOLO-NAS) algorithm is a strong contender for improving real-time object recognition in autonomous vehicles. YOLO-NAS integrated with chimpanzee leader election optimization (CLEO) has the potential to greatly enhance the safety and dependability of autonomous driving systems by utilizing its sophisticated capabilities and skill fully combining it with the vehicle’s perception system. The BDD100K dataset serves as the training and evaluation set for our suggested technique. The YOLO-NAS model with CLEO optimizer achieves mean average precision (mAP) of 83%, which is significantly higher than the mAP of 65.7% attained by the YOLO-NAS model without the optimizer. This improvement highlights the effectiveness of the CLEO optimizer in enhancing detection precision. The proposed approach was compared with existing algorithms and demonstrated remarkable performance in terms of recall, precision, and mAP. It is seen that our proposed model is better by striking equilibrium between detection speed and accuracy. Based on our findings, object detection performance for autonomous driving applications is much enhanced when utilizing our suggested approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Autonomous vehicles Deep learning Object detection YOLO-NAS

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Real-time load balancing and dynamic profile management in mobile data networks

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International Journal of Vehicle information and Communication Systems 2023年第1-2期8卷 33-49页

作者： Dureja, Ajay Dureja, Aman Suman Pahwa, Payal Department of Information Technology Bharati Vidyapeeth’s College of Engineering Paschim Vihar Delhi India Department of Information Technology Bhagwan Parshuram Institute of Technology Sector-17 Rohini Delhi India Department of Computer Science & Engineering Deenbandhu Chhotu Ram University of Science and Technology Haryana Murthal India Department of Computer Science & Engineering Bhagwan Parshuram Institute of Technology Sector-17 Rohini Delhi India

As mobile data grows rapidly, carriers must intelligently manage network traffic. Due to economic realities and the physical limitations of the available spectrum, operators cannot add more network capacity. To satisfy investors, operators must maximise their service returns. The more services a subscriber receives, which costs more, the higher their expectations of acceptable network availability and quality. So it becomes very challenging for operators to provide QoS to their high-value subscribers and manage network traffic dynamically without impacting the user experience of their potential and key users. In this paper, all literature surveys are carried out on different schemes and solutions to manage mobile data networks’ profiles. This paper aims to identify an effective way of managing real-time data traffic by adjusting QoS/profile parameters dynamically so that high-value subscribers get better QoS during congestion than regular users. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Quality of service

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