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2024 International Conference on E-Mobility, Power Control and Smart systems, ICEMPS 2024

作者： Patil, Sachin C. Kasula, Balaram Yadav Mohammed, Vazeer Ali Gupta, Ketan Thamaraimanalan, T. United States University of The Cumberlands Dept of Information Technology WilliamsburgKY United States Lewis University Dept. of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350394399

Genetic Algorithms have been more and more employed to stumble on congenital heart defects in the last variety of years. Gasoline is a computational technique that uses evolutionary concepts to optimize answers to complex problems. This study proposes the use of Genetic Algorithms as a novel approach for detecting Congenital Heart Defects a common and potentially life-threatening condition affecting newborns. By incorporating genetic principles and simulations, GAs have proven to be effective in solving complex medical diagnosis problems. The study aims to optimize the classification process by employing evolutionary techniques to identify key features and prioritize them based on their importance in diagnosing CHDs. Results from the GA model showed a significant improvement in accuracy and speed compared to existing methods, making it a promising tool for early detection and intervention of CHDs in newborns. But gas is more appropriate for this assignment because of its potential to pick out extra subtle variations between ordinary and abnormal cardiovascular anatomy. Gas has additionally been established to be promising within the identity and quantification of CHDs. © 2024 IEEE.

关键词： Genetic algorithms

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Extending and Evaluating Usability Heuristics for Educational Website in Fiji

Extending and Evaluating Usability Heuristics for Educationa...

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2023 IEEE Asia-Pacific Conference on computer Science and Data Engineering, CSDE 2023

作者： Sami, Monesh Devi, Pritika Ali, A. B. M. Shawkat Kumar, Anal Fiji National University Department of Computer Science and Information Systems Nadi Fiji Fiji National University Department of Office Administration/Library and Information System Nadi Fiji School of Science and Technology The University of Fiji Saweni Fiji

ISBN: (纸本)9798350341072

Learning Management systems (LMS) has recently gained a lot of popularity as an effective tool for improving education while promoting distance and flexible learning in Fiji. eLearning platforms should be able to possess usability features, including user-friendliness, ease of use and satisfaction of user requirements. With the diverse number of universities and courses globally, LMS websites created revolutionary changes in the educational sector. With these innovations, it is vital to ensure the effectiveness of educational sites by evaluating the usability interfaces, as there is minimal study on the effectiveness of websites, especially for LMS. This research intends to fill this gap by extending and evaluating the usability heuristics of educational website interfaces for better detection of usability problems, simultaneously running a survey to determine Moodle usage on different devices among different users in Fiji. © 2023 IEEE.

关键词： Human computer interaction

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The Spreading Prediction and Severity Analysis of Blood Cancer Using Scale-Invariant Feature Transform

The Spreading Prediction and Severity Analysis of Blood Canc...

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2023 IEEE International Conference on Network, Multimedia and information technology, NMITCON 2023

作者： Mohammed, Vazeer Ali Mohammed, Mehmood Ali Mohammed, Murtuza Ali Ramakrishnan, Rakesh Logeshwaran, J. Lewis University Department of Computer & Information System IL United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States University of the Cumberlands Department of Information Technology Union CityCA United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350300826

Blood cancer is an ever-growing global health concern. Early detection and accurate diagnosis are key to successful treatment and long-term prognosis of the disease. Despite various advances in medical technologies, current methods for blood cancer detection are far from perfect and often suffer from a lack of accuracy. This paper proposes the use of Scale-Invariant Feature Transform (SIFT) based approaches for predicting the spreading of blood cancer and assessing its severity. SIFT is a computer vision technique that extracts meaningful features from an image. Through SIFT, meaningful patterns in medical imaging datasets, such as those used for blood cancer detection, can be identified. The output from SIFT can then be used to build predictive models which can accurately predict the spread of the disease and its severity. Additionally, this paper explains how machine-learning algorithms and artificial neural networks can be used to further enhance the accuracy of these predictions. Finally, the proposed approach is validated using benchmark datasets and its accuracy is compared with existing methods for blood cancer prediction. The proposed model reached 89.71% accuracy, 89.25% precision, 90.71% recall and 89.40% f1-score results. The results show that prediction accuracy increases significantly when SIFT is used. It is concluded that SIFT can be a powerful tool for predicting blood cancer and estimating its severity. © 2023 IEEE.

关键词： Diseases

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Significance of Internet-of-Things Edge and Fog Computing in Education Sector

Significance of Internet-of-Things Edge and Fog Computing in...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Zeba Rana, Nadim Khan, Haneef Alam, Md Imran College of Cs It Jazan University Department of Computer Science Jazan Saudi Arabia College of Cs It Jazan University Department of Computer and Network Engineering Jazan Saudi Arabia Computer and Information System Applied College Jazan University Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350347050

IoT gadgets include smart lights, ovens, and industrial data-collecting equipment. IDC expects 41.6 billion connected IoT devices to generate 79.4 ZB of data by 2025. Early IoT devices sent data to the cloud for analysis. When sending trillions of gigabytes to the cloud, the data pipeline clogs. Edge computing enables IoT devices to handle data locally instead of sending it to the cloud. Data is processed at the 'edge' of your local network, rather than transferred away. technology's versatility makes it valuable in education. IoT edge computing has different requirements than non-IoT edge computing. IoT devices have limited data processing and storage capabilities, thus processing must occur off the device. The edge offers an environment to manage huge numbers of IoT devices and data. Many functions can be off-loaded to the edge, reducing device cost. In education, IoT edge computing architecture has gained importance due to real-time applications. In order to facilitate the analysis of educational system architecture, this paper seeks to provide an IoT Edge computing framework in the field of education. The framework will identify IoT-related issues and concerns. This study discusses IoT Edge computing in education, identifies limits, and makes recommendations. As well as the paper evaluates IoT Edge computing's performance in educational systems. © 2023 IEEE.

关键词： Edge computing

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A Novel Predictive Analysis to Identify the Weather Impacts for Congenital Heart Disease Using Reinforcement Learning

A Novel Predictive Analysis to Identify the Weather Impacts ...

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2023 IEEE International Conference on Network, Multimedia and information technology, NMITCON 2023

作者： Mohammed, Mehmood Ali Ramakrishnan, Rakesh Mohammed, Murtuza Ali Mohammed, Vazeer Ali Logeshwaran, J. University of the Cumberlands Department of Information Technology WilliamsburgKY United States University of the Cumberlands Department of Information Technology Union CityCA United States Lewis University Department of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350300826

Reinforcement learning is a powerful approach for predictive analysis to identify the weather impacts for congenital heart disease. The key advantages of this method include the utilization of sensor data to predict the weather conditions on a daily basis and the ability to learn from feedback and adapt the predictions over time. In this paper, an innovation model has proposed by using reinforcement learning algorithm. It can gain important insights regarding the impact of weather on congenital heart disease. The predictive models developed using this approach have the potential to help medical professionals as well as the general public in better predicting and managing congenital heart disease. This could help reduce costs associated with care and ultimately improve health outcomes. © 2023 IEEE.

关键词： Predictive analytics

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Chest X-rays Analysis for Lung Disease Prediction and Interpretation Using Transfer Learning

Chest X-rays Analysis for Lung Disease Prediction and Interp...

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International Conference on information and Communication systems (ICICS)

作者： Nour Iqtaish Duha Makhamdih Abeer Zanoun Heba Alawneh Department of Computer Information System Jordan University of Science and Technology Department of Cybersecurity Jordan University of Science and Technology

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

Pneumonia, responsible for 16% of deaths among children under 5, is a severe lung infection primarily caused by bacteria or viruses. Diagnosis typically relies on manual examination of chest X-ray images. Leveraging computer vision and deep learning can expedite the diagnostic process, potentially saving lives. This study aims to enhance pneumonia diagnosis by introducing a robust image classifier using transfer learning techniques. We deployed popular convolutional neural network models, including VGG16 and ResNet50, for this purpose. Additionally, we utilized Gradient-weighted Class Activation Mapping (Grad-CAM) to assist doctors in result interpretation. Our dataset was sourced from the Guangzhou Women and Children’s Medical Center. Our approach outperformed state-of-the-art methods with remarkable accuracies of 95.99% for ResNet50 and 96.53% for VGG16.

关键词： Deep learning Pediatrics Pneumonia Accuracy Scalability Transfer learning Medical services

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

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computers, Materials & Continua 2022年第12期73卷 5491-5507页

作者： Mashael M.Asiri Saud S.Alotaibi Dalia H.Elkamchouchi Amira Sayed A.Aziz Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained *** offers independent access to the target region and performs data collection in an effective *** energy constraints remain a challenging issue in WSN since it operates on in-built *** studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of *** organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic *** and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of *** this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for ***,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of ***,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the *** clustering and routing approaches derive a fitness function with the inclusion of multiple input *** proposed IDTOMHR model was experimentally validated for its performance under different *** extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.

关键词： Wireless sensor network clustering routing metaheuristics energy efficiency lifetime

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer systems Science & Engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

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computer systems Science & Engineering 2023年第5期45卷 1679-1694页

作者： Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application ***,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication *** traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find *** study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)*** major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT *** the preliminary stage,data pre-processing is carried out to transform the input data into useful *** addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection ***,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of ***,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization *** experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several *** simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

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