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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah almujally Saud S.alotaibi Abdulwahab alazeb Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

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computer Systems Science & engineering 2023年第4期45卷 869-886页

作者： Sirisati Ranga Swamy SPhani Praveen Shakeel Ahmed Parvathaneni Naga Srinivasu Abdulaziz alhumam Department of CSE Vignan’s Institute of Management and Technology for Women GhatkesarTelanganaIndia Department of CSE PVPSITVijayawadaAndhra Pradesh520007India Department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science and Engineering-AIML VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at *** pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already *** established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the *** problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical *** models help predict the effects of the disease outbreak and help detect the effects in the coming *** this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan *** are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from *** current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better *** model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion *** is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

关键词： Chest CT COVID-19 classification ROC curves multi-feature disease analysis

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Leveraging IoT Data for Real-Time Business Decision-Making 8

Leveraging IoT Data for Real-Time Business Decision-Making

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Abdualwhab, Muthana Gareed, Jumaa al-Shimary, ali J. Furaijl, Hussein Basim Ahmed, Saadaldeen Rashid algburi, Sameer College of Financial and Administrative Sciences University of Al Maarif Department of Business Administration Al Anbar31001 Iraq Al-Ma'moon University College Department of Finance and Banking Sciences Baghdad Iraq Al Hikma University College Baghdad Iraq College of Pharmacy University of Al-Ameed PO Box 198 Karbala Iraq Bayan University Computer Science Department Kurdistan Erbil Iraq College of Engineering Al-Ayen University Artificial Intelligence Engineering Department Thi-Qar Iraq Al-Kitab University Kirkuk36015 Iraq

ISBN: (纸本)9798350354423

This study intends to examine how the IoT data may help the real-time corporate decision-making utilizing the machine learning models, specifically the Random Forest, to forecast the need for equipment repair. The study entailed the use of a quantitative technique that involved the acquisition of IoT sensor data of industrial equipment. The data was obtained and processed with the use of complicated analytic techniques to develop the prognosis of maintenance. Some of the studies suggest that the Random Forest model efficiently anticipates maintenance needs, thus minimizing downtime and operational expenses. However, the performance of the model is highly impacted by the quality of the data, and if there is missing or erroneous sensor data, the accuracy is impaired. These findings also stress the need for a thorough handling and inspection of the data that is given into the model. The ramifications of this study are huge for enterprises since the integration of IoT sensors and data analytics into organizational processes may result in enhanced decision-making, greater organizational performance, and lower costs. This research adds to developing understanding of IoT and machine learning in business and how these technologies may revolutionize decision-making. The research is a clear indicator of the necessity to keep a careful eye on IoT and ensuring that the data being fed into the system is of the highest quality in order to achieve the promise of the technology to deliver on operational benefits. © 2024 IEEE.

关键词： Predictive maintenance

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Energy Efficiency in 5G Networks Using Gaussian Mixture Models

Energy Efficiency in 5G Networks Using Gaussian Mixture Mode...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： alsalami, Zaid Kasthuri, R. Sowmiya, S. Rani, Pooja Gupta, Deepak Kumar Gowtham Kumar Reddy, I.V. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Sri Ramakrishna Engineering College Department of Mathematics Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali140307 India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

In this research, a new approach for improving energy efficiency of the networks in the 5G context is presented: utilizing the Gaussian Mixture Models. The process includes acquiring data in the first step, training the model in the second step, and using the model to make real-time decisions to minimize resources and inefficient energy consumption in the last step. This includes the collection of data on context and complete flow details of the course of traffic passage as well as actions committed by users. The GMM is then trained using the Expectation-Maximization (EM) algorithm for developing the model and reinforcement learning for real-time decision-making on the developed model. In case studies analyzed, there is evidence of the enhancement of energy consumption by 14% on average absolute reduction in energy per traffic unit, by 11% of the average absolute increase in network throughput and by 49% of the mean absolute reduction of latency in comparison with baseline conditions. © 2024 IEEE.

关键词： Expectation maximization algorithm

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Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI

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computers, Materials & Continua 2021年第9期68卷 3825-3840页

作者： Ayesha Sarwar Kashif Javed Muhammad Jawad Khan Saddaf Rubab Oh-Young Song Usman Tariq National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Software Sejong UniversitySeoulGwangjin-guKorea College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Brain-computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware *** recorded brain data is converted directly to the machine that can be used to control external *** are four major components of the BCI system:acquiring signals,preprocessing of acquired signals,features extraction,and *** traditional machine learning algorithms,the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery *** major reason for this is,features are selected manually,and we are not able to get those features that give higher accuracy *** this study,motor imagery(MI)signals have been classified using different deep learning *** have explored two different methods:Artificial Neural Network(ANN)and Long Short-Term Memory(LSTM).We test the classification accuracy on two datasets:BCI competition III-dataset IIIa and BCI competition IV-dataset *** outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning *** the deep learning classifiers,LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.

关键词： Brain-computer interface motor imagery artificial neural network long-short term memory classification

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Time Management Recommendations with Bayesian Neural Networks: An Intelligent Assistant Approach

Time Management Recommendations with Bayesian Neural Network...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Kumar, Amit Soni, Nidhi Abduraximovich, Sulaymonov Dilshod al-Jawahry, Hassan M. Jayasree, S. Rajaram, Gnanajeyaraman Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Fergana Medical Institute of Public Health Department of Urology and Oncology Fergana Uzbekistan The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

Time management is an important aspect of our daily lives and this research aims at creating an intelligent assistant that will be able to support an individual in managing their time appropriately using application of the Bayesian Neural Networks (BNNs). Many of the conventional time management strategies are posed to be ineffective since they cannot be changed depending on the unique person and situations in a similar way personal organization systems cannot be changed to suit the unique person and situations. Therefore, the use of Bayesian neural networks (BNNs) to perform inference makes them ideal for this purpose given that BNNs use probability and Bayesian inference to work under uncertainty. The involved process involved utilization of effective data gathering methodologies to obtain comprehensive information on users' activities, preferences, and productivity to develop the BNN model. The plan is that the latter was integrated into the context of a mobile application where, similar to an intelligent agent of an organism, it adapts in real time and suggests the most efficient ways of planning the time. A number of user tests pointed at level 3: Most of the users reported high levels of satisfaction and ranked productivity gains throughout the different activities under study. Escalating accuracy was also affirmed by the result analyses owing to the continual refining of the model in subsequent phases. This work demonstrates the possibility of the proposed BNN approach to change time management approaches and present a more effective approach of time management in line with the need of the user. As such, future work will comprise a continuation of the model's development with the addition of new functions that will complement the existing features and make the tool even more useful and appealing to potential users. © 2024 IEEE.

关键词： bayesian neural network intelligence assistance approach neural network time management

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Design of Evolutionary algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

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computer Systems Science & engineering 2023年第10期47卷 1283-1297页

作者： Mohammed altaf Ahmed T.Satyanarayana Murthy Fayadh alenezi E.Laxmi Lydia Seifedine Kadry Yena Kim Yunyoung Nam Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka 72388Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of ICT Convergence Soonchunhyang UniversityAsan 31538Korea

Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to *** the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,which necessitate proper load balancing amongst the clusters and serves a wider monitoring *** clustering technique for WSN has several benefits:lower delay,higher energy efficiency,and collision *** clustering protocol has several *** a large-scale network,cluster-based protocols mainly adapt multi-hop routing to save energy,leading to hot spot problems.A hot spot problem becomes a problem where a cluster node nearer to the base station(BS)tends to drain the energy much quicker than other nodes because of the need to implement more *** article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems(JSOUCP-MHP)in *** JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search,persecution,and jumping skills to attack *** presented JSOUCPMHP technique mainly resolves the hot spot issue for maximizing the network *** JSOUCP-MHP technique elects a proper set of cluster heads(CHs)using average residual energy(RE)to attain *** addition,the JSOUCP-MHP technique determines the cluster sizes based on two measures,i.e.,RE and distance to BS(DBS),showing the novelty of the *** proposed JSOUCP-MHP technique is examined under several experiments to ensure its *** comparison study shows the significance of the JSOUCPMHP technique over other models.

关键词： Wireless sensor networks energy efficiency cluster heads unequal clustering hot spot issue lifetime enhancement

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Adaptive Beamforming in MIMO Systems Using Deep Learning

Adaptive Beamforming in MIMO Systems Using Deep Learning

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Vinithra Banu, T. Hussein, Layth Grover, Varsha Kumar, T. Arun Sharma, Rupesh Sudha, I. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Kongu Engineering College Department of Cse India Ies University Ies Institute of Technology and Management Department of Electronics & Communication Engineering M.P. Bhopal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

One of the most significant procedures in MIMO system is the adaptive beamforming which is the technique of adjusting the direction of signal transmission for better wireless communication. This paper aims to investigate the application of deep learning in combination with adaptive beamforming for the improvement of the existing technologies and techniques having some limitations which prove efficient when working in certain applications and scenarios only. Utilizing the CNNs and RNNs, the proposed approach then determines ideal beamforming patterns from various datasets, synthetic simulations, as well as measured ones. In this paper, various approaches are employed which include the Super & Reinforcement learning techniques, which makes the deep learning-based models capable of learning in real-time the change of channel characteristics. Simulations and practical adaptations of changing protocols have illustrated substantial enhancements on SINR, BER, and system capacity. The conclusion indicates that the proposed DL based technique is better than the existing standards providing more considerably higher data rates with reasonable less error rates and possessing highly adaptive capability. From the literature, it is clear that deep learning has a significant use in adaptive beam forming;although it has significantly increased the computational load. For future wireless communication technologies such as 5G and beyond MIMO systems this work has demonstrated the promises of deep learning to improve such systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer Science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network

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Mathematical Biosciences and engineering 2024年第4期21卷 5712-5734页

作者： Qamar, Hafiz Ghulam Murtza Qureshi, Muhammad Farrukh Mushtaq, Zohaib Zubariah, Zubariah Rehman, Muhammad Zia ur Samee, Nagwan Abdel Mahmoud, Noha F. Gu, Yeong Hyeon al-Masni, Mohammed A. School of Electrical Engineering Yanshan University Hebei Qinhuangdao066104 China Department of Electrical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems College of Engineering and Technology University of Sargodha Sargodha40100 Pakistan Department of Physiotherapy Isfandyar Bukhari Civil Hospital District Headquarter Hospital Attock43600 Pakistan Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Health Science and Technology Aalborg University Aalborg9220 Denmark Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Artificial Intelligence Data Science College of Software & Convergence Technology Sejong University Seoul05006 Korea Republic of

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN’s superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control. © 2024 the Author(s), licensee AIMS Press.

关键词： Convolutional neural networks

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