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

作者： Vijayaraghavan, P. Vasanthi, P. almusawi, Muntather Thakur, Shivani Varma, Pankaj Anakath Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Sri Ramakrishna Engineering College Department of Mathematics Tamil Nadu Coimbatore 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 Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

This work proposes a GA-PSO Metaheuristic algorithm to improve the throughputs and network efficiency of the UWSNs based on using a new technology of WSNs which is Underwater Wireless Sensor Networks. In response to the difficulties of underwater communication, a hybrid GA-PSO algorithm is designed to fit the needs, where GA excels in search capabilities, and PSO accomplishes optimization adjustments. The realistic network model was developed using realistic network topology with MATLAB NS-3 and the integrated GA-PSO hybrid approach was found to significantly enhance the throughput energy efficiency and overall network lifetime as compared to the other routing protocols GA & PSO individually. Based on these findings, it could therefore be concluded that the proposed hybrid bioinspired algorithms can potentially help enhance the efficiency and dependability of UWSNs significantly. © 2024 IEEE.

关键词： Genetic algorithms

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Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

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computers, Materials & Continua 2023年第1期74卷 933-949页

作者： M.V.Rajesh T.Archana Acharya Hafis Hajiyev E.Laxmi Lydia Haya Mesfer alshahrani Mohamed K Nour Abdullah Mohamed Mesfer al Duhayyim Department of Computer Science and Engineering Aditya Engineering CollegeSurampalem533437India Department of Business Administration Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Accounting and Audit Azerbaijan State University of Economics(UNEC)BakuRepublic of Azerbaijan Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track *** sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based *** routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor *** clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such *** this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted *** presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN *** addition,an effective multihop route planning approach was designed by the use of EHGS *** proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS ***,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal *** performance validation of the BCEHGS-RP model is experimented with under diverse number of *** experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

关键词： Internet of things wireless sensor networks routing metaheuristics blockchain

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Detecting and Mitigating Distributed Denial of Service Attacks in software-Defined Networking

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computers, Materials and Continua 2025年第3期83卷 4515-4535页

作者： Abdullah M. alnajim Faisal Mohammed alotaibi Sheroz Khan Department of Information Technology College of Computer Qassim University Buraydah 51452 Saudi Arabia College of Computer Engineering and Sciences Department of information system Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Electrical Engineering College of Engineering and Information Technology Onaizah Colleges Qassim 56447 Saudi Arabia

Distributed denial of service (DDoS) attacks are common network attacks that primarily target Internet of Things (IoT) devices. They are critical for emerging wireless services, especially for applications with limited latency. DDoS attacks pose significant risks to entrepreneurial businesses, preventing legitimate customers from accessing their websites. These attacks require intelligent analytics before processing service requests. Distributed denial of service (DDoS) attacks exploit vulnerabilities in IoT devices by launching multi-point distributed attacks. These attacks generate massive traffic that overwhelms the victim’s network, disrupting normal operations. The consequences of distributed denial of service (DDoS) attacks are typically more severe in software-defined networks (SDNs) than in traditional networks. The centralised architecture of these networks can exacerbate existing vulnerabilities, as these weaknesses may not be effectively addressed in this model. The preliminary objective for detecting and mitigating distributed denial of service (DDoS) attacks in software-defined networks (SDN) is to monitor traffic patterns and identify anomalies that indicate distributed denial of service (DDoS) attacks. It implements measures to counter the effects of DDoS attacks, and ensure network reliability and availability by leveraging the flexibility and programmability of SDN to adaptively respond to threats. The authors present a mechanism that leverages the OpenFlow and sFlow protocols to counter the threats posed by DDoS attacks. The results indicate that the proposed model effectively mitigates the negative effects of DDoS attacks in an SDN environment.

关键词： software-defined networking (SDN) distributed denial of service (DDoS) attack sampling Flow (sFlow) OpenFlow OpenDaylight controller

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed ali Ullah, Tahir Khan, Arif ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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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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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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