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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Ogundoyin, Sunday Oyinlola Kamil, Ismaila Adeniyi Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Bowen University Electrical and Electronic Engineering Programme Iwo Nigeria University of Ibadan Department of Electrical and Electronic Engineering Ibadan Nigeria Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bando 40601 Kenya

ISBN: (纸本)9798331542559

To reduce communication latency and improve vehicular communication, it is crucial to integrate a fog-based mobile crowdsensing (FBMCS) system with the rapidly evolving Internet of Vehicles (IoV). A four-tier FBMCS architecture is proposed for IoV environments, partitioned into smart vehicles, vehicular fog, fog gateway, and cloud services. An extensive experimental simulation is conducted using iFogSim2 to evaluate the impact of trust, fog mobility, and fog-to-fog (F2F) collaboration on the system performance. The results show a reduction in communication latency and better network resource usage when vehicular fogs move at a maximum speed of 10m/s, with the trust management system contributing an insignificant increase in communication latency and network usage. Moreover, the fusion of these three mechanisms marginally increases energy consumption by approximately 0.33%, while the speed of vehicular fogs is limited to 10m/s. The findings demonstrate the potential of the proposed architecture to improve the overall performance of IoV services. © 2024 IEEE.

关键词： Fog Gateway Fog Mobility Fog-to-Fog (F2F) iFogSim2 Trust

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognition 22

ProtoKD: Learning from Extremely Scarce Data for Parasite Ov...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Trehan, Shubham Ramachandran, Udhav Scimeca, Ruth Aakur, Sathyanarayanan N. Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States Oklahoma State University Department of Veterinary Pathobiology StillwaterOK74078 United States

ISBN: (纸本)9798350345346

Developing reliable computational frameworks for early parasite detection, particularly at the ova (or egg) stage, is crucial for advancing healthcare and effectively managing potential public health crises. While deep learning has significantly assisted human workers in various tasks, its application in diagnostics has been constrained by the need for extensive datasets. The ability to learn from an extremely scarce training dataset, i.e., when fewer than 5 examples per class are present, is essential for scaling deep learning models in biomedical applications where large-scale data collection and annotation can be expensive or not possible (in case of novel or unknown infectious agents). In this study, we introduce ProtoKD, one of the first approaches to tackle the problem of multi-class parasitic ova recognition using extremely scarce data. Combining the principles of prototypical networks and self-distillation, we can learn robust representations from only one sample per class. Furthermore, we establish a new benchmark to drive research in this critical direction and validate that the proposed ProtoKD framework achieves state-of-the-art performance. Additionally, we evaluate the framework's generalizability to other downstream tasks by assessing its performance on a large-scale taxonomic profiling task based on metagenomes sequenced from real-world clinical data. © 2023 IEEE.

关键词： Learning from Extremely Scarce Data Microscope Image Analysis Ova Detection

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Recursive Broadcasting Approach 33

Recursive Broadcasting Approach

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Concordia University Department of Computer Science and Software Engineering MontrealQCH3G 1M8 Canada Institute for Informatics and Automation Problems NAS RA Yerevan0014 Armenia

ISBN: (纸本)9798331524937

Broadcasting is one of the fundamental information dissemination primitives in interconnection networks, where a message is passed from one node (called originator) to all other nodes in the network. Following the increasing interest in interconnection networks, extensive research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this study, we will mainly focus on determining the broadcast time and nearoptimal broadcasting schemes in networks. Determination of the broadcast time of any node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few network topologies. There also exist various heuristic and approximation algorithms for different network topologies. In this study, we consider the broadcast time problem on graphs that comprise some recursive structures. We initiate a novel direction to designing broadcasting algorithms on recursively defined graphs. We provide a theoretical foundation for future broadcasting studies, as well as discuss several practical applications of the approach we introduce. © 2025 IEEE.

关键词： Interconnection networks (circuit switching)

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Efficiency-First Fault-Tolerant Replica Scheduling Strategy for Reliability Constrained Cloud Application 18th

Efficiency-First Fault-Tolerant Replica Scheduling Strategy ...

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18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

作者： Zhang, Yingxue Fan, Guisheng Yu, Huiqun Chen, Xingpeng Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030935702

Reliability requirement assurance is an important prerequisite for application execution in the cloud. Although copy management can improve the reliability of applications, it also brings a series of resource waste and overhead issues. Therefore, the efficiency-first fault-tolerant algorithm (EFFT) with minimum execution cost in the cloud application is proposed. This algorithm minimizes the execution cost of the application under the constraints of reliability, and solves the problem of excessive overhead caused by too many copies. The EFFT algorithm is divided into two stages: initial allocation and dynamic adjustment. On the initial allocation of EFFT algorithm, a sorting rule is defined to determine the priority of tasks and instances. During the adjustment phase, by defining an actual efficiency ratio indicator to measure the cost-effectiveness of an instance, the EFFT algorithm makes a good trade-off between cost and reliability in order to minimize execution costs. Run our algorithm on randomly generated parallel applications of different scales and compare the experimental results with four advanced algorithms. The experiments show that the performance of the algorithm we proposed is better than the other algorithms in terms of execution cost and fault tolerance. © 2022, IFIP International Federation for Information Processing.

关键词： Fault tolerance

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Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

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Journal on Big Data 2023年第1期5卷 1-18页

作者： Mian Muhammad Danyal Sarwar Shah Khan Muzammil Khan Muhammad Bilal Ghaffar Bilal Khan Muhammad Arshad Department of Computer Science City University of Science&TechnologyPeshawar25000Pakistan Department of Computer Science and Software Technology University of SwatSwat19200Pakistan Department of Computer Software Engineering University of Engineering&Technology MardanMardan23200Pakistan Department of Computer Science IQRA National UniversitySwat19200Pakistan

Movies are the better source of *** year,a great percentage of movies are *** comment on movies in the form of reviews after watching *** it is difficult to read all of the reviews for a movie,summarizing all of the reviews will help make this decision without wasting time in reading all of the *** mining also known as sentiment analysis is the process of extracting subjective information from textual *** mining involves identifying and extracting the opinions of individuals,which can be positive,neutral,or *** task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie *** reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular *** summary of all reviews can give a general idea about the *** study compares baseline techniques,Logistic Regression,Random Forest Classifier,Decision Tree,K-Nearest Neighbor,Gradient Boosting Classifier,and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version *** applying these classifiers,in pre-processing both datasets are cleaned,duplicate data is dropped and chat words are treated for better *** the IMDB Dataset of 50K reviews,Linear Support Vector Machines achieve the highest accuracy of 89.48%,and after hyperparameter tuning,the Passive Aggressive Classifier achieves the highest accuracy of 90.27%,while Multinomial Nave Bayes achieves the highest accuracy of 70.69%and 71.04%after hyperparameter tuning on the Sentiment Polarity Dataset Version *** study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.

关键词： Opinion mining machine learning movie reviews IMDB Dataset of 50K reviews Sentiment Polarity Dataset Version 2.0

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A Gamified Virtual Learning Environment to Enhance Online Teaching and Learning Experience 7

A Gamified Virtual Learning Environment to Enhance Online Te...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Tharaka, W.C.M.K. Dilanka, R.M.T. Perera, H.D.D.S. Rathnayake, R.H.C.S. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665421683

In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pandemic. However, the learning-teaching process has become ineffective because existing video conferencing solutions are not intended for academic purposes. This research was conducted to identify the pain points of online education and develop an enhanced software solution. A user survey confirmed that an isolated environment tends to diminish attentiveness during online lectures. Also, it is difficult for teachers to observe the attentiveness of all the students. As a solution, student attentiveness was measured using their facial expressions and collected data shown to lecturers through a virtual student behavioural environment. The physical separation causes students to feel isolated during lectures, which can negatively affect their academic development and social and psychological development. The developed application also provides a virtual group study environment as a feature. According to the results gathered in the user acceptance testing phase, it was found that the attention detection feature helps students keep their attention at a significant level. Further, 9 out of 10 teachers who participated in the testing acknowledged that the simulated student behavioural view could provide a more immersive experience. 71% of students preferred the new collaborative virtual environment, and students further elaborated that the virtual environment was more likely the physical group studies. 72% of students mentioned that the collaborative group study tool helped eliminate isolation during group studies. © 2022 IEEE.

关键词： E-learning

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Comprehensive Analysis of Blackhole Attack on RPL in Static, Mobile and Hybrid Network Environment 5

Comprehensive Analysis of Blackhole Attack on RPL in Static,...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Garba, Faisal Ali Oduwole, Oluwakemi Isa, Abubakar Balarabe Aliyu, Abdulkadir Sarki Dima, Rosemary Mrumun Sa'Adatu Rimi University of Education Department of Computer Science Education Kano Nigeria National Mathematical Center Kwali-Sheda Computer Science Programme Abuja Nigeria Federal University Dutsinma Department of Software Engineering Katsina Nigeria

ISBN: (纸本)9798331542559

The Routing Protocol for Low-Power and Lossy Networks (RPL), extensively applied in IoT applications, is susceptible to the Blackhole attack, wherein hostile nodes discard packets without transmission, resulting in considerable loss of network performance. This attack jeopardizes the reliability and usefulness of RPL-based networks, particularly in crucial applications like environmental monitoring and healthcare. Understanding the effects of Blackhole attacks in diverse network environments - static, mobile, and hybrid - is vital for creating personalized security solutions. Every scenario provides various issues, including the stability of static networks and the dynamic nature of mobile and hybrid setups, which determine the severity of the attack. This study performs a comparative investigation of the impact of Blackhole attacks on RPL-based networks in static, mobile, and hybrid scenarios. Simulations were done with varied node densities (10, 20, and 30 nodes) and different percentages of malicious nodes (10%, 20%, and 30%). Key performance indicators, such as Packet Delivery Ratio (PDR), Average End-to-End Delay (AE2ED), overhead packets, energy utilization, and throughput, were investigated across these conditions. The analysis suggests that static networks, however more robust to random attacks, are susceptible to disruption when Blackhole nodes interfere with important links. Mobile networks incur the highest performance deterioration due to frequent route changes and increasing energy demand. Hybrid networks, having both static and mobile nodes, demonstrate intermediate susceptibility but are more affected by mobile attackers. Overall, the statistics imply that mobility improves the impact of Blackhole attacks. The findings underscore the need for environment-specific security solutions in RPL-based networks. Static networks demand stronger detection systems for targeted attacks, while mobile and hybrid environments need adaptable, low-overhead security so

关键词： Blackhole Attack Hybrid Networks IoT Low Power and Lossy Networks Mobile Networks RPL Security Static Networks

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An software Defined Networking (SDN) Enhanced Edge Computing Framework for Internet of Healthcare Things (IoHT)

An Software Defined Networking (SDN) Enhanced Edge Computing...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Anh, Dang Van Chehri, Abdellah Quy, Nguyen Minh Hue, Chu Thi Minh Nguyen, Dinh C. Quy, Vu Khanh Hung Yen University of Technology and Education Faculty of Information Technology Hungyen Viet Nam Royal Military College of Canada Department of Mathematics and Computer Science Kingston Canada Fpt University Faculty of Software Engineering Hanoi Viet Nam The University of Alabama Department of Electrical and Computer Engineering HuntsvilleAL United States

ISBN: (纸本)9798350351255

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems' service response times across various workloads and traffic. © 2024 IEEE.

关键词： Electronic health record

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Hybrid Machine Learning Model for Face Recognition Using SVM

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computers, Materials & Continua 2022年第8期72卷 2697-2712页

作者： Anil Kumar Yadav R.K.Pateriya Nirmal Kumar Gupta Punit Gupta Dinesh Kumar Saini Mohammad Alahmadi IES College of Technology Bhopal462044India Maulana Azad National Institute of Technology Bhopal462003India Department of Information Technology Manipal University JaipurJaipur303007India Department of Computer and Communication Engineering Manipal University JaipurJaipur303007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

Face recognition systems have enhanced human-computer interactions in the last ten ***,the literature reveals that current techniques used for identifying or verifying faces are not immune to *** Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis *** to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data *** from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than ***-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s *** study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face *** experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.

关键词： Face recognition system(FRS) face identification SVM discrete cosine transform(DCT) artificial neural network(ANN) machine learning

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