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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Asim, Sherrjel Nayyer, Muhammad Ziad Ahmed, Usama Faheem, Muhammad GIFT University Department of Computer Science Gujranwala Pakistan Government College University Department of Software Engineering Faisalabad Pakistan

ISBN: (纸本)9781665476010

Rapidly increasing data requires a lot of time to execute different tasks by the systems at Cloudlet Federation (CF). The demand of today's computing is to exploit resource utilization up to maximum. Efficient resource allocation and scheduling of tasks are the significant characteristics of Cloudlet Federation (CF). Different tasks have diverse execution time. Some tasks require greater execution time whereas some need just a couple of seconds for their execution. Scheduling offers a feasible solution for handling these tasks with the help of different scheduling algorithms. To address task scheduling issue, this paper proposes a scheduling technique named Least Busy Time Scheduling (LBTS) for smart scheduling of tasks in Cloudlet Federation (CF). The proposed technique assists the scheduler to assign task to a system considering the parameter of least busy time to reduce waiting time and optimize the overall performance of the system. © 2023 IEEE.

关键词： Scheduling algorithms

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Human Pose Estimation and Object Interaction for Sports Behaviour

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computers, Materials & Continua 2022年第7期72卷 1-18页

作者： Ayesha Arif Yazeed Yasin Ghadi Mohammed Alarfaj Ahmad Jalal Shaharyar Kamal Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-AhsaSaudi Arabia Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and *** understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose *** understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been *** existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized *** existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the *** estimation depends on appearance features and spatial ***,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the ***,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using *** predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction *** interactions are linked with the human and object to predict the *** experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.

关键词： Human object interaction human pose estimation object detection sports estimation sports prediction

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A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

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computers, Materials & Continua 2023年第4期75卷 165-182页

作者： Muhammad Azhar Sehat Ullah Khalil Ullah Habib Shah Abdallah Namoun Khaliq Ur Rahman Department of Computer Science&IT University of MalakandChakdara18800Pakistan Department of Software Engineering University of MalakandChakdara18800Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Statistics Abdul Wali Khan UniversityMardan23200Pakistan

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time *** (50) volunteers of different ages participated in the experimental *** the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

关键词： Age estimation gait biometrics classical linear regression model

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Towards Safe Online Machine Learning Model Training and Inference on Edge Networks 22

Towards Safe Online Machine Learning Model Training and Infe...

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

作者： Maruf, Md Al Azim, Akramul Auluck, Nitin Sahi, Mansi Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada Indian Institute of Technology Departmcnt of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350345346

With the increasing demand for edge computing in cyber-physical system (CPS) applications, ensuring the safety and reliability of machine learning models running on edge devices during online model training and inference is essential. Although data and model parallelism offer significant advantages for large machine learning model training, adopting parallel computing architecture in edge networks is challenging. It introduces safety concerns while splitting and integrating machine learning models over different computing nodes, which can pose risks to the integrity and reliability of the system. Therefore, online model training and inference in edge networks require a safe parallel computing architecture to achieve improved performance with optimal resource utilization. To address this challenge, we propose an efficient machine learning model partitioning algorithm that considers the safety constraint and requirements of edge networks and includes the triple-modular redundancy (TMR) technique for trusted computation. Our proposed approach achieves a significant speedup of approximately 56.3% in net training time compared to the non-partitioning approach, making it more efficient and suitable for real-time applications in edge networks. © 2023 IEEE.

关键词： Edge Networks Machine Learning Model Partitioning Triple Modular Redundancy (TMR)

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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry 8

Improving Supplier Evaluation Model using Ensemble Method-Ma...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Asrol, Muhammad Wahyudi, Sofyan Suharjito Harito, Christian Utama, Ditdit N. Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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Resource Availability Forecasting for Federated Clouds

Resource Availability Forecasting for Federated Clouds

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Mursal, Muhammad Hassan Ahmed, Usama Nayyer, Muhammad Ziad Government College University Department of Software Engineering Faisalabad Pakistan GIFT University Department of Computer Science Gujranwala Pakistan

ISBN: (纸本)9781665476010

Cloud federation has enabled organizations to adopt collaborative services for sharing data and workloads across various platforms. Induction of federation members may require some verifications and predictions related to capacity and capability of these members for compensating such types of workloads. However, the nature of federated services require stringent methods to keep track of dynamically forming resource clusters for forecasting their behavior. Recent literature has mostly focused on the applicability of forecasting algorithms based on static datasets with little or no applicability to real time scenarios. Proposed research has utilized a real world application of Clouds4Coordination (C4C) federation system. A resource forecasting strategy using two well-known algorithms, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) has been proposed for collaborative clouds used in Architecture, engineering and Construction (AEC) industry. The results have shown that no single algorithm is sufficient enough to deal with dynamic scenarios of cloud federation. Moreover, the selection of algorithm is highly dependent upon the type and duration of prediction required i.e. short term or long term as required by the user. © 2023 IEEE.

关键词： Forecasting

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

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computers, Materials & Continua 2022年第8期72卷 2597-2615页

作者： Yazeed Yasin Ghadi Nida Khalid Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or ***,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of *** propose a robust system comprising both RGB(red,green,blue)and depth *** particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency *** track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector ***,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for *** are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity ***,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI *** results proved the validity of the proposed system.

关键词： Artificial neural network human-human interaction humanobject interaction k-ary tree hashing machine learning

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A Framework for Multi-Grade Classification of Ulcerative-Colitis Using Deep Neural Networks 25

A Framework for Multi-Grade Classification of Ulcerative-Col...

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25th International Multi Topic Conference, INMIC 2023

作者： Noor, Muhammad Nouman Nazir, Muhammad Dilshad, Veena Haneef, Farah Hitec University Taxila Department of Computer Science Taxila Pakistan Cust University Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350317701

Endoscopic disease severity assessment is a critical component in the management of ulcerative colitis patients. Endoscopic evaluation, on the other hand, suffers from significant intra-observer and inter-observer differences, reducing the reliability of individual assessments. As a result, we set out to create a deep-learning model capable of distinguishing between distinct endoscopic disease severity levels. Initially, we preprocessed the dataset and then applied data augmentations on the images using various geometric transformations. Subsequently, we have utilized the transfer learning concept by applying modified ResNet-50 by stacking additional layers which further improves the classification performance. Our proposed model achieved an accuracy of 84.21%, 81.06% recall, and 88.33% precision. © 2023 IEEE.

关键词： Diseases

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Regularized forensic efficient net: a game theory based generalized approach for video deepfakes detection

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Multimedia Tools and Applications 2024年 1-44页

作者： Ain, Qurat Ul Javed, Ali Malik, Khalid Mahmood Irtaza, Aun Department of Computer Science University of Engineering and Technology-Taxila Punjab Taxila47050 Pakistan Department of Software Engineering University of Engineering and Technology-Taxila Punjab Taxila47050 Pakistan College of Innovation and Technology University of Michigan-Flint FlintMI48502 United States

Due to the advancements in cutting-edge generative AI algorithms, generating hyper realistic deepfake videos has become easier for the public. This hyperrealism consequently fails contemporary methods to reliably discriminate between original and fake videos. Therefore, to counter any threat caused by these next-generation artificially generated videos, dependable approaches are required to address this classification challenge. To achieve this objective this paper presents an interdisciplinary approach that integrates game theory with deep learning to bring a novel solution to the problem of deepfake detection and protect the detectors against anti-forensics attack. To the best of our knowledge, there does not exist any other work dedicated to video deepfake detection using the integrated approach of game theory and deep learning. The game is designed for two players to distinguish between pristine and deepfake videos. The game utilizes different strategies for the data manipulator as a player P1 and the deepfake detector as P2. Strategies used for P1 involve the formation of the subsets like open and close-set, combined subsets, imbalanced dataset, and post-processing attacks to create challenging strategies for P2. To counter the strategies of P1,we propose a novel Regularized Forensic Efficient Net (RFE Net) that employs regularization techniques, such as batch normalization, dropout, augmentation, and early stopping. Based on the P1 move, the detector chooses the regularization techniques by considering factors such as generalizability and efficiency. Regularization-based strategies improve the performance of our model when compared to contemporary methods. Computation of the Nash equilibrium with the proposed zero-sum game helps to effectively detect deepfakes and leads the game to maximum payoff. Performance of the proposed game theory-based RFE Net was measured on standard and diverse datasets of FaceForensic++, DFDC preview, CelebDF, DFFD, and the World lea

关键词： Nash equilibrium

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