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IAENG International Journal of computer science 2021年第2期48卷 1-12页

作者： Zhang, Xiaoxia Yu, Ziqiao Hu, Yinyin Yang, Jiao A Professor of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China A Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Milling force prediction of titanium alloy plays an important role in titanium alloy milling process. In the article, the milling process of titanium alloy materials is analyzed, and this material quality is affected directly by the milling force. Support vector machine (SVM) has shown prominent performance for many real-world problems. However, SVM computational complexity increases, as the number of samples increases. How to effectively apply it to massive datasets is still a serious challenge. Although there are some literatures on parameter optimization techniques of SVM regression, the performance of this model still needs to be further studied and improved. We present a new milling force predicting model, namely ACO&SVM, which integrate SVM regression with ACO algorithm to enhance the prediction accuracy of milling force for mill process of titanium alloy. The primary innovation feature of hybrid model is to apply the ACO to automatically determine parameters of SVM regression model. In addition, as being applied to discrete optimization, to solve continuous optimization problems, ACO algorithm needs to transform continuous variables into discrete ones by discretizing of continuous variables. The results have shown that proposed ACO&SVM model yields better prediction accuracy, and thus it can be widely applied to the fields of material processing optimization. © 2021. All Rights Reserved.

关键词： Titanium alloys

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A Comprehensive Prediction Model for T20 and Test Match Outcomes Using Machine Learning

A Comprehensive Prediction Model for T20 and Test Match Outc...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Ahsan, Zoha Ghumman, Shahwaiz Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350349719

Cricket is a popular sport worldwide, played with a bat and balls. This paper used classification and regression to predict the T20 and Test matches results and scores because cricket fans and analysts always want to predict which team will win the match and how much score a team will make. We have used a supervised machine learning technique to predict the score and result for T20 and Test matches. Cricket match prediction dataset comprising of 13 features and 7827 instances utilized for training the model. For the prediction of winning and losing, we have used Naïve Bayes, Gradient Boosted Trees, Logistic Regression, Deep learning, Decision Tree, Random Forest, and Generalized Linear Model. For Score Prediction, we have used Decision Trees, Random Forests, Gradient Boosted Trees, Generalized Linear Models, and Deep Learning. We've shown the most accurate model after evaluating the accuracy percentages of the several classifiers listed above. Our introduced model has gained an accuracy rate of 96.15% using the Gradient Boosted Trees classifier for Match Score Prediction and 71.72% accuracy by using the Generalized Linear Model for match result prediction. The Rapid Miner tool has been used to perform machine learning techniques to train models. This paper discusses the effectiveness and use of machine learning methods to develop highly accurate models for Cricket match prediction dataset comprising of 13 features and 7827 instances utilized for training the model. © 2024 IEEE.

关键词： Forecasting

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A Process Oriented Integration Model for Smart Health Services

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Intelligent Automation & Soft Computing 2023年第2期35卷 1369-1386页

作者： Farzana Kausar Gondal Syed Khuram Shahzad Muhammad Arfan Jaffar Muhammad Waseem Iqbal Department of Computer Science Superior UniversityLahore54600Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Faculty of Computer Science&IT Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan

Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing ***,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efﬁcient collection and sharing of data,along with inadequate collaboration of software and *** to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling *** this integration modeling solution,atﬁrst,the service participants,processes and tasks of smart services are identiﬁed and then standard illustrations are developed for the better understand-ing of the integrated service group *** process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart *** research is concluded with the integration process model application for the required data sharing among different service *** outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.

关键词： Process modeling business process business process integration business process modeling and notation

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A Cloud Computing Model for Libyan Higher Education Institutions 3

A Cloud Computing Model for Libyan Higher Education Institut...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Imragaa, Najwa A. Alfsai, Entesar H. Gargoum, Bothaina F. Maatuk, Abdelsalam M. University of Benghazi Department of Computer Science Benghazi Libya University of Benghazi Department of Software Engineering Benghazi Libya

ISBN: (纸本)9798350319897

Cloud computing is now changing the IT landscape and playing a significant economic role for companies that develop software. Users of educational resources now have more flexibility and access because of cloud computing. Students, faculty members, and support staff all use cloud computing in academic institutions. Internet-based computing, known as "Cloud Computing,"allows users to share IT resources securely and efficiently. The use of cloud computing in universities has many benefits, such as access to files, e-mail, databases, educational resources, and search applications as requested by faculty, administrators, staff, and students at a university alike. Moreover, cloud computing significantly reduces the complexity and cost of IT in universities. This paper discusses cloud computing, its structures, types, and services it provides, and then examines the possibility of implementing cloud services in universities and discovers the various opportunities and benefits of cloud services. The paper presents a proposed model for cloud computing for Libyan universities, which was applied to the University of Benghazi and produced satisfactory results and facilitated a lot of university work. Through the application of the proposed model, effective management of the technological needs of the Libyan university has been achieved, such as connecting programs and providing a platform for development, data storage and computing. © 2023 IEEE.

关键词： Application programs

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Classification of Human Physical Activities and Postures During Everyday Life 18

Classification of Human Physical Activities and Postures Dur...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khan, Sheharyar Noorani, Sadam Hussain Arsalan, Aamir Mahmood, Awais Rauf, Usman Ali, Zohaib University of Engineering & Technology Department of Computer Engineering Taxila Pakistan Fatima Jinnah Women University Department of Software Engineering Rawalpindi Pakistan Szabist University Department of Computer Science Islamabad Pakistan Bahria University Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

Nowadays Wearable sensor-based Human Activity Recognition (HAR) is gaining popularity for its affordability and low computational demands. These sensors are widely used in healthcare and surveillance. However, using smartphone sensors for HAR can be inaccurate due to their non-fixed position. This study uses the publicly available Human Activity Recognition Trondheim dataset (HARTH) to develop a HAR model. The proposed HAR model is capable to recognize diverse human daily life activities in the free-living environment. Identifying individual natural behaviour in the wild (free living) remains challenging because humans engage in unscripted daily activities. While working with controlled conditions (scripted data) can produce optimal results, this system often struggles when applied in real-life scenario. Multiple machine learning classifiers are tested on time domain features extracted from sensor data, with the Multilayer Perceptron (MLP) classifier achieving an impressive 92.92% accuracy, making a significant contribution to the HAR field. © 2023 IEEE.

关键词： Wearable sensors

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The Impact of Stuttering Event Representation on Detection Performance 2

The Impact of Stuttering Event Representation on Detection P...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Banna, Abedal-Kareem Fang, Hui Shubita, Ahmad F. Arafah, Mohammad Eqteeshat, Amani Abuaisheh, Saif B. University Of Petra Department Of Ai And Data Science Jordan Loughborough University Department Of Computer Science United Kingdom University Of Petra Department Of Software Engineering Jordan University Of Petra Department Of Computer Science Jordan Malaya University Department Of Information System Malaysia

ISBN: (纸本)9798350394962

Stuttering or stammering is a common neurogenic, psychogenic fluency disorder wherein people have uncontrolled blocks on the natural flow of speech. Automatic Stuttering Event Detection (SED) is a widely recognised challenge due to the heterogenic and overlapped nature of stuttering speech. Therefore, this comparative study investigates the impact of stuttering event representation on detection performance. The paper evaluates the performance of SED and shows the impact of employing ASR pre-trained feature extractions on each stuttering event. Moreover, the paper provides different groups of experiments that evaluate the impact of three acoustic features, the Zero Crossing Rate (ZCR), the Spectral Flux Onset Strength Envelope (SFO), and Fundamental Frequency (FF) features, on SED performance. The paper demonstrates that the fusion of spectral and ASR features improves SED performance across all stuttering events by 8%. The F1 score of the Block class increased by approximately 10%, from 15% to 25%, while the word repetition and interjection increased by 8%. In addition, our experiments proved that the fusion of temporal features increased the average F1 score of SED by 2% across all stuttering events. The F1 score of the prolongation and sound repetition classes increased by 6% and 3%, respectively. © 2024 IEEE.

关键词： Deep learning

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Predictive Modeling of Students' Stress Levels Using Machine Learning Algorithm 4

Predictive Modeling of Students' Stress Levels Using Machine...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Ali, Hassan Amin, M. Hamza Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350353266

The increasing prevalence of stress among university students has raised concern about its impact on academic performance and overall well-being. This conference paper explores the applications of machine learning algorithms to predict and analyze the student's stress level. A comprehensive dataset encompassing various stress-related factors, including academic workload, social interactions, and lifestyle, was collected from Kaggle which consists of 1100 instances and 19 attributes. Our methodology places a premium on human connection, integration of qualitative data to refine the accuracy of stress predictions. By seamlessly merging the capability of machine learning with an empathetic understanding of the human experience, our approach strives to pave the way for a more holistic and personalized educational ecosystem. The insights derived from this study aspire to guide educators, administrators, and policy makers in crafting nurturing environments that empower students to excel both academically and emotionally. Embarking on a journey to unravel the myriad factors influencing students' stress, we consider academic pressures, social dynamics, and personal experiences. Harnessing machine learning algorithms such as decision trees, neural networks, we navigate expansive student populations. Acknowledging the nuanced nature of stress, our study integrates a compassionate approach with cutting-edge technology. The study employs state-of-the-art machine learning techniques using various classification algorithms, to build predictive models for assessing stress levels out of which random forest gives 97.45% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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System for Table Run Strategy in Peruvian Billiards with Real-Time Oject Detection using YOLOv8n 10

System for Table Run Strategy in Peruvian Billiards with Rea...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Silva, Italo Chavez, Raquel Castillo-Sequera, José Luis Wong, Lenis Peruvian University of Applied Sciences Department of Software Engineering Lima Peru University of Alcalá Department of Computer Science Alcalá de Henares Spain

ISBN: (纸本)9798350387353

The main challenge for billiard players, regardless of their skill level, is to develop a strategy that allows them to consecutively pocket balls in a single turn, commonly known as 'closing the table.' This strategy requires high precision and consistency, especially for less experienced players. This study integrates a YOLOv8n model, a shot suggestion algorithm, and a shot prediction algorithm to build a system that aids players in developing this 'table closing' strategy through visual guides indicating the shots to take and where to position the cue ball for the next shot. The proposed system follows these phases: (1) Selection of the video camera, (2) Selection of the object detection technique, (3) Development of the shot suggestion algorithm, (4) Development of the shot prediction algorithm, and (5) System construction. Phases (1) and (2) address the implementation of the YOLOv8n model, while phases (3) and (4) involve the development of algorithms for the 'table closing' strategy. To validate the system's effectiveness, an experiment with 24 players of different skill levels was conducted, showing that the system reduced the average number of shots needed to close a table to 22, compared to the 47-shot average of professional players. Beginners accepted 100% of the system's recommendations, while intermediate and professional players showed acceptance rates of 95.1% and 95.6%, respectively. A survey indicated high overall satisfaction, with a median response of 4.5 on a 5-point scale. The system also demonstrates significant competitive advantages over existing solutions by maintaining high detection and prediction accuracy while effectively functioning in real-world scenarios, leveraging the latest advances in computer vision to surpass older techniques in both effectiveness and efficiency. © 2024 IEEE.

关键词： computer vision

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Forecasting the Stability of COVID-19 Vaccine Companies Stock Market using LSTM and Time-series Models 3

Forecasting the Stability of COVID-19 Vaccine Companies Stoc...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Khan, Rahemeen Baloch, Safia Bahria University Department of Software Engineering Karachi Pakistan Fast University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798350332391

Stock price forecasting is challenging because stock markets are volatile and nonlinear in nature. With the impact of the Covid-19 outbreak in 2019, stock market forecasting has become more complicated. The goal of this study is to identify the top brands in the global covid-19 vaccination market and their impact on the stock market. By forecasting the next months stock exchange, traders will be able to select the most suitable company to invest in. In this paper, MSE, RMSE, and MAPE are used as evaluation matrices to compare timeseries and neural network models such as Arima, Prophet, and LSTM. According to the results of a comparative analysis, the LSTM model produced the most accurate predictions on majority stock prices dataset for the selected vaccination brands. Brand stability is achieved by predicting future stock exchange prices using an outperformed LSTM model. As a result, BioNTech has been identified as a high potential stable brand that the buyer can invest to gain enormous returns among the massive number of probabilistic events. © 2023 IEEE.

关键词： Time series

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A computer Vision-Based Vehicle Detection System Leveraging Deep Learning 1

A Computer Vision-Based Vehicle Detection System Leveraging ...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Khan, Saadullah Vohra, Sanayna Siddique, Sultan Abdullah Abro, Abdul Ahad Ebrahim, Mansoor Iqra University Main Campus Department of Software Engineering Karachi Pakistan Iqra University Main Campus Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

This study uses advanced computer vision (CV) and deep learning (DL) techniques to present a combined approach for extensive vehicle detection and number plate recognition. Improving the effectiveness and precision of traffic surveillance and administration systems is the main goal. The two primary parts of the suggested system are identification of number plates recognition and vehicle detection. The vehicle detection module uses rigid images or video streams to recognize along with monitor vehicles in real-time using innovative DL algorithms. Such limitations of system reliability are overcome through the application of CNN and object detection models in an integrated computer vision system that allows for reliable performance under a variety of environmental conditions including unfavorable lighting and weather situations. The goal of an identification plate recognition feature is to read and extract alphanumeric characters from license plates that have been detected. To precisely identify and interpret license plate data, this stage combines DL models with the use of optical character recognition (OCR) techniques. The system can adapt to different geographical locations by supporting changes in plate layouts, fonts, and positions. By combining these two modules, a complete intelligent transportation system solution is produced, offering useful information for applications such as congestion examination, safety, and assurance. Because of its modular architecture, the system is easily scalable and customizable, enabling it to be adjusted to various hardware configurations and surveillance scenarios. Using real-world scenarios and benchmark datasets, extensive experiments are conducted to assess the effectiveness of the proposed system. The outcomes show excellent efficiency and accuracy in tasks involving the detection of vehicles and number plate recognition. By promoting enhanced security, integrity, and general traffic management, the suggested integrated syste

关键词： Object detection

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