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Graph-Based Feature Learning for Cross-Project software Defect Prediction

computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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HistoMind: AI-Based Historical Place Navigator 5

HistoMind: AI-Based Historical Place Navigator

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Ranasinghe, Chathra Navoda Ranasinghe, Kavindu Ushan Munasighe, Niketh Priyaratne, Manul Harshanath, Buddika Rajapakse, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research addresses a critical gap in Sri Lanka's tourism infrastructure: the absence of a comprehensive smart location-based recommendation system with a route planner and a multilingual AI chatbot. To date, no integrated solution has been developed to cater to the diverse preferences of tourists, offer community-validated recommendations, and bridge language barriers. This research is pioneering the creation of these novel systems, with a clear focus on enhancing the tourism experience and supporting the country's economy. The smart recommendation system harmonizes various recommendation techniques, encompassing user preferences, community-approved choices, and hidden gems. By integrating these aspects, the system aims to provide tourists with personalized, community-endorsed recommendations while introducing them to unique destinations, thus addressing the existing void in Sri Lanka's tourism landscape. Furthermore, the AI-based multilingual chatbot tackles the language barrier head-on, enabling tourists to communicate effortlessly in their native language, a feature hitherto unavailable in the Sri Lankan tourism sector. This innovation aims to break down language barriers, making the country more accessible to a global audience. Through advanced methodologies and cutting-edge technology, this research strives to fill these critical gaps, promising a brighter future for Sri Lanka's tourism and its economic prosperity. © 2023 IEEE.

关键词： Tourism

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Accurate Stress Detection for Developers: Leveraging Low-Cost IoT Devices (ESP32 and MAX30102) to Analyze Heart Rate Variability via an External Mouse 5

Accurate Stress Detection for Developers: Leveraging Low-Cos...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayathilake, Amantha Ranasinghe, Janudi Perera, Dulshan Bartholomeusz, Shehan Rathnayake, Samadhi Ganegoda, Devanshi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Colombo Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9798350358131

In today's fast-paced work environment, stress levels have become a critical concern. This research introduces an innovative approach to address this issue by utilizing readily available, cost-effective components - the ESP32 microcontroller and the MAX30102 heart rate sensor, seamlessly integrated into an external mouse. This non-intrusive system enables real-time stress assessment for developers during their tasks. By analyzing heart rate variability data, a practical and effective solution is offered. The collected data is securely transmitted for analysis, potentially revolutionizing developer well-being and productivity. This study pioneers a unique and practical stress detection solution finely tuned to the software development milieu. © 2023 IEEE.

关键词： Microcontrollers

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An Intelligent Technique for the Effective Prediction of Monkeypox Outbreak 3

An Intelligent Technique for the Effective Prediction of Mon...

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Mir, Azka Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad 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)9798350322125

Monkey pox is a viral disease that spreads from animals especially monkey to human beings. Monkey pox outbreak has been increasing at a concerning rate. The outbreak of monkey pox has infected several people around the world. The extent and intensity of the disease can be determined by the occurrence of the symptoms. The objective of this paper is to predict monkeypox virus so that outbreak can be administered before monkeypox looms as a viral health hazard. The monkeypox case has been classified as confirmed, discarded and suspected. This paper uses a supervised machine learning model to predict the status of monkey pox case. To diagnose monkeypox virus case, clinical parameters are required. The selected dataset contains the parameters of monkey pox virus from April 2022 onwards. It is necessary to predict the monkey pox outbreak before it effects more valuable lives. For the purpose of this paper, supervised machine learning techniques have been used to determine the performance of the dataset through experimental analysis. The experiment has been performed using various classifiers such as Decision tree, Naïve Bayes etc. to compare the accuracy rate. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 93.51% using K-NN classifier with k=5 neighbors. Rapid miner platform is used for the application of the machine learning tools and techniques for the purpose of this research. This paper highlights the effective machine learning steps for the development of highly accurate model using machine learning techniques on monkey pox outbreak dataset. © 2023 IEEE.

关键词： Viruses

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computer Vision and Control Algorithm for Sustainable and Precision Agriculture

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SN computer Science 2025年第4期6卷 1-13页

作者： Adebayo, Segun Aworinde, Halleluyah O. Ojerinde, Stephen A. Oyedoja, Kayode O. Adeniyi, Abidemi Emmanuel Olanloye, Odunayo Dauda Osueke, Christian O. Ogundokun, Roseline Oluwaseun Das, Abhishek Mechatronics Engineering Bowen University Iwo Nigeria Computer Science Bowen University Iwo Nigeria Computer Science Western Illinois University IL United States Technology Education Emmanuel Alayande University of Education Oyo Nigeria Department of Software Engineering Kaunas University of Technology Kaunas Lithuania Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science and Engineering Aliah University Kolkata India Tshwane University of Technology Pretoria South Africa Durban University of Technology Durban South Africa Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The world population continues to increase with a large proportion of this increase happening in Africa just as predicated by Food and Agriculture Organization of the United Nation. To meet the demand of feeding this increasing population, innovative farming practices are being developed to increase the yield of the major stable food supply. This research developed a computer vision and control algorithm on a quadcopter for farm surveillance, crop protection from the invasion of pest, early disease detection as well as crop yield estimation. A process of developing a model capable of detecting and classifying these pests in the environment was presented in this study. The process involves selecting, preprocess and transforming the sensed data from a vision sensor to train classification algorithm and to detect and track object of interest in the environment. This study addresses a notable research gap in computer vision, control systems, big data analytics, and robotics, specifically within the context of smart farming. It primarily utilizes conventional learning algorithms, including Artificial Neural Networks and Support Vector Machines, typically found in widely-used commercial software. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Algorithm computer vision Machine learning Quadcopter UAV

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ESL127: Emirate Sign Language Dataset(s) and e-Dictionary (Version 2.0) Utilizing Deep Learning Recognition Models 15

ESL127: Emirate Sign Language Dataset(s) and e-Dictionary (V...

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15th International Conference on Innovations in Information technology, IIT 2023

作者： Ahmed, Ahmed Abdelhadi Gochoo, Munkhjargal Taghizadeh, Mohamad Otgonbold, Munkh-Erdene Batnasan, Ganzorig Department of Computer Science and Software Engineering Al Ain United Arab Emirates Iran University of Science and Technology Department of Electrical Engineering Iran

ISBN: (纸本)9798350382396

As stated by the United Arab Emirates's (UAE) Community Development Authority (CDA), there are around 3,065 individuals with hearing disabilities in the country. These individuals often struggle to communicate with broader society and rely on scarce sign language (SL) interpreters. Moreover, Arabic's dialects diversity compounds the issue by causing dialects in the Arabic Sign Language (ArSL). Hence, the call for a standardized reference for ArSL in the region is a priority. To address these challenges, we've developed an Emirate Sign Language (ESL) electronic dictionary (e-dictionary) with a dataset of 127 signs and 50 sentences, recorded by hearing-impaired individuals in the UAE with various degrees of deafness. Supervised by certified interpreters and validated by ESL's department head at CDA in Dubai, the recordings were made using Azure Kinect DK, resulting in 708 recordings. The dataset is then processed to 10fps. The e-dictionary offers features such as webcam-based sign recognition using YOLOv8 technology, voice-based signing via Arabic Automatic Speech Recognition, text-based signing, and words spelling in ArSL. © 2023 IEEE.

关键词： Character recognition

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Day-ahead PV Output Power Forecasting Utilizing Boosting Recursive LightGBM-LSTM Framework

Day-ahead PV Output Power Forecasting Utilizing Boosting Rec...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Hokmabad, Hossein Nourollahi Husev, Oleksandr Vinnikov, Dmitri Belikov, Juri Petlenkov, Eduard Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

ISBN: (纸本)9798350396782

Recently, global photovoltaic (PV) system installations have surged. Precise forecasting is vital for their grid integration and carbon emission cuts. However, due to fluctuating solar radiation, predicting PV output is difficult. Machine learning models, notably Long Short-Term Memory (LSTM) networks, offer a solution. This study presents a novel framework using a boosted recursive Light Gradient Boosting Machine (LightGBM)-LSTM network to forecast daily PV output at hourly intervals. Conducted in Northern Europe—a region with significant solar radiation variability—the models trained on meteorological and historical data showed a 12% improvement in RMSE, a 13% reduction in MAPE, and a 5% increase in the R2 score compared to standalone LSTM models. © 2023 IEEE.

关键词： boosting ensemble method LightGBM LSTM numerical weather prediction Solar power forecasting

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A Real-Time Carboy Tracking and Counting System Based on YOLOv8 and ByteTrack

A Real-Time Carboy Tracking and Counting System Based on YOL...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Akar, Ferhat Berber, Sezer Koca, Oğuzhan Bayılmış, Cüneyt R&D AYGAZ A.Ş. İstanbul Turkey Software Department Rise Technology Mersin Turkey Deparment of Computer Engineering Sakarya University Sakarya Turkey

ISBN: (纸本)9798350353488

In industrial processes, the accurate detection, counting, and classification of objects are crucial for the efficiency of production lines. However, when these processes are performed manually, human errors and time loss can occur. In this study, a YOLOv8-based system was developed to separate carboys based on their expiration dates, count solid carboys, and detect carboys containing foreign matter at the Pürsu Water Factory. The system, which processes real-time video streams, integrates YOLOv8 for object detection, tracking, and counting. Additionally, the performances of the DeepSORT and ByteTrack algorithms were compared for object tracking. The results show that ByteTrack performed faster and more stably than DeepSORT. The YOLOv8-based model achieved 98% accuracy, and ByteTrack worked at a higher FPS than DeepSORT, thereby improving the efficiency of the production line. This study presents an in-depth analysis of the system's accuracy, speed, and overall performance. © 2024 IEEE.

关键词： Object detection

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A Privacy-Preserving System Design for Digital Presence Protection

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computers, Materials & Continua 2023年第5期75卷 3091-3110页

作者： Eric Yocam Ahmad Alomari Amjad Gawanmeh Wathiq Mansoor Department of Computer and Cyber Sciences The Beacom CollegeDakota State UniversitySouth DakotaUSA Department of Software Engineering and Information Technology Ecole de Technologie Superieure(ETS)MontrealCanada College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

A person’s privacy has become a growing concern,given the nature of an expansive reliance on real-time video activities with video capture,stream,and *** paper presents an innovative system design based on a privacy-preserving *** proposed system design is implemented by employing an enhanced capability that overcomes today’s single parameterbased access control protection mechanism for digital privacy *** enhanced capability combines multiple access control parameters:facial expression,resource,environment,location,and *** proposed system design demonstrated that a person’s facial expressions combined with a set of access control rules can achieve a person’s privacy-preserving *** findings resulted in different facial expressions successfully triggering a person’s face to be blurred and a person’s privacy when using a real-time video conferencing service captured from a webcam or virtual webcam.A comparison analysis of capabilities between existing designs and the proposed system design shows enhancement of the capabilities of the proposed system.A series of experiments exercising the enhanced,real-time multi-parameterbased system was shown as a viable path forward for preserving a person’s privacy while using a webcam or virtual webcam to capture,stream,and store videos.

关键词： Attribute-based access control authentication authorization biometrics facial recognition identity privacy machine learning deep learning

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Energy Storage and Forecasting Error Impact Analysis in Photovoltaic Equipped Residential Nano-Grids 17

Energy Storage and Forecasting Error Impact Analysis in Phot...

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17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2023

作者： Hokmabad, Hossein Nourollahi Husev, Oleksandr Belikov, Juri Vinnikov, Dmitri Petlenkov, Eduard Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Tallinn University of Technology Department of Software Science Tallinn Estonia Tallinn University of Technology Department of Computer Systems Tallinn Estonia

ISBN: (纸本)9798350300048

Battery energy storage systems (BESS) enable many applications for photovoltaic (PV) equipped nano-grids. Stored excessive energy is utilized for energy arbitrage, demand response during blackouts, and peak shaving. This technology helped utility service providers deal with the duck-curve-effect and intermittency of renewable energy systems. In this paper, we investigate the benefits of using energy storage systems in PV nano-grids for residential sectors and evaluate the relationship between battery presence and system performance. We also analyze the effects of battery degradation on energy independence and self-sufficiency and investigate the relationship between simple payback time and state of health (SoH) levels of nano-grid components. Additionally, we propose a simple rule-based energy supervision strategy and evaluate its performance under various forecasting error levels, highlighting the importance of high-performance forecasting methods for proper energy management systems. Our contributions will help select and optimize PV-battery solutions and energy management algorithms to achieve the highest benefit and minimize investment risks. © 2023 IEEE.

关键词： Energy management systems

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