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检索条件"机构=dept. Computer Science and Software Engineering"

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Investigates the Impact of AI-generated Code Tools on software Readability Code Quality Factor 25

Investigates the Impact of AI-generated Code Tools on Softwa...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Fawareh, Hamed Al-Shdaifat, Hazim M. Al-Refai, M. Fawareh, Faid AlNoor Khouj, Mohammed Zarqa University Dept. of Software Engineering Jordan Zarqa University Dept. Of Mathmitical Science Jordan University of Business and Technology Dept. of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Artificial Intelligence (AI) has made great strides in various industries, including software development, with tools such as ChatGPT changing the way code is written, maintained and developed. This study investigates the impact of AI-generated code on software quality, focusing on readability, code clarity, complexity, and documentation. Comparing AI-generated code with open-source code from GitHub for three tasks of varying difficulty (easy, medium, and hard), we evaluated key metrics such as cyclomatic complexity (CC), code lines, comment density, code-to-code ratio, and variable names. The results revealed that although both AI-generated and human-generated code can be concise and efficient, code generated by AI tools shows a greater focus on documentation more clearly. AI-generated code especially in complex tasks showed improved readability due to higher comment density and the use of clear variable names. These results show that AI tools such as ChatGPT can enhance code quality, especially in the software industry where readability and comprehensibility are critical. © 2024 IEEE.

关键词： software quality

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Speech Emotion Recognition Using ML 7

Speech Emotion Recognition Using ML

引用

7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Niharika, S.M. Soumya, A. Rv College of Engineering Dept. of Computer Science & Engineering India

ISBN: (纸本)9798350343144

Due to its numerous uses in industries including human-computer interaction, healthcare, and entertainment, speech emotion recognition (SER) has attracted a lot of interest recently. The use of Convolutional Neural Networks (CNN) in combination with the Librosa library for SER is used to recognise emotions from audio data. The capacity to automatically recognise and categorise emotions from spoken words can revolutionise many applications since emotions are a crucial component of human communication. Here, CNNs-known for their skill in image analysis-are adapted to the spectrogram representation of audio, efficiently identifying distinguishing characteristics. To improve the reliability of emotion identification, Librosa, a Python library for music and audio analysis, offers crucial capabilities for preprocessing and feature extraction from audio data. The architecture of the CNN model, which includes of convolutional layers for feature extraction and pooling layers for dimensionality reduction, is required to build the model. To increase model generality, methods for data augmentation and transfer learning are employed. The two feature extraction capabilities of Librosa, chroma features and mel-frequency cepstral coefficients (MFCCs), are emphasised for their effectiveness in extracting emotional cues from audio data. It has been proved via considerable research that CNNs and Librosa together produce state-of-the-art results in a variety of emotion identification tasks, considerably improving SER accuracy. This study has broad repercussions, from enhancing the emotional intelligence of virtual assistants to helping mental health practitioners monitor patients' emotional health through speech analysis. In summary, the combination of CNNs and Librosa in SER offers a potential path for developing the discipline of emotional analysis in speech, resulting in more emotionally intelligent technology and a deeper comprehension of human emotions. © 2023 IEEE.

关键词： Feature extraction

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AI-Enabled Cyber Physical System And Battery Life Estimation For Smart Grid Applications 49

AI-Enabled Cyber Physical System And Battery Life Estimation...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： N.k, Anantha Padmanabhan Kumar, Aman Chitransh, Varun Singh, Rajeev Kumar Lal, Vivek Nandan Singh, Sanjay Kumar Dept. of Computer Science and Engineering Varanasi India Dept. of Electrical Engineering Varanasi India

ISBN: (纸本)9798350331820

In this paper, the development of an efficient three- layer Cyber Physical System (CPS) using a multi-output power electronic interface integrated with IoT-enabled modules and AI- based algorithms is presented. The power electronic interface layer of the proposed CPS eliminates the need of bulky electrolytic capacitors and reduces the switching loss. This layer facilitates DC to DC and DC to three-phase AC power conversion for battery charging and smart-grid applications respectively. AI-based algorithms are utilized for accurately predicting the Remaining Useful Life (RUL) of batteries. The performance of the proposed CPS is validated through simulation and experimental measurement results using a 700 W prototype system. The measurement results show that the proposed CPS enables real-time monitoring, accurate prediction of RUL, predictive maintenance decisions, data analytics, and optimized power flow. © 2023 IEEE.

关键词： Cyber Physical System

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Enhancing DNA Sequence Classification Through Machine Learning: A Comparative Study on Human and Chimpanzee Genomic Data

Enhancing DNA Sequence Classification Through Machine Learni...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Pandya, Kushagra Hathi, Heli Ravikumar, R.N. Nadarajan, Sivakumar Praveen, Manoj Pal, Vinod Kumar Tss Consultancy Pvt. Ltd. Rajkot India Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Velalar College of Engineering and Technology Dept. of Ai & Ds Erode India Marwadi University Dept. of Computer Science Rajkot India

ISBN: (纸本)9798350379716

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance. © 2024 IEEE.

关键词： Contrastive Learning

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Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna Federated Learning 21

Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna...

引用

21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

ISBN: (纸本)9783903176553

We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and uplink over-the-air aggregation, we derive the global model update expression over communication rounds. We then obtain an upper bound on the expected global loss function, capturing the downlink and uplink beamforming and receiver noise effect. We propose a low-complexity joint beamforming algorithm to minimize this upper bound, which employs alternating optimization to breakdown the problem into three subproblems, each solved via closed-form gradient updates. Simulation under practical wireless system setup shows that our proposed joint beamforming design solution substantially outperforms the conventional separate-link design approach and nearly attains the performance of ideal FL with error-free communication links. © 2023 IFIP.

关键词： Beamforming

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Modeling of Weight Perception Based on Subjectivity by Considering Individual Differences 13

Modeling of Weight Perception Based on Subjectivity by Consi...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kushida, Daisuke Nakajima, Kei Tottori University Faculty of Engineering Dept. Electrical Engineering and Computer Science Tottori Japan Graduate School of Sustainability Science Tottori University Dept. Engineering Tottori Japan

ISBN: (纸本)9798350373332

The minimum amount of perception that humans can perceive is called the stimulus threshold. This study aims to clarify the stimulus threshold of weight perception for the biceps brachii muscle based on a method of constant stimuli. In general, the stimulus threshold depends on the individual. The authors consider that physical characteristics are related to individual differences, construct a model of the relationship between physical characteristics and stimulus thresholds, and attempt to estimate stimulus thresholds from physical characteristics. The constructed model is evaluated via cross-validation and found to be able to estimate stimulus thresholds from physical characteristics with an 85% accuracy. © 2024 IEEE.

关键词： individual difference modeling stimuli threshold subjectivity weight perception

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Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique 3

Faulty Classes Prediction in Object-Oriented Programming Usi...

引用

3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahfuz, Nagib Shill, Pintu Chandra North Western University Dept. of Computer Science and Engineering Khulna Bangladesh Khulna University of Engineering and Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350345360

Class is one of the fundamental concepts of the object-oriented paradigm and has been scrutinized since the developers moved on from procedural programming design. In software fault prediction, the legalization of software metrics is essential. As a handful of software metrics suites exist, it is a very hard task to predict the defective classes flawlessly using a particular set of metrics suites. However, it is a rational approach to use only the object-oriented metrics that are directly relatable to the class definitions in the code that helps the developers foresee the errors in defining the classes and minimize the errors as much as possible. This paper utilized twelve object-oriented metrics selected from various metrics suites. The dagging ensemble model is merged with three well-known classification algorithms (Naive Bayes, Multilayer Perceptron, J48 Decision Tree) individually and applied to twelve java projects. The study depicts that the proposed ensemble method gives improved outcomes that are statistically significant when merged with Naive Bayes and Multilayer Perceptron. The proposed ensemble method shows improvements up to 12.5% in accuracy and 15% in F-Score. © 2023 IEEE.

关键词： Object oriented programming

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Implementing HR Management System with an AI Powered ChatBot 3

Implementing HR Management System with an AI Powered ChatBot

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susitra, D. Pratheesh, G. Karthiga, J.D. Dhanalakshmi, K. Shamreen Ahamed, B. Sylvia Grace, J. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Human Resource (HR) management is one important section that increases the success of an organization to handle some important functions like employee data manipulation, attendance tracking, payroll processing, and performance evaluation. With the management of these processes becoming key to the success of small-scale startups, it poses a very big challenge for many of them to be able to manage these processes efficiently and cost efficient. By contrast, the old era of traditional HR systems was based on database driven architectures that are expensive in terms of operational cost, require storage space and complex infrastructural needs. These limitations often make traditional HR systems impossible for a startup that would prefer lightweight, affordable, and scalable solutions. Startups need robust platforms to streamline HR work streams, but often lack with financial resources for building complex database driven systems. In this paper, an AI powered HR management system is developed to simplify employee management processes for small scale startups. In this system. cache memory is used instead of traditional databases, thereby the operational cost of the system will be reduced for storage and retrieval of employee details. Automated Attendance Management enhanced with Face Recognition technology, Payroll Management, Leave Management, Feedback Submission modules are included. It is integrated with an AI powered chatbot, which facilitates a smooth interaction by the employees with query for information, submit leave requests and pull up all payroll information etc. A user-friendly dashboard for a consolidated view of employee profiles, attendance records, leave approvals, and payroll data can be served over an optimized and time efficient HR workflow. This system addresses the needs of the startups such as flexible usage, cost effective operations and improved resource management. © 2025 IEEE.

关键词： AI powered chatbot for Small-scale startups using cache memory attendance management face recognition HR Management System leave management payroll management

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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IoT-Enabled Covert Surveillance Camera Identification through IR Sensor 2

IoT-Enabled Covert Surveillance Camera Identification throug...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ruksana, Shaik Imran, Shaik Farheen, Shaik Fatima Kireeti, Krovi Mohan Sailaja, K.L. VR Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India VR Siddhartha Engineering College Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350371314

A rising number of spy cameras installed in sensitive locations globally poses an escalating threat to personal privacy. Detecting these small and well-concealed cameras with the naked eye is challenging. Despite the abundance of Android apps, accurate detection remains elusive, with many requiring subscriptions and some charging users without prior notice. Furthermore, most IoT devices are designed to detect wired cameras, overlooking the prevalence of wireless spy cameras. Existing studies often rely on impractical methods, such as wireless transmission detection. The primary objective of this paper is to develop a cost-effective, scalable, robust, and user-friendly spy camera detector. This detector identifies all night vision cameras emitting infrared light, promptly alerting the user through a beep sound from the attached buzzer. In addressing the imperative need for heightened privacy protection, the IoT spy camera detector adept.y spots concealed cameras via IR signals. By offering user-friendly location alerts through a reassuring buzzer, it actively contributes to enhancing security measures against evolving privacy threats, creating a safer environment for all. © 2024 IEEE.

关键词： Cost effectiveness

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