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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Vinod Veeramachaneni Pradeep Kumar Mallick Subhashree Rout Piyush Kumar Pareek Department of Information Technology Colorado Technical University USA School of Computer Engineering KIIT Deemed to be University Department of Computer Science and Engineering Nalanda Institute of Technology Bhubaneswar Odisha Nitte Meenakshi Institute of Technology Yelahanka Bengaluru Karnataka India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

software systems are growing in size and complexity. These properties make software defect prevention challenging. Thus, automatically predicting software module defects can help engineers conserve resources. Several ways have been presented to discover and fix these issues cheaply. However, these solutions may need a performance bump. This paper introduces the Multi-Deep Convolutional Neural Network to improve binary and multi-class software classification. Instead of one-dimensional CNNs, the proposed model captures n-gram characteristics inside and between datasets using a two-dimensional multi-scale convolutional technique. Advanced crow search optimisation algorithm (ACSOA) is used to optimally select model parameters. Besides Apache Active MQ, the study employed Eclipse data sets to evaluate the recommended deep learning method. F1-score, recall, accuracy, and precision were utilized to validate the model. The results show that the suggested strategy finds software vulnerabilities better and more reliably than current models.

关键词： Accuracy Computational modeling Prevention and mitigation software algorithms Optimization methods Computer architecture Syntactics software systems software reliability Convolutional neural networks

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algorithms, HARDWARE, AND software FOR A DIGITAL SIGNAL PROCESSOR MICROCOMPUTER-BASED SPEECH PROCESSOR IN A MULTIELECTRODE COCHLEAR IMPLANT SYSTEM

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1989年第6期36卷 573-584页

作者： MORRIS, LR BARSZCZEWSKI, P BELL NO RES SCI STAFF MONTREAL QUEBEC CANADA

The authors discuss software and hardware developed to create a powerful, inexpensive, compact digital signal processing system that in real time extracts a low-bit-rate linear predictive coding (LPC) speech system model. The model parameters derived include accurate spectral envelope, format, pitch, and amplitude information. The system is based on the Texas Instruments TMS320 family, and the most compact realization requires only three chips (TMS320E17, A/D-D/A, op-amp), consuming a total of less than 0.5 W. The processor is part of a programmable cochlear implant system under development by a multi-university Canadian team, but also the speech features extracted can serve as input to vibrotactile and visual aids to the hearing handicapped.< >

关键词： software algorithms Signal processing algorithms Hardware Digital signal processors Speech processing Cochlear implants Linear predictive coding Power system modeling Digital signal processing chips Real time systems

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Methods and software Tools for Converting Road Marking Line Segmentation Mask to Object Representation for Highly Automated Vehicles

Methods and Software Tools for Converting Road Marking Line ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Danil Dolgov Alexander Dolgov Imil Khairullin Faculty of Information Systems and Technologies Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper describes the development of an algorithm for converting segmentation masks derived from neural networks into ordered sets of point coordinates representing road markings for highly automated vehicles (HAVs). Several methods are considered, including the nearest neighbor method, polynomial approximation, Hough transform, and parallel contouring. Performance tests and complexity analyses are performed for each method. The parallel contour method with dynamic search for opposite points was the most effective, demonstrating linear complexity and high accuracy in approximating road marking contours. This solution ensures efficient processing of large real-time datasets, critical for HAV navigation. The study integrates the algorithm into a unified system, combining segmentation with mask-to-line conversion, enhancing autonomous driving precision.

关键词： Industries Accuracy Navigation Roads Neural networks software algorithms Transforms Approximation algorithms Polynomials Real-time systems

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Dynamic Load Balancing on Switches of software Defined Network Managed by OpenDayLight Controller

Dynamic Load Balancing on Switches of Software Defined Netwo...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Manasa Kulkarni Bhargavi Goswami Muhammad Furqan Joy Paulose CHRIST (Deemed to be University) Bangalore India Queensland University of Technology Brisbane Australia

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In recent times, the world is becoming a global village where connectivity is a new norm irrespective of geographical location. Within corporate networks, huge setbacks are faced due to a lack of efficient resource management. Load balancing is inevitable to cater to a reliable, faster, and congestion-free communication experience for exponentially increasing online enterprises. Dynamic network resource management for high performance and low data transmission latency in a network is necessary. The major issue faced by the traditional network is that it relies on static hardware switches. software-Defined Network approaches paved a way to overcome the limitations of traditional networks. This research proposes the Dynamic Load Balancing Algorithm for software-Defined Networks to utilize network resources optimally. The major function of the proposed algorithm is to determine alternative paths and further distribute the incoming and outgoing network flows to achieve optimum network resource utilization with faster traffic flow completion. The experiment is performed with the OpenDayLight Controller on the Mininet simulator, which emulates the network with the novel scheme. The results prove that the proposed solution has accomplished the benchmarks of optimum throughput, reduced redundancy, and reduced flow completion time.

关键词： Heuristic algorithms software algorithms Redundancy Packet loss Load management Throughput Dynamic scheduling Control systems Reliability software defined networking

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software tool-set for automated quantum system identification and device bring up

Software tool-set for automated quantum system identificatio...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Anurag Saha Roy Kevin Pack Nicolas Wittler Shai Machnes Peter Grünberg Institute – Quantum Computing Analytics (PGI 12) Forschungszentrum Jülich Jülich Germany Theoretical Physics Saarland University Saarbrücken Germany Qruise GmbH Saarbrücken Germany

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

We present a software tool-set which combines the theoretical, optimal control view of quantum devices with the practical operation and characterization tasks required for quantum computing. In the same framework, we perform model-based simulations to create control schemes, calibrate these controls in a closed-loop with the device (or in this demo —by emulating the experimental process) and finally improve the system model through minimization of the mismatch between simulation and experiment, resulting in a digital twin of the device. The model based simulator is implemented using TensorFlow, for numeric efficiency, scalability and to make use of automatic differentiation, which enables gradient-based optimization for arbitrary models and control schemes. Optimizations are carried out with a collection of state-of-the-art algorithms originated in the field of machine learning. All of this comes with a user-friendly Qiskit interface, which allows end-users to easily simulate their quantum circuits on a high-fidelity differentiable physics simulator.

关键词： Quantum system Computational modeling Scalability software algorithms Process control software Numerical models Object recognition Quantum circuit Optimization

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Boosting software Quality: A Machine Learning Technique for Predicting Defects

Boosting Software Quality: A Machine Learning Technique for ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Akilan E. Daniel Alwin Infant P Jainish G R Computer science & Engineering Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Computer Science and Engineering Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges.

关键词： Computational modeling software algorithms software quality Predictive models Prediction algorithms Data models software reliability Decision trees software measurement Embedded software

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Integrating AI into software Engineering: A Critical Review and Future Directions

Integrating AI into Software Engineering: A Critical Review ...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Aditya Pai H Sharath K R Kavita Babalad Guru Prasad M S Chandrappa S Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Department of Computer Science and Engineering Graphic Era (Deemed-to-be University) Dehradun India School of Computing and Information Technology REVA University Bangalore India School of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Department of Computer Applications Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The field of Artificial Intelligence (AI) has been witnessing a huge demand in the field of research, tools development, and applications of deployment. There are multiple software companies which are shifting their focus to developing AI systems, many are also deploying the AI paradigms to their current system. Correspondingly, the academic research community has also used AI paradigms to solve the traditional software engineering problems. AI has become very useful to the software engineering community. The paper also uses a survey of the most commonplace methods of using AI into the software Engineering, that covers the methods for SE phases such as Requirements, Design, Development, Testing, Release, and Maintenance. The paper also aims in answering to the sufficient intelligence in the SE lifecycle, this evaluates the overlapping of the SE and AI domains, and challenge the current conventional wisdom of the state-of-the-art.

关键词： software testing Surveys software maintenance Reviews software algorithms Prediction algorithms Requirements engineering Artificial intelligence software engineering software development management

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Ethical Challenges and Frameworks in AI-Driven software Development and Testing

Ethical Challenges and Frameworks in AI-Driven Software Deve...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Anujkumarsinh Donvir Gaurav Sharma Application Development Wayne NJ USA AI Test Automation Solution Architect Atlanta GA USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Artificial Intelligence (AI) has revolutionized and transformed the landscape of software development and testing by introducing new efficiencies and capabilities through advancements like Generative AI (GenAI) and Large Language Models (LLMs). While these technologies bring major benefits in terms of productivity, personalization, and innovation, they also raise critical ethical challenges, such as biases, lack of transparency, data privacy concerns, and potential negative societal impacts. This paper examines the ethical considerations involved in developing such advanced AI systems as well using AI systems within software development and testing. It explores existing ethical frameworks and principles provided by leading organizations, emphasizing core concepts like human-centered design, accountability, transparency, fairness, and privacy. Practical strategies for integrating ethical practices throughout the AI development lifecycle are discussed, with a strong emphasis on the need for continuous ethical evaluation. The paper explores the ethical landscape of AI in software development, addressing challenges like algorithmic bias, data security, and broader societal impacts. Real-world case studies presented in the paper demonstrate the consequences of neglecting ethical considerations. Looking forward, the paper suggests future directions, including the development of unified ethical standards, collaborative ethical auditing, regulatory advancements, and higher societal engagement.

关键词： Ethics Technological innovation Data privacy Standards organizations software algorithms Collaboration Stakeholders Artificial intelligence software development management Testing

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Enhancing software Quality with Fault Detection and Prediction based on AI-Driven Model

Enhancing Software Quality with Fault Detection and Predicti...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Ramesh Bishukarma Sumeet Mathur Puneet Matapurkar Sandeep Gupta University of Waikato NZ – Joint Institute at Zhejiang University Hangzhou China Department of Mathematical Sciences & Computer Application Bundelkhand University Jhansi India Department of Computer Science Engineering SATI Vidisha India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Early defect detection and prediction are essential in software engineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on the CM1 dataset from NASA’s software metrics program. The data preprocessing steps include normalisation, feature selection, and the use of the ADASYN algorithm to enhance minority class detection. Faults are predicted using ML classifiers: Extra Trees (ET), Support Vector Machine (SVM), KNN, RF, MLP, and NB. F1 score, recall, accuracy, and precision are a metrics utilised to assess a models. When it comes to defect prediction, the Extra Trees Classifier (ETC) model outperforms all of the others with an accuracy91.51%, precision91.76%, and recall99%. Although the Naive Bayes model shows lower accuracy (77.61%) and efficiency, it offers a simpler approach for fault detection. Future research in this area should be guided by these findings, which highlight the promise of AI-driven approaches to enhance frameworks for software defect prediction and quality assurance.

关键词： Support vector machines Accuracy Fault detection software algorithms software quality Predictive models Nearest neighbor methods software measurement Classification tree analysis software engineering

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A Comprehensive Evaluation of Machine Learning Techniques for Predicting software Bugs

A Comprehensive Evaluation of Machine Learning Techniques fo...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Fahim Faysal Sakib Md. Sajjadul Islam Juel Zinniya Tafannum Jihad Shahriar Joy Razuan Karim Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

software Bug Prediction (SBP) is essential to control overall software quality. As systems become complex, faults increase, and early prediction is needed to enhance quality and reduce expenses. This paper provides a technical study of thirteen Machine Learning (ML) techniques for predicting software bugs by utilizing NASA's Promise Repository datasets. The study examines the effectiveness of these classification techniques focusing on evaluation metrics such as Precision, Recall, F-measure, Accuracy, Error Rate, FPR, FNR, ROC, and MCC. The results indicate that Random Forest (RF) achieved the highest accuracy on PC1 (94.96%) and CM1 (96.60%), while Multilayer Perceptron (MLP) scored 85.6% on KC1, and Logistic Regression (LR) got 86.25% on KC2. Regarding error rates, RF had the lowest on PC1 (0.050), LR on KC2 (0.137). For FPR, SVM, MLP, LR, RBF, ANN, and AdaBoost excelled on PC1 (0.00). For FNR, Decision Tree (DT) performed best on PC1 (0.611), KC2 (0.518), and LDA on CM1 (0.500).

关键词： Radio frequency Support vector machines Measurement Accuracy Error analysis Computer bugs software algorithms Signal processing algorithms Prediction algorithms Random forests

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