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software Fault Prediction Using FeatBoost Feature Selection Algorithm 2

Software Fault Prediction Using FeatBoost Feature Selection ...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Medicharla, Sirisha Kumar, Shubham Devarakonda, Praphul Agrawalla, Bikash Reddy, B Ramachandra Department of CSE National Institute of Technology Jharkhand Jamshedpur India

A critical aspect of software engineering is software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. © 2024 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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Research on Deep learning-Based Algorithms for Medical Image Characterisation 2

Research on Deep Learning-Based Algorithms for Medical Image...

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2nd international conference on Data Science and Information System, ICDSIS 2024

作者： Yu, Yunxia Qi, Zhiyong Xu, Ling Zhou, Xiong Jingchu University Of Technology Big Data Research Center Jingmen China College Of Computer Science Wuhan Vocational College Of Software And Engineering Wuhan China Shayang People's Hospital Jingmen China

ISBN: (纸本)9798350381665

Lung disease is a worldwide health problem, and accurate image diagnosis of lung disease is crucial for patient treatment and management. In recent years, deep learning has made significant progress in the field of medical image analysis, in which Graph Neural Networks (GNNs), as a special deep learning model, show great potential in lung disease diagnosis. In this study, we propose a graph neural network-based model for lung disease diagnosis, aiming to automate the analysis of lung images and lesion detection. First, we represent the lung images as a graph structure, where each pathogenic region represents a node in the graph. Then, the adjacency matrix and node features of the graph are constructed, and the image is subjected to feature extraction and representation learning using a graph neural network, which enables the model to capture the structural and contextual information in the image. To further improve the performance of the model, we introduce multilayer graph convolution and multilayer perceptron to enhance the sensitivity and differentiation ability of the graph neural network for lung lesions. We conducted experimental evaluations based on publicly available lung image datasets and compared them with traditional machine learning methods as well as other deep learning models. The experimental results show that the proposed graph neural network model achieves excellent performance on the lung disease diagnosis task. © 2024 IEEE.

关键词： Multilayers

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Elevating Industrial Picker Arm through software Mechanic Fusion 2

Elevating Industrial Picker Arm through Software Mechanic Fu...

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2nd international conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Patil, Rupali S. Apraj, Aniket Bhosale, Sahil Borikar, Srushti Chauhan, Anushka Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Engineering Belpada Maharashtra Navi Mumbai India

ISBN: (纸本)9798350367461

The manual operations that are performed in logistic warehouses like the separation of packages from clusters and their placement in the appropriate drop zones require immense human effort. Existing large production facilities use robotic arms for segregation, but because they can only pick up packages from predefined positions and drop them in designated zones, they are unable to manage packages that are stacked randomly. Our project presents an industrial robotic arm with sophisticated weight and dimension based sensing capabilities in order to get over this restriction. This robotic arm locates desired products in clusters that are randomly placed and drops them precisely in the right spots on the conveyor belt. The arm, which uses computer vision and machine learning algorithms, provides a workable way to expedite the process of segregation in a variety of industries, such as food and beverage, medical, and more. Customizing the arm to meet industry requirements could result in a significant increase in production and efficiency while lowering the need for manual labor. © 2024 IEEE.

关键词： Robotic arms

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Research on grasp planning of automatic loading and unloading with robot twist lock 2

Research on grasp planning of automatic loading and unloadin...

引用

2nd IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Zheng, Kang Hubei Engineering Institute Huangshi435000 China

ISBN: (纸本)9798350368208

With the rapid development of modern logistics industry, container loading rate continues to increase, and the degree of automation of container handling is gradually improving. However, the assembly of container lock is still inseparable from manual work. Therefore, based on the requirements of the automation of container handling logistics process, a scheme is proposed to replace manual container lock handling with robots to improve the automation of container loading. In this paper, the industrial robot of ABB Robotics Company is adopted as the research object. ABB robot's offline programming software RobotStudio has relatively simple functions in container twist lock handling, especially in the two aspects of grasping, handling and assembly. First of all, in terms of grasping and handling torsional locks, it is impossible to flexibly plan the handling path for complex working environment, and changing the robot path is relatively complicated. In terms of the assembly of twisted locks, only fixed mode assembly operations can be carried out, and flexible intelligent assembly cannot be carried out according to site changes. In view of the above problems, this paper mainly through the secondary development of RobotStudio offline programming software and the study of machine learning algorithms, establishes a virtual simulation laboratory, simulates the grasping, handling and intelligent assembly of twist-lock, and verifies the correctness of the developed grasping scheme for container twist-lock handling with the help of ABB robot platform. The secondary development of RobotStudio offline programming software mainly focuses on the setting of target points and path planning. By analyzing the data structure of the target points, mathematical tools such as quaternion are used to simplify the entry process of the target points: through the data storage and arrangement mode of Queue, the target points are arranged one by one, and the path is constructed along with the mo

关键词： Locks (fasteners)

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Cyber-Attacks and Anomaly Detection in Networking Based on Deep learning—A Survey 1

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2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

作者： Swathi, K. Narsimha, G. Department of Computer Science and Engineering CVR College of Engineering Jawaharlal Nehru Technological University Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad India

ISBN: (数字)9789819979547

ISBN: (纸本)9789819979530

The rapid development in Internet has brought great convenience in our daily life, as well as cyber criminals focus shifted from real to virtual life as well. These security problems threaten user’s privacy and also increasing prominently because hackers launch attacks by injecting malicious software in to the network and disturbs normal flow of network activities. Traditional Artificial Intelligence specifically machine learning Algorithms is no longer effective in detecting new attack patterns and unknown abnormal behavior in the network traffic. The anomaly detection process helps in providing the report of malicious activities and gives better results. To detect abnormal traffic in network, we need a strong robust deep learning abnormal traffic detection framework. In this paper, different machine learning algorithms were studied in detecting the anomalies and cyber-attacks in network and to identify the problem in existing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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Examining Dynamic Time Warping Algorithms for Hyper Spectral Image Data 2

Examining Dynamic Time Warping Algorithms for Hyper Spectral...

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2nd international conference on Artificial Intelligence and machine learning Applications, AIMLA 2024

作者： Kumar, Ravindra Maney, Kiran Lokesh Rai, Keerti Vivekananda Global University Department of Mathematics Jaipur India School of Mangement - UG Department of Management Karnataka Bangalore India ARKA JAIN University Department of Electrical & Electronics Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

Dynamic Time Warping (DTW) algorithms have currently been implemented to provide efficient and accurate time series analysis in a selection of programs. This paper examines the performance of numerous DTW algorithms within the context of hyperspectral image records. Specifically, the focal point of the research is to determine the most suitable technique for mapping and producing effective initialized fashions for use in imaging segmentation and feature extraction responsibilities. The software of those algorithms is verified by utilizing exclusive styles of hyperspectral imagery and appearing example-precise experiments for every one of the algorithms. The GLCM texture function set is used to have a look at modifications in the statistics set with respect to distinct alterations. The overall performance evaluation metrics used to evaluate the diverse techniques are time, accuracy, and inter-pixel as opposed to intra-pixel dependencies. Results show that the SOGA-DTW is the most appropriate alternative for interpreting the hyperspectral information sets in phrases of time, accuracy, and its ability to version inter-pixel and intra-pixel relationships. The changed DTW algorithms additionally look like greater stability and robustness over varying ameliorations. Finally, the ability to create a customized version for a specific instance of statistics is highlighted as a precious function while thinking about DTW for hyperspectral imagery analysis. © 2024 IEEE.

关键词： Pixels

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Detection and Categorization of conflict Flows Within SDN Environments using machine learning Approach 2

Detection and Categorization of Conflict Flows Within SDN En...

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2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Kousalya, S. Sundararajan, Kanthimathi Vasudevan, I. Jaichitra Sheela, L. Jaba Thyagarajan, Sarunya Panimalar Engineering College M.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

software-Defined Networking (SDN) is known for its remarkable versatility and adaptability. SDN applications encompass a wide range of network management and control functionalities, including load balancing, access control, and routing. However, SDN can encounter issues stemming from conflicting traffic patterns that may degrade network performance in relation of efficiency and optimization. These conflicts arise when factors such as priority and action settings are adjusted. Furthermore, IP spoofing, commonly employed for anonymity and amplification, frequently leads to widespread distributed denial-of-service (DDoS) attacks. We've established a sufficient condition based on the duration and frequency of DDoS attacks. Notably, our sophisticated attack method generates legitimate traffic within the SIP network using reflection techniques. 'As a result, it has the ability to bypass security measures such as blacklists, IP blocking, rate limiting based on packet counts or session/transaction volumes, and automated systems designed to detect unnatural message generation. These security mechanisms are commonly employed in advanced security setups, but the system or technique in question can effectively avoid detection or restriction when interacting with digital environments.' © 2024 IEEE.

关键词： Network security

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2nd international conference on Data Science and Network engineering, ICDSNE 2024

2nd International Conference on Data Science and Network Eng...

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2nd international conference on Data Science and Network engineering, ICDSNE 2024

ISBN: (纸本)9789819783359

The proceedings contain 29 papers. The special focus in this conference is on Data Science and Network engineering. The topics include: Explainable AI Insights into a Time Series Weather Prediction Model Using Stacked LSTM;patient-Specific and Patient-Independent Seizure Prediction Using Ensemble learning Technique;an Intuitive and Modular Framework for Enhanced Human-machine Synergy;Wavelet-Transformed K-NN Pipeline for EEG-Based Eye Blink Classification with Time Wrapping;enabling Cursor Control Through Eye Movement Using Hidden Markov Model;Comparative Study: Word2Vec Versus TF-IDF in software Defect Predictions;an Improved Deep learning Framework Based on Multi-Scale Convolutional Architecture for Road Crack Detection;Knowledge Graph Relation learning Using GAN-BERT;sightAssist: A Multi-facility machine learning Approach for Empowering the Visually Impaired;Predicting COVID-19 Cases in India Using ARIMA, Prophet, LSTM and Data Analysis Using Power BI;Classification of Muti-Labeled Retinal Diseases in Retinal Fundus Images Using CNN Model ResNet18;LSTM-Based Portfolio Optimization with Gerber Covariance Estimator for Increased Robustness;handwritten Character Recognition from Small Grayscale Images Using Pre-trained Models;Forecasting Heart Disease Using Deep BI-DI Neural Networks;emo-Tune: Harnessing Emotion-Based Music for Patient Wellness;Making Data Secure in Detecting ADHD with Supervised learning;an Analysis of Automatic Question Generation Research Progress and Challenges;a Federated learning Approach Towards a Privacy-Preserving Technique for Brain Tumor Classification;low Cost Emergency Communication System for Disaster Affected Areas;a Slot-Integrated Based Partial Ground and Tapered Patch Antenna for Satellite Communications;a Review on Wireless Power Transfer Systems;application of Big Data to Traffic Generated in Mechanisms Containment on Optical Burst Switching Distributed Networks;a Real-Time Arm-Worn Sensor-Based Human Fall Alert Notification Mode

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Interactive Delta Bot with Voice Recognition and Image Processing - A Review 2

Interactive Delta Bot with Voice Recognition and Image Proce...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Mallam, Madhavi Hemantha, G. Reddy Manu, H. Mounika, K. Bhoomika, P. Keerthana, H. Department of Electronics and Communication PES Institute of Technology and Management Karnataka India Department of Electronics and Communication Madanapalle Institution of Technology and Science Andra Pradesh India Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350383867

The paper is designed to build a voice-controlled pick-and-place bot using a Raspberry Pi. Python and other open-source software are programmed to the architecture of bots with Raspberry Pi commands. The robots perform the action with a voice assistant via voice input. The system recognizes the object with the aid of image processing and can lift a maximum weight of 750 grams with a sled arm movement of 360 degrees. This robot uses the TensorFlow USB Coral accelerator to increase Raspberry Pi object detection. Interfacing of the bot with sensors and cameras to identify items in its surroundings, analysing spatial characteristics through image processing. Speech recognition technology enables natural communication with the robot, allowing users to give specific commands. This human-robot interaction ensures intuitive control and an improved user experience. The system's architecture includes a processing unit, motor controllers, cameras, microphones, and other hardware and software components. machine learning methods is utilized for voice recognition and image manipulation which is adaptive and enhances the performance. © 2024 IEEE.

关键词： Human robot interaction

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2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

2nd International Conference on Computational Intelligence i...

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2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

ISBN: (纸本)9789819979530

The proceedings contain 64 papers. The special focus in this conference is on Computational Intelligence in machine learning. The topics include: An Energy-Efficient Mechanism Using Blockchain Technology for machine-Type Communication in LTE Network;Comparison of Echo State Network with ANN-Based Forecasting Model for Solar Power Generation Forecasting;a Grey Wolf Integrated with Jaya Optimization Based Route Selection in IoT Network;secure software Development Life Cycle: An Approach to Reduce the Risks of Cyber Attacks in Cyber Physical Systems and Digital Twins;social Networks and Time Taken for Adoption of Organic Food Product in Virudhunagar District—An Empirical Study;usage of Generative Adversarial Network to Improve Text to Image Synthesis;Recurrent Neural Network-Based Solar Power Generation Forecasting Model in Comparison with ANN;android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and machine learning;cyber-Attacks and Anomaly Detection in Networking Based on Deep learning—A Survey;mental Health Disorder Predication Using machine learning for Online Social Media;An Application on Sweeping machines Detection Using YOLOv5 Custom Object Detection;analyze and Detect Lung Disorders Using machine learning Approaches—A Systematic Review;a Study on Predicting Skilled Employees’ Using machine learning Techniques;forensic Analysis of the Uncompressed Image Data Cluster for Estimating the Image Width;performance Comparison of Various Supervised learning Algorithms for Credit Card Fraud Detection;simple-X a General-Purpose Image Classification Model Through Investigative Approach;blockchain-Based Source Tracing System Using Deep learning: A Review;geospatial Data Visualization of an Energy Landscape and Geographical Mapping of a Power Transmission Line Distribution Network;pollX—Polling Assistant.

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