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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Leveraging AI Techniques for Enhanced Software Defect Prediction

Leveraging AI Techniques for Enhanced Software Defect Predic...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Mohd Mustaqeem Suhel Mustajab Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

In software engineering (SE), software defect prediction (SDP) plays a pivotal role in enhancing software quality by detecting defects during development. However, the challenge of imbalanced datasets significantly impacts the efficiency of defect prediction models. this study leverages Artificial Intelligence (AI) techniques to overcome these challenges and improve defect prediction. We explore ensemble methods, resampling with cost-sensitive learning, and anomaly detection via Transfer learning (TL). Experiments on NASA MDP’s PC2 and AEEEM’s JDT datasets demonstrate our strategies' effectiveness, particularly in managing imbalanced data. Notable outcomes include a 95% accuracy, 97% precision, 98% recall, and an F1-score of 97% for the ensemble model on the PC2 dataset and similarly with the JDT dataset. these AI-driven techniques represent a substantial advancement in SDP. Furthermore, our One-Class Support Vector machine (SVM) anomaly detection identifies 115 anomalies in the PC2 dataset, with similar results in the JDT dataset. TL broadens predictive capabilities, providing insights from a wider context. these outcomes underscore the significant impact of AI-driven techniques in navigating the complexities of SDP and advancing defect prediction models.

关键词： Support vector machines Industries Navigation Transfer learning NASA Software quality Predictive models

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Slope Stability Predictions using machine learning Techniques

Slope Stability Predictions using Machine Learning Technique...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Arun Kumar Sahoo Jitendra Pramanik Singam Jayanthu Abhaya Kumar Samal Department of Mining Engineering National Institute of Technology Rourkela India Centurion University of Technology and Management Odisha India Department. of Computer Science and Engineering Trident Academy of Technology Bhubaneswar India

the geotechnical analysis is considered to be an important aspect in providing a safe mine working environment. It not only covers the active monitoring of open pit walls but also effectively predicts the slope deformations and failures. the approach or method used in slope failure is considered to be legitimate when it predicts the time of failure of slope prior to its actual failure. this research plays a paramount role in mitigating the risk associated with slope failure. the aim of this study is to demonstrate the application of the machine learning technique to effectively predict the occurrence of slope failure. Specifically, random forest, support vector classifier, and logistic regression algorithms are employed to assess the stability of the slopes. the dataset included in the study uses cohesion, angle of friction, and unit weight of the designed slopes. the performance of the implemented machine learning models for the factor of safety (FOS) prediction is analysed and compared.

关键词： Analytical models Support vector machine classification Production Predictive models Prediction algorithms Stability analysis Safety

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A Simulation Modeling Study of Cooperation Evolution Based on Subjective learning and Influence Preference 23

A Simulation Modeling Study of Cooperation Evolution Based o...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Yu Wang Xin He School of Information Engineering Yangzhou University China

ISBN: (纸本)9798400709517

In reality, the behavioral choices of individuals depend on their subjective cognition formed in the process of repeated interactions. the formation of cognition is not only based on the game situation but also influenced by the surrounding individuals. the influence of the surrounding individuals will play a role in the cognition to generate choice preferences. In this paper, we constructed a game model based on subjective game theory and influence preference, explored the co-evolution of subjective cognition and individual influence in structured populations, and investigated the evolution of cooperative behavior. the influence of individuals in the population is heterogeneous. the influence of the surrounding individuals, as one of the key factors, interferes with the formation of cognition, causing individuals to develop preferences in behavioral choices. the mechanism of the influence effect was analyzed by simulation experiments. the existence of influence preference has a significant impact on cooperation. the results show that the size of influence and the magnitude of the increase or decrease in influence are not the greater the better. there is an optimal values, and the relative control over influence can better sustain the cooperation. Compared with the case of no influence mechanism, individuals have a better tendency to learn cooperation in subjective cognition, and constantly tend to choose cooperation. And the system is finally in a relative equilibrium state in which cooperation prevails.

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4th IFIP TC 12 international conference on Intelligence Science, ICIS 2020

4th IFIP TC 12 International Conference on Intelligence Scie...

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4th IFIP TC 12 international conference on Intelligence Science, ICIS 2020

ISBN: (纸本)9783030748258

the proceedings contain 27 papers. the special focus in this conference is on Intelligence Science. the topics include: Trilevel Multi-criteria Decision Analysis Based on three-Way Decision;Recurrent Self-evolving Takagi–Sugeno–Kan Fuzzy Neural Network (RST-FNN) Based Type-2 Diabetic Modeling;granulated Tables with Frequency by Discretization and their application;person Authentication by Gait Data from Smartphone Sensors Using Convolutional Autoencoder;research on Personal Credit Risk Assessment Model Based on Instance-Based Transfer learning;from Texts to Classification Knowledge;ANAS: Sentence Similarity Calculation Based on Automatic Neural Architecture Search;fully Interval Integer Transhipment Problem- A Solution Approach;Novel Image Compression and Deblocking Approach Using BPN and Deep Neural Network Architecture;characterization of Orderly Behavior of Human Crowd in Videos Using Deep learning;some Discussions on Subjectivity of machine and its Function;stability Analysis of Imprecise Prey-Predator Model;comparative Performance Study on Human Activity Recognition with Deep Neural Networks;beam and Ball Plant System Controlling Using Intuitionistic Fuzzy Control;application of Pinching Method to Quantify Sensitivity of Reactivity Coefficients on Power Defect;computation with Democracy: An Intelligent System;Economy and Unemployment Due to COVID19: Secondary Research;optimal Control of Dengue-Chikungunya Co-infection: A Mathematical Study;comparative Analysis of machine learning Algorithms for Categorizing Eye Diseases;hexagon of Intelligence;interactive Granular Computing Model for Intelligent Systems;a Framework for the Approximation of Relations;logical Treatment of Incomplete/Uncertain Information Relying on Different Systems of Rough Sets;possibility theory and Possibilistic Logic: Tools for Reasoning Under and About Incomplete Information;similarity-Based Rough Sets with Annotation Using Deep learning.

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Prospects of Ant Colony Optimization(ACO) in Various Domains

Prospects of Ant Colony Optimization(ACO) in Various Domains

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international conference on Circuits, Communication, Control and Computing

作者： Preethi S. H. Darius Joshua Devadason Darius Gnanaraj Solomon Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore India School of Mechanical Engineering Vellore Institute of Technology Vellore India

the various prospects of Ant Colony Optimization(ACO) highlighting the versatile nature of the algorithm in a wide range of resource allocation problems in domains is presented. the problem representation in each of the domains is illustrated for a researcher keen on working with ACO. the mechanics of ACO that control and enhance its performance are also explored in each application. the various libraries available for testing discrete and continuous problems in ACO are demonstrated. ACO has some limitations and the prospects of hybrid versions of ACO and the scope for further research are presented and discussed.

关键词： Ant colony optimization Costs machine learning Routing Libraries Safety Resource management

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Skin Melanoma diagnosis using machine learning and ABCD rule

Skin Melanoma diagnosis using machine learning and ABCD rule

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Science and Information Technology in Smart Administration (ICSINTESA), international conference of

作者： Zahra B. Kadeem Qusay O. Mosa dept. Computer Science College of Computer Science and Information Technology Diwaniyah Iraq

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Skin cancer, being the most predominant type of malignant growth, represents more than half of all cancer instances. While melanoma is less common than other skin cancers, it poses the highest risk due to its tendency to metastasize and potentially result in mortality. Nonetheless, early detection significantly increases the chances of curing melanoma. the detection of melanoma poses a considerable challenge, primarily attributed to the minimal interclass variance observed between images of melanoma and non-melanoma, alongside the heightened interclass variance within melanoma images. Hence, it is imperative to formulate a computer-aided Diagnosis (CAD) to identify such lesions and facilitate early disease diagnosis to enhance the probability of patient survival. the proposed methodology consists of a sequence of four distinct phases. the initial stage involves preprocessing, which incorporates the application of filtering and contrast enhancement algorithms such as the Median filter, Dull razor, and Histogram Equalization. the segmentation process proceeds by utilizing Otsu methods in order to detect any anomalies. Subsequently, the following phase involves the extraction of features through GLCM for textural attributes and ABCD base parameters, encompassing asymmetry, border irregularity, color, and diameter. Following this, a model based on artificial neural networks (ANN) is developed by leveraging these extracted traits to discern instances of skin cancer within the provided dataset. this methodology achieves a commendable overall accuracy rate of 99%. Dermatologists may derive significant assistance in their clinical decision-making procedures from this promising breakthrough.

关键词： Training Solid modeling Accuracy Image color analysis Shape Decision making Melanoma Feature extraction Skin Surges

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Cloud-IoT Based Smart Healthcare Framework: application and Trends 22

Cloud-IoT Based Smart Healthcare Framework: Application and ...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Navneet Kumar Rajpoot Prabhdeep Singh Bhaskar Pant Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781450399937

Healthcare is a core part of living a full and healthy life. there is a pressing need for high-quality, cutting-edge healthcare systems due to the consistently rising prevalence of chronic diseases and the attendant demand for medical attention, hospitals, physicians, and nurses among patients at high risk for consequences. the term "IoT" relates to the interconnection of several devices that use a network to carry out procedures and provide the services necessary to maintain human life. Recent studies have revealed that IoT can improve healthcare system strains by delivering effective solutions. On the other side, cloud computing is utilized to store massive amounts of data and offers scalable, nearly limitless computing capabilities online as a service. In this paper, we have studied many smart healthcare technologies that rely on the cloud as well as the IoT to improve healthcare systems. these frameworks for healthcare systems allow for the seamless sharing of information and services between patients, their friends and families, medical professionals, and the institutions where they receive care, such as clinics, hospitals, and labs. With the help of this technology, performance, efficiency, and resource usage have all improved.

关键词： Cloud IoT Integration

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Structured Approach for Relation Extraction in Legal Documents

Structured Approach for Relation Extraction in Legal Documen...

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Global conference for Advancement in Technology (GCAT)

作者： Anjali K Sasidharan R Rahulnath Department Of Computer Science And Application Amrita School Of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

Legal documents contain a wealth of valuable information, making them crucial for knowledge extraction and analysis. Extracting structured relationships between entities in legal texts can significantly enhance our understanding of legal concepts and facilitate the creation of knowledge graphs. In this paper, we propose a structured approach combining relation extraction, graph reasoning, and clustering methods to effectively extract and represent legal relationships. Firstly, we employ state-of-the-art relation extraction techniques to identify relevant entities and their relationships from unstructured legal documents. these techniques leverage natural language processing (NLP) and machine learning algorithms to recognize entity pairs and assign appropriate relationship labels. Next, we leverage graph reasoning to enhance the extracted relationships by incorporating additional contextual information. By representing the legal entities and their relationships as nodes and edges in a knowledge graph, we can apply reasoning algorithms to infer implicit relationships, resolve conflicts, and expand the graph based on the legal domain’s rules and regulations. Furthermore, we integrate clustering methods to organize related legal entities and relationships into meaningful clusters within the knowledge graph. By grouping semantically similar entities and relationships, we can uncover patterns, identify common themes, and enable more effective retrieval and analysis of legal information. Our experiments demonstrate that the structured approach, combining relation extraction, graph reasoning, and clustering methods, effectively extracts and represents legal relationships in a knowledge graph. the resulting graph provides a comprehensive and organized representation of legal concepts, facilitating legal research, decision-making, and knowledge discovery in the legal domain.

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A Federated learning Mechanism for Preserving Security of Sensitive Data

A Federated Learning Mechanism for Preserving Security of Se...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Shagun Sharma Kalpna Guleria Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

Federated learning (FL) is a distributed machine learning(ML) method which allows for model training on information distributed across various servers and devices without sharing the sensitive data. It works by aggregating updates and weight values from the devices while keeping raw data private and secure. FL is a field which is widely used in finance, healthcare, and IoT systems along with personalized recommendations. Traditional ML and deep learning (DL) models are capable of producing good outcomes, however, these algorithms do not provide data security due to directly working on the raw data. Moreover, the FL can avoid security concerns due to its behaviour of sending the model to the data instead of working on the raw data itself. In this work, the FL has been introduced along with its three kinds of architectures. the motivation behind this work is that healthcare departments and financial institutes avoid sharing patients' sensitive information due to privacy concerns. However, the ML and DL models require a vast volume of data for accurate prediction outcomes. Hence, FL has been identified as the best model for working on sensitive data without privacy concerns. Furthermore, the work has also introduced the core challenges and application areas of FL. Moreover, in future, the FL architectures will be implemented with the healthcare dataset to increase data security and efficiently identify the outcomes.

关键词： Training Solid modeling Federated learning Data security Urban areas Finance Medical services

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Sky-Net: A Deep learning Approach to Predicting Lung Function Decline in Sufferers of Idiopathic Pulmonary Fibrosis 22

Sky-Net: A Deep Learning Approach to Predicting Lung Functio...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Arjun Taneja Anju Yadav Department of Information Technology Manipal University Jaipur India IT Manipal University Jaipur India

ISBN: (纸本)9781450399937

Idiopathic Pulmonary Fibrosis (IPF) is a kind of Interstitial Lung Disease (ILD) that can be recognized by observing an atypical formation and accumulation of fibrotic tissue in the lungs. the lung's alveolar structure is damaged; as a result, people afflicted with IPF experience increasingly restricted lung capacity as time progresses. Diagnosis of this disease is typically performed by analyzing the patient's computed tomography (CT) scans and measuring their Forced Vital Capacity (FVC) using a Spirometer. However, the absence of an apparent cause of IPF restricts the ability of doctors to accurately diagnose the patient. Furthermore, IPF progression in patients is highly volatile and unpredictable, which means that one patient's health could deteriorate significantly quicker compared to another. Taking the problems mentioned above into account, in this paper a 3-layer ResNet machine learning model is proposed that determines the rate of lung function decline of sufferers from IPF. Proposed model is applied on the “OSIC Pulmonary Fibrosis Progression” dataset publicly available on Kaggle, and compare it against various state-of-the-art models and winning Kaggle entries.

关键词： Computed Tomography

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