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machine learning-based Weather Forecasting for Precision Agriculture: Model Development, Evaluation, and Predictive Insights 3

Machine Learning-based Weather Forecasting for Precision Agr...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chaganti, Pavanchandra Vishal Manitha, P.V. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering India

ISBN: (纸本)9798350343632

Weather forecasting performs a critical function in agricultural practices, influencing crop management, irrigation and pest manage choices. The paper examines the usage of coverage specialist strategies to refine climate forecasting in agriculture. Forecasting structures that analyze historical weather information, sensor readings, and outdoor climate forecasts can offer valuable insights into crop yields, most effective planting instances, and useful resource allocation. The paper discusses the hardware and software program interfaces required for data segregating, preprocessing, and model training. It emphasizes the significance of characteristic engineering, model evaluation, and actual-time choice guide systems. Ultimately, the study objectives to grow international food demand and empower farmers through correct and timely climate forecasting, promoting sustainable and green agricultural practices throughout a change in weather. The research project pioneers precision agriculture by using numerous system getting to know models along with Random Forest Regressor, Linear Regression, and Support Vector machine to predict crop yields primarily based on complicated relationships with climate parameters, all backed by robust information control and feature importance analysis. © 2023 IEEE.

关键词： Linear Regression machine learning Random Forest SVM. Weather Forecasting

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On the suitability of using ANSYS Motor-CAD software for teaching special electrical machines 10

On the suitability of using ANSYS Motor-CAD software for tea...

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10th international conference on Modern Power Systems, MPS 2023

作者： Szabó, Loránd Coteţ, Florina-Ambrozia Technical University of Cluj-Napoca Electrical Machines and Drives Dept. Cluj-Napoca Romania

ISBN: (纸本)9798350326826

Special electrical machines are sophisticated electromagnetic devices in terms of both their design and behavior. As a result, their study is extensive and calls for a lot of multidisciplinary knowledge. The academic staff members who are participating in their teaching face a variety of educational difficulties as they work to ensure that the students fully comprehend all relevant matters. Modern teaching methods (also including distance learning) require advanced software tools to be used by the students. The ANSYS Motor-CAD software, which is frequently applied in the development of various electrical machine types, can also be used in higher education because it makes it simple to build interactive models of a wide range of electrical machines and because any changes made to the machine structures are immediately graphically displayed. With the aid of this flexible program, it is simple and accurate to conduct an electromagnetic analysis of the machines under investigation. The simulations also result in a wide range of electrical and mechanical properties for the examined electrical machines. The students may instantly see and feel the impacts of altering the topologies, as well as the various sizes and materials of the machines they are studying by utilizing this software program, which helps them comprehend their working principle and particular features. The easy-to-be-performed studies make it possible for electrical engineering students to improve their design abilities. The paper discusses two straightforward studies carried out utilizing the ANSYS Motor-CAD program. The emphasis is on how useful they are for ***. electrical engineering students learning about special electrical machines. © 2023 IEEE.

关键词： Students

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Proceedings - 2023 13th international conference on software Technology and engineering, ICSTE 2023

Proceedings - 2023 13th International Conference on Software...

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13th international conference on software Technology and engineering, ICSTE 2023

ISBN: (纸本)9798350371475

The proceedings contain 24 papers. The topics discussed include: CareForPaws: a mobile application for pet adoption and other services with location-based technology developed in react native framework;WellnessWise: user experience design of the proposed mobile application for physical and mental health self-care;local outlier reclassifier (LORec): a method for relocating local outliers generated by k-means;analyzing the sentiments of Filipino commuters: an application of data science techniques through the use of machine learning algorithms;effectiveness of using fundus image data containing other retinal diseases in identifying age-related macular degeneration using image classification;Kubernetes-based edge system for managing intermittently connected IoT gateway on rail vehicle;and narrate: a user-driven web application for developing speech corpora in endangered Philippine languages and dialect.

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A novel few-shot learning approach for the classification of histological images of liver tumors

A novel few-shot learning approach for the classification of...

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Bo Wang, Qirui Li, Longjiao Yang, Jinqiao Lin, Hongfei Ding, Feng Dalian University of Technology School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350337488

The liver is an internal organ that is most commonly affected by cancer metastasis. Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. Identifying the histological category of this disease is a crucial step in determining the most appropriate treatment plan. Currently, the classification of HCC primarily relies on the observation of pathologists through a visual microscope, which is undoubtedly subjective. To address this issue, the present study conducted pathological examination of liver tumors based on few-shot learning. In order to achieve automatic identification of the three categories in HCC tissue, we propose a few-shot learning based HCC histological image classification method that combines contrastive learning (CL) using different backbone architectures with several well-known machine learning methods. We evaluate 12 backbone-classifier combinations, and the best performance is achieved by combining CL using DeiT-TinyR as the backbone architecture with support vector machine (SVM), with an accuracy of 74.42% and an F1 score of 74.58% in the 3-way-10-shot setting. Experimental results demonstrate the effectiveness of this method in NB histological image classification. © 2023 IEEE.

关键词： classification contrastive learning few-shot hepatocellular carcinoma hyperspectral image

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Challenges of Testing an Evolving Cancer Registration Support System in Practice 23

Challenges of Testing an Evolving Cancer Registration Suppor...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Laaber, Christoph Yue, Tao Ali, Shaukat Schwitalla, Thomas Nygard, Jan F. Simula Res Lab Oslo Norway Canc Registry Norway Oslo Norway UiT Arctic Univ Norway Tromso Norway

ISBN: (纸本)9798350322637

The Cancer Registry of Norway (CRN) is a public body responsible for capturing and curating cancer patient data histories to provide a unified access to research data and statistics for doctors, patients, and policymakers. For this purpose, CRN develops and operates a complex, constantly-evolving, and socio-technical software system. Recently, machine learning (ML) algorithms have been introduced into this system to augment the manual decisions made by humans with automated decision support from learned models. To ensure that the system is correct and robust and cancer patients' data are properly handled and do not violate privacy concerns, automated testing solutions are being developed. In this paper, we share the challenges that we identified when developing automated testing solutions at CRN. Such testing potentially impacts the quality of cancer data for years to come, which is also used by the system's stakeholders to make critical decisions. The challenges identified are not specific to CRN but are also valid in the context of other healthcare registries. We also provide some details on initial solutions that we are investigating to solve the identified challenges.

关键词： research challenges healthcare cancer registry software testing evolution

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Features and Supervised machine learning Based Method for Singleton Design Pattern Variants Detection 18

Features and Supervised Machine Learning Based Method for Si...

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Nacef, Abir Bahroun, Sahbi Khalfallah, Adel Ben Ahmed, Samir Tunis El Manar Univ Comp Lab Ind Syst Fac Math Phys & Nat Sci Tunis FST Tunis Tunisia Tunis El Manar Univ Higher Inst Comp Sci ISI Limt Lab Tunis Tunisia

ISBN: (纸本)9789897586477

Design patterns codify standard solutions to common problems in software design and architecture. Given their importance in improving software quality and facilitating code reuse, many types of research are proposed on their automatic detection. In this paper, we focus on singleton pattern recovery by proposing a method that can identify orthodox implementations and non-standard variants. The recovery process is based on specific data created using a set of relevant features. These features are specific information defining each variant which is extracted from the Java program by syntactical and semantic analysis. We are based on the singleton analysis and different proposed features in ou previous work (Nacef et al., 2022) to create structured data. This data contains a combination of feature values defining each singleton variant to train a supervised machine learning (ML) algorithm. The goal is not limited to detecting the singleton pattern but also the specification of the implemented variant as so as the incoherent structure that inhib the pattern intent. We use different ML algorithms to create the Singleton Detector (SD) and compare their performance. The empirical results demonstrate that our method based on features and supervised ML, can identify any singleton implementation with the specific variant's name achieving 99% of precision, and recall. We have compared the proposed approach to similar studies namely DPDf and GEML. The results show that the SD outperforms the state-of-the-art approaches by more than 20% on evaluated data constructed from different repositories;PMART, DPB and DPDf corpus in terms of precision.

关键词： Design Patterns Singleton Variant RNN-LSTM Classifier machine learning SD Classifier

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Intelligent Network Load Balancing System with Geographic Routing and SDN Multi-Controller Architecture 2

Intelligent Network Load Balancing System with Geographic Ro...

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2nd international conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Badotra, Sumit Sundas, Amit Ganguli, Isha Verma, Amit Singh, Gurpreet School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India University Centre for Research and Development Department of Computer Science and engineering Chandigarh University Gharuan Mohali India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305418

software-defined networking (SDN) is a network architecture that allows for greater control and programmability of network traffic. SDN load balancing can be implemented using various techniques, such as round-robin, IP-hash, and least connections. SDN load balancing can be done using a single controller or multiple controllers. "Intelligent Network Load Balancing System with Geographic Routing and Multi-Controller Architecture" is a comprehensive solution that addresses network performance optimization and downtime reduction. The system's components work collaboratively to distribute network traffic intelligently, minimize latency, and enhance user experience. The incorporation of machine learning techniques ensures continuous improvement and adaptability to evolving network conditions. Overall, this system provides a promising approach to improving network performance and user satisfaction in modern network environments. © 2023 IEEE.

关键词： control plane data plane loadbalancing machine learning multicontroller sdn

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2nd international conference on Science, engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd international conference on Science, engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722837

The proceedings contain 46 papers. The special focus in this conference is on Science, engineering Management and Information Technology. The topics include: Verification and Validation of Knowledge engineering Systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing machine learning Methods for Accurate Detection;monkeypox Detection with K-mer Using machine learning Algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of machine learning Models for Improved Diabetes Prediction;image Processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the Visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest software;a Diagnosis Model Based on Federated learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces Images Using Transfer learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of Signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time Based on the Digital Twin;role of Top Management Commitment and Information Technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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software Complexity Prediction Model: A Combined machine learning Approach 5th

Software Complexity Prediction Model: A Combined Machine Lea...

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5th international conference on Recent Innovations in Computing , ICRIC 2022

作者： Birihanu, Ermiyas Adamu, Birtukan Kefie, Hailemichael Beshah, Tibebe Software Engineering Wolkite University Wolkite Ethiopia Applied Informatics Silesian University of Technology Gliwice Poland School of Information System Addis Ababa University Addis Ababa Ethiopia

ISBN: (纸本)9789819906000

The need for computers increased quickly. As a result, the program is utilized in a significant and intricate manner. More complex systems are being developed by software businesses. Additionally, customers expect great quality, but the market requires them to finish their assignment faster. Different measuring methods are employed by software firms. Some of these include customer feedback after it has been given to customers, software testing, and stakeholder input. The objective of this project is to use a combination of machine learning techniques to predict software bug states using the NASA MDP dataset. The research process considered data preprocessing methods and applied singular and combination machine learning algorithms. To create the model, the single classifiers were combined using the voting method. Accuracy, precision, and recall were used to evaluate the model's effectiveness, along with tenfold cross-validation. The promising result was recorded by a combination of J48 and SMO classifiers. Before attempting to test the software product, the researcher retrieved attribute data from the source code;the complexity of the software product will then be ascertained using the constructed model. The main contribution of this study is to improve software quality by incorporating a machine learning framework into the present software development life cycle between implementation and testing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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SecDAN: Prevention of Network Breaches in Defense Area Network Using machine learning Techniques

SecDAN: Prevention of Network Breaches in Defense Area Netwo...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Kumar, Atul Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350305708

This abstract summarizes a comprehensive study aimed at improving the safety of Defense Area Networks through the use of advanced machine learning techniques. The suggested model takes a new approach by combining deep learning architectures and anomaly detection methods to detect both established and previously unexpected network intrusions. The study draws on a variety of datasets, including network traffic and security records, and employs thorough data preparation and feature engineering procedures. The accuracy of three important algorithms is rigorously compared Random Forest, and Logistic Regression K-nearest Neighbors. The findings of this study highlight the clear edge of K-nearest Neighbors in terms of overall better performance and technique and analysis. This paper contributes to network security by proposing a comprehensive model tailored to the complexities of Defense Area Networks, hence increasing the resistance against network breaches. In the modern interconnected digital environment, the susceptibility to network breaches is particularly pronounced. These breaches, often referred to as cyberattacks or data breaches, undermine the security of information confidentiality, integrity, and accessibility by unauthorized entry into systems, networks, or private data repositories. Cybercriminals deploy a diverse array of sophisticated techniques, encompassing malicious software, phishing, ransomware, and social manipulation, to infiltrate corporate networks and abscond with sensitive data. The expanding realm of technological advancement correlates with the escalating risk of network breaches, underscoring the imperative for robust cybersecurity strategies, continuous monitoring, and swift incident response. © 2023 IEEE.

关键词： Cyber Attack Decision Tree Logistic Regression machine learning Model Malicious Network Breach Detection Random Forest Security Techniques

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