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检索条件"任意字段=2nd International Conference on Software Engineering and Machine Learning, CONF-SEML 2024"

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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)9783031722868

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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Empowering Precision machine: Deep learning Technique for Lung Cancer Diagnosis

Empowering Precision Machine: Deep Learning Technique for Lu...

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Computing and Data Analytics (ICCDA), international conference on

作者： Danish Jamil Syed Mehr Ali Shah Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed AlJarwani Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Sultanat of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics.

关键词： Deep learning Technological innovation Computed tomography Computational modeling Precision medicine Lungs Lung cancer Medical services Data models Medical diagnostic imaging

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The Application of Time Series Analysis for Scalable Systems

The Application of Time Series Analysis for Scalable Systems

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Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Amandeep Gill Supriya Rai Aarsi Kumari Department of R&D Vivekananda Global University Jaipur India Department of Management School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

this technical abstract gives a top-level view of the utilization of time collection analysis for scalable structures. Time collection evaluation is a crucial approach in the fields of facts era and engineering. It could be used to discover styles in large datasets over certain periods or to discover anomalous conduct. This evaluation may be used to come across possibilities for overall performance development, pick out correlations among gadget additives, and make informed predictions about destiny developments. It is particularly and properly ideal for scaling structures because it allows for the enormously fast comparison of ability and performance metrics so as to identify the regions that require further attention quickly. Moreover, time collection analysis may be used to identify impactful trends related to machine scalability and help guide selections approximately device layout.

关键词： Performance evaluation Scalability System performance Time series analysis Market research software Resource management

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Using machine learning and AI to Find Homes for the Voiceless

Using Machine Learning and AI to Find Homes for the Voiceles...

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Disruptive Technologies (ICDT), international conference on

作者： Neha Jha Anushka Chaurasia Pradyuman Chauhan Vansh Rajput Department of Applied Computational Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida Department of Applied computational sciences and engineering G.L Bajaj institute of technology and management.

Adoption of stray animals from animal shelters has grown during the pandemic. Despite the fact that many people still choose to purchase pets from pet stores. Through this app, we encourage them to adopt rather than purchase. In India, this is typically done by purchasing dogs, cats, and many other animals from pet stores or from individuals whose animals have already given birth. The majority of adoption procedures nowadays are laborious and time- consuming. The project's primary goal is to create a platform that will streamline those procedures and give abandoned animals a fresh lease on *** the epidemic, everyone has learned the value of cellphones, and their use has expanded significantly, therefore we decided to make this programme a mobile app. As a result, it facilitates quickand effective transportation between the user and the animal shelter. Flutter technology is being used to develop the project. Firebase is used on the back end whereas flutter is used for the front end. This app's goal is to make it easier for stray animals to be adopted. To assist in automating the process of servingthe welfare of pets, the application offers a user-friendly interface. Because Flutter is Google's free, open-source software development kit (SDK) for developing cross-platform mobile applications, we are using it to create the app and Firebase as the database. It creates scalable, high-performance applications with appealing user interfaces.

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Design and Development of a Smart Antenna System for Electromagnetic Compatibility

Design and Development of a Smart Antenna System for Electro...

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Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Manish Kumar Goyal H Malathi Binod Kumar Choudhary Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Electrical & Electronics Engineering ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

This technical abstract introduces the design and development of a smart Antenna gadget for Electromagnetic Compatibility (EMC). The clever antenna device pursuits to lessen unwanted interference in wireless verbal exchange systems, and also to locate time Electromagnetic interference (EMI) alerts and optimize running conditions. This antenna gadget is built on both software and hardware systems. Its design relies on the improvement of antenna arrays, which make use of adaptive sign-processing techniques with the intention of reaping the desired EMC necessities. The antenna array, with its signal-processing architecture, gives the possibility of at the same time optimizing radiation samples and electrical parameters by way of electronically scanning the antenna in more than one direction and taking into consideration disturbances in the environment. The layout additionally incorporates an optimized weight matrix that is used to optimize the antenna’s signal-processing structure. Subsequently, the advanced system is simulated and evaluated for its overall performance, and the results are compared with theoretical predictions.

关键词： Wireless communication Performance evaluation Layout Electromagnetic interference Adaptive arrays Medical services machine learning

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FPGA Suitability for Implementation of Pedestrian Detection algorithms for Autonomous Driving Application

FPGA Suitability for Implementation of Pedestrian Detection ...

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Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Isha Gupta Deepti Prit Kaur Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

An emerging field of study called autonomous driving seeks to create self-driving cars that can guarantee the safety of both passengers and other road users. Although several countries have already developed basic autonomous vehicles, more research is required for more sophisticated automation. Numerous different computer vision (CV) algorithms are needed to handle the extensive elements in an autonomous vehicle. Although many computer vision algorithms have already been built as software, they still require a stable hardware platform in order to be tested and used. Due to the many advantages it offers, Field Programmable Gate Array (FPGA) has become the most popular hardware platform. This paper addresses the significance and necessity of autonomous vehicles, highlighting one of their most difficult features— pedestrian detection. The paper also reviews the numerous advantages of FPGAs specifically for the development of autonomous driving challenging features like pedestrian detection and it also documents that how different researchers have used FPGA’s as their hardware platform to construct self-driving car’s features with an eye towards potential future improvements.

关键词： Computer vision Automation Pedestrians software algorithms Feature extraction Hardware Detection algorithms

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Blood Glucose Diabetic Prediction using machine learning Algorithm

Blood Glucose Diabetic Prediction using Machine Learning Alg...

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Sustainable Computing and Smart Systems (ICSCSS), international conference on

作者： A. Devi P. Sasireka K. Kovardhani G. Premalatha T. Sivasakthi R. Subash Department of ECE IFET College of Engineering Villupuram Department of ECE SA Engineering College Chennai

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

One characteristic of type 1 diabetes mellitus (T1DM), a chronic autoimmune disease, is the body's incapacity to produce insulin. Blood glucose levels must be constantly monitored and controlled by people with kind 1 diabetes with the goal to prevent complications. machine learning (ML) techniques offer a chance to help with personalized diabetes treatment by predicting blood glucose levels. The project's purpose is to build a Web app that employs machine learning methods to forecast blood glucose levels in persons with type 1 diabetics. This research study aims to collect real-time data from T1DM patients on relevant parameters such as exercise, insulin dosage, and consumption of carbohydrates. The information can be utilized for teaching machine learning methods, including cooperative gets closer, neural systems, and model-based regression, to estimate upcoming blood glucose levels. Individuals with type 1 diabetes can use the Flask programs straightforward interface to enter their routines, insulin dosages, and meal information. Subsequently, the software will employ the acquired machine learning methods to generate customized predictions for blood sugar levels. It will additionally involve studies on the factors influencing blood sugar fluctuations and recommendations for bettering diabetes treatment. The project is significant as it offers accurate and personalized blood sugar predictions, which might enhance the standard living for those with type 1 diabetes. By applying machine learning techniques and developing a simple implementation, the project aims to empower T1DM patients to take educated choices regarding their healthcare, thereby improving their general wellness and health.

关键词： machine learning algorithms Accuracy Vectors Diabetes Glucose Biosensors Biomedical monitoring

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

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

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Electrical, Automation and Computer engineering (ICEACE), international conference on

作者： Kang Zheng Hubei Engineering Institute Huangshi China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

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： machine learning algorithms Automation Service robots Loading Grasping Containers Robot sensing systems software Robots Assembly

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Towards Resilient IoT Security: An Analysis and Classification of Attacks in MQTT-based Networks

Towards Resilient IoT Security: An Analysis and Classificati...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Anshika Sharma Himanshi Babbar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

The term Internet of Things (IoT) refers to a system of actual physical objects, or things, that are integrated with software, firmware, sensors, and other technologies to communicate and share data with other internet-connected devices and systems. Since IoT security is even more comprehensive than IoT, many different approaches come under its purview. This paper has examined various attack scenarios against MQTT, a protocol utilized in IoT systems. The paper utilizes machine learning (ML) methods including Random Forest (RF), Adaptive Gradient Boosting (AdaBoost), Support Vector machines (SVM), and Extreme Gradient Boosting (XGBoost). machine learning has the potential to mitigate cyber threats and enhance security architecture through the identification of trends, the generation of real-time cybercrime maps, and the execution of thorough security testing. The meticulously selected data on IoT dangers served as a strong basis for evaluating and instructing the model. The assessment of the scalability and effectiveness of different algorithms has shown that proactive monitoring for IoT threats is achievable. XGBoost is the most accurate approach, with a 99% accuracy rate. The SVM, AdaBoost, and RF methods have detection rates of 85%, 89%, and 95% respectively.

关键词： Support vector machines Radio frequency Scalability Smart homes Boosting software Sensor systems

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A Novel Hybrid Model for Efficient Prediction of Diabetes Using machine learning

A Novel Hybrid Model for Efficient Prediction of Diabetes Us...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER international conference on

作者： Akshit Jain Shagun Jain Department of Production and Industrial Engineering Delhi Technological University Delhi India Department of Software Engineering Delhi Technological University Delhi India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Diabetes mellitus, a long-term metabolic disease marked by elevated blood glucose levels, has a substantial negative impact on public health worldwide. Accurate prediction is essential for effective diabetes treatment due to its increasing prevalence. machine learning (ML) addresses the issues of this chronic metabolic condition, which provides a promising answer by enabling individualized therapies. This paper presents a comprehensive study on diabetes prediction using ML techniques, focusing on a hybrid approach stacked ensemble models. The research employs the Indian Pima dataset from Kaggle. The primary objective is to assess various ML models with and without preprocessing techniques. Preprocessing involves data cleaning, normalization, and feature engineering to enhance predictive accuracy. Overall, the results underscore the importance of preprocessing in improving predictive performance and highlight the efficacy of hybrid ensemble techniques in diabetic prediction tasks. Moreover, the hybrid approach utilizes a stacked ensemble comprising Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP) as base models and Logistic Regression as a meta-model. The hybrid approach achieves superior performance compared to individual models with 95.1% accuracy. This methodology effectively combines the strengths of diverse algorithms, mitigating their weaknesses and yielding more robust predictions. It provides substantial benefits by integrating ML algorithms, statistical techniques, and expert knowledge.

关键词： Training Accuracy machine learning algorithms Data preprocessing Focusing Predictive models Prediction algorithms Data models Diabetes Diseases

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