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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

5th International Conference on Innovative Intelligent Indus...

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

ISBN: (纸本)9783031807596

the proceedings contain 61 papers. the special focus in this conference is on Innovative Intelligent Industrial Production and Logistics. the topics include: An Out-of-Sample Clustering Ensemble Method for Defect Detection and Classification in Metal Additive Manufacturing;Advanced Process Monitoring and OEE Metrics: Leveraging AASs for Efficiency;obsolescence Forecasting of Intel Processors: A Transformers Approach;machine learning Tool for Yield Maximization in Cream Cheese Production;comparative Evaluation of Irregular Shape Strip-Packing Algorithms;an Inventory Management Support Tool through Indirect Q-Value Estimation: A Combined Optimization and Forecasting Approach;multiscale Clustering to Improve Anomaly Detection in Nuclear Equipments;clustering Analysis for Forecasting Medicine Consumption;a Decision Support System of the Configuration of a Supermarket in a Components Company for the Automotive Industry;a Distributed Framework for Cooperative Scheduling of Production, Transportation, and Maintenance Using Multi-Agent Systems;the Role of Actor’s Creative Self-efficacy in AI Enabled Value Creation;building Realistic Environment from computer Vision Approach Applied to Manufacturing Simulation in the Digital Twin Context;towards a Process-Based Industry 5.0 Maturity Model: A Feasibility Study in Supply Chain;a Multi-objective Genetic Algorithm Approach for Multi-component Products Recovery and Remanufacturing Planning;reinforcement learning for Optimizing Routing in the Production Supply of Matrix Production Systems;an Innovative Fault Detection Robotic Tool for Overhead Cranes in Industries: Magnetic Wheel Modelling and Experimental Validation;current Trends and Future Challenges to Put Circular Manufacturing in Practice;enhancing human – Robot Collaboration in the Industry 5.0 Framework with Physics-Informed Neural Networks: Application to Collision Detection;AI-Driven Smart Air Conditioning System for a Sustainable and Energy-Efficient Industrial Future.

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

5th International Conference on Innovative Intelligent Indus...

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

ISBN: (纸本)9783031807749

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machine learning for Diversity and Inclusion: Addressing Biases in HR Practices

Machine Learning for Diversity and Inclusion: Addressing Bia...

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Recent Trends in computer Science and Technology (ICRTCST), international conference on

作者： D. Anitha Kumari Irfan Abdul Karim Shaikh S. Gangadharan P B Narendra Kiran S. R. Ramya Amarja Satish Nargunde Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai TamilNadu Faculty of Business Bahrain Polytechnic Bahrain Department of Management Studies St. Joseph's College of Engineering Chennai Mittal School of Business Lovely Professional University Jalandhar-Delhi Phagwara Punjab India School of Management Studies Karpagam College of Engineering Coimbatore Tamilnadu Management Studies Bharati Vidyapeeth Institute of Management and Rural Development Administration Sangli Bharati Vidyapeeth Bhavan Sangli Maharashtra

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

As organizations strive to foster diverse and inclusive workplaces, the role of technology, particularly machine learning, becomes crucial in addressing biases inherent in human Resources (HR) practices. this research explores the application of machine learning algorithms to mitigate biases in recruitment, performance evaluation, and talent management processes, ultimately promoting diversity and inclusion. Biases, whether conscious or unconscious, can affect decision-making at various stages, leading to underrepresentation and disparities within the workforce. Recognizing the need for innovative solutions, this paper advocates for the integration of machine learning techniques to augment HR processes. By leveraging advanced algorithms, organizations can develop unbiased job descriptions, eliminate resume screening biases, and create diverse candidate shortlists. machine learning models can provide objective insights by analyzing performance metrics, removing subjective biases, and fostering a more equitable assessment of employees. Talent management, the focus of the fourth section, is critical for the retention and development of a diverse workforce. machine learning algorithms can assist in identifying high-potential employees based on merit, skills, and contributions, thus promoting a fair and inclusive talent pipeline. Addressing concerns related to algorithmic biases, the research suggests guidelines for responsible ML implementation to ensure that technology aligns with organizational values and legal requirements. In conclusion, this research underscores the transformative potential of machine learning in creating more equitable and inclusive workplaces. By addressing biases in HR practices, organizations can harness the power of technology to build diverse teams, foster innovation, and promote a culture of inclusion. However, it also emphasizes the need for ongoing research, collaboration, and ethical considerations to maximize the positive impact of machi

关键词： Training Technological innovation machine learning algorithms Employment Resumes Pipelines machine learning

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Crop Disease and Pest Detection using Convolutional Neural Networks (CNN)

Crop Disease and Pest Detection using Convolutional Neural N...

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： S Kalaimanivel K France Department of Computer Applications 22295014 Hindustan institute of technology and science Chennai India Department of Computer Applications Hindustan institute of technology and science Chennai India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption, fiber production, and other reasons. It is one of the oldest and most important human activities, laying the groundwork for food production and billions of people’s lives throughout the world. As technology advances, additional capabilities for crop protection and disease prevention become accessible. Artificial Intelligence (AI) and machine learning (ML) algorithms capture features such as crop and soil monitoring, crop maturity detection, autonomous weeding, intelligent crop spraying, pest and disease detection, and more. this study suggests a novel technique for automated crop disease identification by utilizing Convolutional Neural Networks (CNNs) from the field of computer vision. By performing a thorough testing and validation on separate test sets, the proposed methodology outperforms other existing methods in terms of accuracy. the Plant Village collection, maintained by the Centers for Disease Control and Prevention (CDC), includes damaged plant leaf photos and labels. the proposed method has achieved an accuracy of about 99.6%.

关键词： Training Accuracy Prevention and mitigation Crops Production Soil Loss measurement

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Reimagine Sentiments in Nature Language through Crystalize- Water Visualization

Reimagine Sentiments in Nature Language through Crystalize- ...

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international conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Wen-Hao Png Fang-Yu Chang Jie-Jin Lo Su-Chu Hsu Faculty of Information and Computer Technology Universiti Tunku Abdul Rahman Kampar Malaysia Institute of Information Systems and Applications National Tsing Hua University Hsinchu R.O.C. Taiwan Institute of Information Security National Tsing Hua University Hsinchu R.O.C. Taiwan Research Center for Technology and Art National Tsing Hua University Hsinchu R.O.C. Taiwan

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Text art translates words into artworks through various mediums, including neon signage, light art, and hand- painted or drawn text. the visual representations focus on the typographical design and the ideas and meaning conveyed through human language. While sentiment analysis is a pivotal area of study in natural language processing (NLP) that aims to determine the sentiment expressed in text data with wide range of applications span various domains, including marketing, social media monitoring, and opinion mining. In this work, we reimagine the sentiments in nature language for artworks through a creative tandem composition of machine learning, physics, and art. By reframing the textual sentiment as virtual snow crystal fabrication, we created charming yet representative visualizations based on wide variety of cyber and literature texts, including classical poems and social network comments. the visualization aims to creatively generate text-based art and raise awareness of how sentiment in natural language can affect human emotion.

关键词： Fabrication Sentiment analysis Visualization Art Social networking (online) Snow machine learning Crystals Turning Physics

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Integrating Social Platforms with IoT: Security Challenges, Applications, and Trust Issues

Integrating Social Platforms with IoT: Security Challenges, ...

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India Council international Subsections conference (INDISCON), IEEE

作者： Bhabendu Kumar Mohanta Mohan Kumar Dehury Desetti Tulasi Sunayana Das College of Information Technology United Arab Emirates University Al Ain United Arab Emirates Amity Institute of Information Technology Amity University Ranchi Jharkhand India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Dept. of Computer Scinece and Engineering DRIEMS University Tangi Cuttack Odisha India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

the term Social Internet of things (Social IoT) refers to an expansion of the term Internet of things (IoT) that integrates social networking capabilities and concepts into IoT applications. the number of connected devices has already crossed the total world population. With the development of other technologies like Blockchain, Artificial Intelligence, machine learning, and Data Science, IoT become more usable in many applications. With the rapid use of digital devices, social media like Facebook and others are part of the human lifestyle. the integration of IoT with a social platform for different services become an emerging research field in recent times. In this paper, the authors investigated the impact of social services in IoT and its corresponding decentralized architecture. the paper also identified the security challenges associated with the Social IoT and trust issues. Finally, the paper addresses some of the emerging technologies which are capable of addressing the security challenges in Social IoT.

关键词： Privacy Social networking (online) machine learning Real-time systems Social Internet of things Internet of things Security Stakeholders Trust management Predictive analytics

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A Culture- centered Designing of Web-based Language Acquisition Module for the Hearing Impaired

A Culture- centered Designing of Web-based Language Acquisit...

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India Council international Subsections conference (INDISCON), IEEE

作者： Varghese Kandathil Anie Mathew JG International School Ahmedabad India Digital Arts Academy for the Deaf (DAAD) Thiruvananthapuram India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

the hearing impaired (HI) is a marginalized linguistic minority whose language acquisition can be expedited using Sign Language Automating Assistive Technologies (SLAAT). Although primarily designed for the everyday communication SLAAT is gradually being used for teaching HI vernacular languages which encountered several bottlenecks. From a user perspective, we analyze these constraints drawing on the human computer interactions (HCI) approaches to design and use of SLAAT. Our analysis reveals the underlying user-centered design approach as the main source of these bottlenecks with exclusionary implication for the HI. Hence, for a better and more inclusive user experience, we propose a culture-centered approach to designing SLAAT based on HCI research. We designed, tested and put in use a culture-centered web-based learning module that helps the HI to acquire a vernacular language of Kerala state, India. Based on our experience, we briefly discuss how our culture-centered design and its implementation incorporate the user beneficial aspects of the currently prevalent user-centered design approach while overcoming its bottlenecks.

关键词： human computer interaction Sign language Costs User centered design Auditory system Educational technology Linguistics Writing User experience Wearable devices

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UAV-Assisted Fog Computing Offloading Strategy Based on Deep Reinforcement learning

UAV-Assisted Fog Computing Offloading Strategy Based on Deep...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Mingshuo Liu Wenle Bai School of Information Science and Technology North China University of Technology Beijing China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

With the arrival of the Internet of things era, unmanned aerial vehicle (UAV) communication become more popular due to their high maneuverability and low operating costs. In this paper, a UAV equipped a fog computing server used to provide computing offloading services for user equipments (UEs). Under the constraint of certain energy consumption, we jointly optimize user scheduling, computing resource allocation, UAV flight direction and speed to achieve the optimization goal of minimizing the maximum processing delay. the deep deterministic policy gradient (DDPG) offloading algorithm improved by a priority experience replay (PER) pool can increase the probability of high-value sampling and solve the problem of sparse rewards. We introduce the DDPG-PER offloading algorithm into this scene to achieve minimize the maximum processing delay and design experiments to compare with other algorithms. Experiments show that the DDPG-PER offloading algorithm can quickly achieve the optimization goals of convergence. the proposed DDPG-PER algorithm can faster achieve convergence always has lower delay compared with other benchmark algorithms in different task size. Meanwhile, in different computing capability of UAV, it has a better performance on offloading ratio and decreasing delay compared with the other benchmark algorithm.

关键词： Energy consumption Processor scheduling Simulation Benchmark testing Autonomous aerial vehicles Delays Resource management Optimization Edge computing Convergence

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machine Vision Intelligence Using Layer-Wise Relevance Backward Propagation For Breast Cancer Diagnosis

Machine Vision Intelligence Using Layer-Wise Relevance Backw...

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： Ukech Melissa Manuela Rose Nakasi Daudi Jjingo Nakayiza Hellen Martin Ngobye Ggaliwango Marvin Department of Computer Science Makerere University Kampala Uganda Department of Information Techonology Makerere University Kampala Uganda Department of Computer and Information Science Muni University Arua Uganda

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

1 machine vision intelligence with layer-wise relevance propagation (LRP) for breast cancer diagnosis is an innovative approach aimed at improving diagnostic accuracy and efficiency. Traditional methods for diagnosing breast cancer often rely on the manual interpretation of medical images, which can be time-consuming and prone to human error. this study proposes the development of a machine vision model utilising LRP designed to analyse mammographic images for the diagnosis of breast cancer. Various machine learning models, including Convolutional Neural Networks (CNN), VGG19, VGG16, ResNet50, EfficientNet, and Vision Transformer, were trained on a breast cancer ultrasound dataset of mammograms. Features such as texture, density, and shape were extracted from each image for analysis. the different models achieved high accuracies of $\mathbf{9 9 \%}$ $\mathbf{9 1 \%}, \mathbf{8 0 \%}, \mathbf{9 9 \%}, \mathbf{9 5 \%}$, and $\mathbf{8 9 \%}$, respectively. LRP is then used to explain the model’s classification by highlighting image regions most relevant to its decision. this approach aims to attain high diagnosis accuracy while also offering interpretable insights into the model’s logic, potentially increasing its reliability in clinica contexts. By automating the interpretation of mammographic images, this has the potential to improve diagnosis accuracy and patient outcomes. 1 Makerere University Research and Innovations Fund, under Digital Domiciliary Platform to Standardize Data Capture in Community-based Midwifery Practices and Quality of Care (DC4Midwifery)

关键词： Backpropagation Accuracy Ultrasonic imaging Biological system modeling machine vision machine learning Predictive models

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Deep learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks

Deep Learning-Based Age and Gender Prediction through Featur...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Manasa S M Raghu Ramamoorthy Anitha Velu Angel Donny F Chaitrashree S Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bengaluru India Department of Computer Science and Engineering The Oxford College of Engineering Bengaluru India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. this paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). the employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. the research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. the model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques.

关键词： Training Accuracy Target recognition Face recognition Supervised learning Estimation Transportation Benchmark testing Feature extraction Convolutional neural networks

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