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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Chitranjanjit Kaur Anurag Sharma dept. School of Engineering Design and Automation (Computer Science and Engineering) GNA University Phagwara India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Industrial maintenance is crucial for companies due to its significant impact on operational costs and efficiency. Many industrial firms find that a substantial portion of their expenses stems from equipment breakdowns, failures, or suboptimal performance over time. These costs can sometimes account for up to 50% of overall expenditures. Additionally, companies face various losses, including downtime from breakdowns, challenges in inventory and purchase management, and potential risks of injuries. The objective of this writing is to investigate how Predive maintenance is a footstep towards industry 5.0 where automation of predictive maintenance is evolved to autonomy in maintenance. In addition to it various machine learning algorithms are suggested to identify between healthy and poor data of a machine which is first step in Predictive maintenance then Deep learning algorithms to construct XAI (Explainable artificial Intelligence). Predictive maintenance, production scheduling, problem detection, predictive quality, and increased energy efficiency are highlighted in the typical use cases of the selected AI applications. Data from the real environment is transmitted to be virtually recreated. Efficient industrial maintenance helps mitigate these costs by reducing downtime, improving equipment reliability, and lowering the need for emergency repairs. By implementing a well-designed and optimized maintenance strategy, production plants can ensure their equipment operates as reliably as possible, minimizing disruptions and enhancing overall operational efficiency. Machine Learning (ML) methods have been appeared as a crucial tool in Predictive Maintenance (PdM) applications to prevent failures in equipment that make up the production lines. However, the performance of PdM applications depends on the appropriate choice of the ML method. To save expenses, find inefficiencies, duplicate tool tracking systems, and do other tasks, Digital Twin evaluates material utilization

关键词： Adaptation models Costs Machine learning algorithms Electric breakdown Stacking Companies Writing Vectors Maintenance Predictive maintenance

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Unveiling the Enigma: Sudden Cardiac Arrest Among Youth and Uncovering Underlying Causes: A Data Analysis Study 5th

Unveiling the Enigma: Sudden Cardiac Arrest Among Youth and ...

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5th International Conference on Artificial Intelligence and Speech Technology, AIST 2023

作者： Rao, Mohit M. Shrivastava, Virendra Kumar Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore562106 India

ISBN: (纸本)9783031751660

This study focuses on analyzing electrocardiogram (ECG) records related to sudden cardiac death in youth and analyze the factors influencing a sudden cardiac arrest. Sudden cardiac arrest poses a severe threat to life and can result in death if not addressed promptly. Recent American research indicates a 13% increase in sudden cardiac arrest cases among individuals aged between 35–45. Furthermore, the Indian Heart Association has reported that heart disease tends to affect Indians at a younger age compared to other demographic groups, often without prior warning. Studies reveal that Indians tend to experience heart diseases approximately a decade earlier than their Western counterparts. This work aims to conduct data collection and surveys to collect ECG records and other relevant information that is crucial for training the machine learning models to achieve the study outcomes. The study's scope encompasses evaluating the feasibility of the task, identifying relevant datasets, conducting data preprocessing, and assessing the performance using K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and random forest algorithm. Among these, both SVM and Random Forest demonstrated superior performance, with the Random Forest algorithm further fine-tuned to align with the dataset's specific requirements. The refined model exhibited 93% accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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Security Implementation on IoT using CoAP and Elliptical Curve Cryptography

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Procedia computer science 2023年 230卷 493-502页

作者： Rathnakar Achary Chetan J Shelke Kavin Marx Aishwarya Rajesh Dept. Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India

IoT devices typically encompass objects or devices equipped with software and internet connectivity, allowing them to collect data. They have limited processing power, memory, and storage. These limitations can make it difficult to secure these devices, as traditional security measures may not be practical. Implementing security for IoT devices can be challenging due to these characteristics, but several steps can be taken to mitigate these challenges. Elliptic Curve Cryptography (ECC) can secure resource constrained IoT devices by providing a more efficient method of encryption and authentication than traditional methods such as RSA. In this paper, we analyzed the lightweight cryptographic algorithm ECC for securing resource-constrained devices such as IoT and evaluated its performance compared to the RSA cryptosystem. The analysis result indicates that ECC is considered to provide stronger security than RSA for the same key length. The CoAP protocol is applied to devices with limited resources, and an examination of its impact is conducted, considering three key performance metrics: CPU usage, bandwidth efficiency, and message communication latency.

关键词： IoT OSCORE CoAP Elliptical Curve Cryptography RSA Resource-constrained devices

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Making BRAMs Compute: Creating Scalable Computational Memory Fabric Overlays

Making BRAMs Compute: Creating Scalable Computational Memory...

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： MD Arafat Kabir Joshua Hollis Atiyehsadat Panahi Jason Bakos Miaoqing Huang David Andrews Department of Computer Science and Computer Engineering University of Arkansas Department of Computer Science and Computer Engineering Cadence Design Systems Department of Computer Science and Computer Engineering University of South Carolina

The increasing density of distributed BRAMs diffused throughout modern Field Programmable Gate Arrays (FP-GAs) is ideal for forming processor in/near memory architectures. This breaks the traditional von Neumann memory bottleneck limiting concurrency and degrading energy efficiency. Ideally, processing density should scale linearly with BRAM capacity, and clock frequencies should be set by the read/write access times of the BRAM. In this paper, we present a PIM overlay that achieves these goals. We observe an improvement of performance by 2.25 x, logic resource utilization by 2 x, and accumulation delay by 17 x compared to prior published work.

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AI-Powered Clinical Decision Support Systems (CDSS): Challenges, Benefits, Applications, and Future Directions 2

AI-Powered Clinical Decision Support Systems (CDSS): Challen...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Yesankar, Prajyot Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

AI-based Clinical Decision Support Systems are a revolutionary innovation in the healthcare sector, using artificial intelligence to improve diagnostic precision, optimize treatment planning, and enhance patient outcomes. This paper discusses the challenges, benefits, applications, and future directions of AI-driven CDSS. Some of the key challenges are data quality, integration with EHRs, ethical concerns, and explainability issues. Despite these challenges, benefits such as efficiency improvements, reduced clinician workload, and the possibility of tailored medicine are substantial. In addition, the paper depicts diverse applications across oncology, cardiology, and primary care. The paper further stresses the directions: validation of robustness, user-friendly interfaces, and equitable deployment to achieve maximal utilization of AI-CDSS in global health care systems. Besides this, the integration of multimodal data and improvement in predictive analytics will help to revolutionize preventive care. Moreover, ethical practices with AI will bring forth trust and adoption. Such an all-inclusive review signifies the pivotal role AI will play in shaping clinical decision-making for the future. © 2025 IEEE.

关键词： Personalized medicine

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Landscape Patterns in Mining Cities Influenced by Extraction and Terrain Features

Landscape Patterns in Mining Cities Influenced by Extraction...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Madhavan, K. Babu, J.Jai Jaganath Karthikeyan, P. Centre for System Design Chennai Institute of Technology Department of Computer Science & Engineering Tamil Nadu Chennai600 069 India Centre for System Design Chennai Institute of Technology Department of Biomedical Engineering Tamil Nadu Chennai600 069 India

ISBN: (纸本)9798350348910

The combined influence of mining activities and the underlying terrain gradient has caused significant transformations in the landscape patterns of mining cities. The natural topography being altered by mining operations can result in a distinct evolution in the spatial arrangement and ecological dynamics of these cities. Spatial heterogeneities are influenced by factors such as extraction techniques, waste disposal methods, and infrastructure development. The inherent terrain gradient heavily influences the distribution of mining sites and urban expansion patterns. This research underscores the intricate interplay between human-induced activities and natural topographical features in shaping the landscape of mining cities. The development of resilient communities in areas that are evolving due to mining requires a clear understanding of these dynamics for sustainable urban planning, environmental conservation, and community development. © 2023 IEEE.

关键词： Waste disposal

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Semantic Analysis Using Deep Learning for Predicting Stock Trends

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Procedia computer science 2024年 235卷 820-829页

作者： Manisha Galphade V.B. Nikam Dhanalekshmi Yedurkar Prabhishek Singh Thompson Stephan Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set.

关键词： Deep learning emotion mining financial analysts investors sentiment analysis

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Effective Blockchain-Based Management of Agri-Food Supply Chains Using Deep Reinforcement Learning

Effective Blockchain-Based Management of Agri-Food Supply Ch...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： G Shivaji Rao Ilakkiya S Lakshmi Priya G Sneha S Vijay Raj S Department of Computer Science and Design Karpagam College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Blockchain based agri food supply chains augmented with Deep Reinforcement Learning (DRL) optimize important problems like perishability, seasonality as well as demand variability. The traditional supply chain models, which are mostly static, fail to respond to the dynamic supply chain conditions and hence there is a strong need for the introduction of new ideas. In this work, blockchain is integrated for secure and transparent data management, and data for automation, like inventory management, route planning and price adjustment, is optimized in real time using DRL. The blockchain is a phenomenon of decentralization that improves trust and traceability, and the flexibility of DRL guarantees resource utilization in a way that can adapt to unexpected settings. The proposed system is piloted and a case studied to show its capability of enhancing supply chain resilience, recycle wasted products and scale efficiently. It specifies the transformative outlook of the mixture of blockchain and AI technologies in agricultural and Supply chain administration.

关键词： Supply chain management Supply chains Transforms Inventory management Deep reinforcement learning Blockchains Resource management Artificial intelligence Optimization Resilience

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Proposal for the Support Method Presentation Model Utilizing Nursing Home Insights 9

Proposal for the Support Method Presentation Model Utilizing...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Nakajima, Yoko Yuki, Masamune Ptaszynski, Michal Honma, Hirotoshi Akiba, Tomoyoshi Masui, Fumito Institute of Technology Kushiro Colloege Dept. of Creative Engineering Kushiro Japan School of Regional Innovation and Social Design Engineering of Kitami Institute of Technology Kitami Japan Toyohashi University of Technology Dept. of Computer Science and Technology and Engineering Toyohashi Japan

ISBN: (纸本)9798350383010

One of the significant problems faced by dementia group homes is the high caregiver turnover rate. In particular, there is an urgent need to prevent the turnover of new caregivers and work toward ensuring and improving the quality of care. To address this issue, we are developing a care method support system that utilizes two databases: a personal attributes database and a care case database ([1]-[3]). The personal attributes database extracts personal attributes such as hobbies and previous occupations of residents from care records and care conference minutes. The care case database extracts cases of resident problems, their solutions, and the original text containing them. The care method support system presents candidates for appropriate responses to problems faced by residents based on these databases. In this study, we generated two care suggestion models: Care Suggestion Model: This model applies text similarity to the care case database to suggest candidates for appropriate responses to unregistered problems. Personalized Care Suggestion Model: This model applies clustering to the personal attributes database and generates a care suggestion model that considers personal attributes. We compared the two models and tested the usefulness of the model. © 2024 IEEE.

关键词： Nursing

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Image processing with deep-learning and transfer learning for cutting tool degradation monitoring 12

Image processing with deep-learning and transfer learning fo...

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12th CIRP Global Web Conference, CIRPe 2024

作者： Colantonio, Lorenzo Equeter, Lucas Giovannelli, Hugo Dehombreux, Pierre Mahmoudi, Saïd Ducobu, François Machine Design and Production Engineering Lab University of Mom Place du pare 20 Mons7000 Belgium Computer Science Unit University of Mons Place du parc 20 Mons7000 Belgium

Monitoring the degradation of cutting tools is of utmost importance in the manufacturing world. Tools with substantial wear fail to produce high-quality parts in terms of geometry, residual stress, and surface finish. Furthermore, replacing tools in a non-optimal manner can lead to increased production costs and downtime. Therefore, monitoring the condition of the tool is essential to avoid these additional costs and ensure good production quality. This article explores various classification models, specifically VGG19, EfficientNetV2, and Vision Transformers. These models classify the state of tools using their images. Using transfer learning, a comparison of the best-performing artificial intelligence-based image analysis models is conducted to identify those most suitable for monitoring cutting tools. A comparative analysis of their generalizability, performance and explainability is realized. The model with the best performance is VGG19 with an accuracy of 94%, followed by EfficientNetV2 and ViT with an accuracy of 87%. A full comparison of these results is carried out. © 2025 The Author(s).

关键词： Contrastive Learning

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