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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Yadav, Gaurav Bakhshi, Deepa Shafiqueuddin, Syed Mehta, Namish Garg, K.K. Singh Rathore, Saurabh Pratap J.S. University Dept. of Computer Science Shikohabad India Bhabha University Dept. of Management Bhopal India Bhabha University Dept. of Mechanical Engineering Bhopal India Lingayas Vidyapeeth Dept. of Commerce Faridabad India ICAPSR Dept. of Management New Delhi India

ISBN: (纸本)9798350356816

In supply chain management, data-driven decision-making makes use of real-time data and sophisticated analytics to boost productivity, cut expenses, and boost overall performance. Businesses can obtain important insights into their supply chain operations by combining big data, machine learning, and predictive analytics. Better demand forecasting, inventory level optimization, and proactive detection of possible disruptions are made possible by this strategy. Utilizing data analytics tools makes it easier to analyze enormous volumes of data from different sources, including manufacturers, suppliers, and consumers, in order to present a thorough picture of the supply chain. Making well-informed decisions about distribution, production scheduling, and procurement is aided by these insights. Additionally, data-driven decision-making facilitates the adoption of just-in-time inventory procedures, which cut down on excess inventory and holding expenses. By offering early warning indicators, it also helps identify and reduce risks like supply chain bottlenecks or supplier failures. Furthermore, dynamic modifications to production and distribution schedules are made possible by real-time data, guaranteeing that the supply chain stays flexible and sensitive to shifts in the market. This strategy improves cooperation and communication between supply chain participants, which boosts efficiency and coordination. In the end, data-driven decision-making in supply chain management creates a more competitive and robust supply chain that can satisfy consumer demands while lowering expenses and hazards. Data in supply chain management will play an ever-more-important role in promoting operational excellence and attaining sustainable growth as businesses continue to embrace digital technologies and data-driven strategies. © 2024 IEEE.

关键词： Predictive analytics

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Importance of Machine Learning and Deep Learning Algorithms in Earthquake Prediction: A Review 11

Importance of Machine Learning and Deep Learning Algorithms ...

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11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Gursoy, Gunes Varol, Asaf Nasab, Ahad Maltepe University College of Engineering and Natural Sciences Dept. of Computer Engineering Istanbul Turkey The University of Chattanooga College of Engineering and Computer Science Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (纸本)9798350336986

Earthquakes are the leading natural disasters that have caused loss of life and property since the formation of the world. Machine learning and deep learning are frequently used in studies for earthquake prediction. This article consists of a compilation of studies using machine learning and deep learning algorithms. In the article, studies on topics such as earthquake magnitude estimation, signal discrimination, electron density estimations in the ionosphere, examination of radon gas anomalies using machine learning and deep learning algorithms are included. The studies in this paper show that Deep Learning algorithms are used more frequently in earthquake forecasting. It is expected that Deep Learning will provide more successful results in future studies due to its ability to work with larger data sets compared to Machine Learning and its ability to improve itself from errors. © 2023 IEEE.

关键词： Earthquakes

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Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 24-31页

作者： Zivelonghi, Alessandro Giuseppi, Alessandro Dep. of Applied Mathematical Science IIS "Calabrese-Levi" VR San Pietro in Cariano Italy PowerConsulting Studio San Pietro in Cariano Verona Italy Dept. of Computer Control and Management Engineering University of Rome "La Sapienza" Via Ariosto 25 Rome00185 Italy

Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO2 levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO2. The multi-room monitoring concept of indoor-CO2 levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes. © 2023 The Authors

关键词： Internet of things

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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh 14

A novel approach incorporating feature extraction followed b...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kundu, Debashis Rahaman, Md. Mushfiqur Sakib, Md. Nazmus Abdullah Al Sohan, Md. Faruk Fardaush Mithu, Jannatul Hossain, Afzal Aktar, Nishita American International University- Bangladesh Dept. Management Information Systems Dhaka Bangladesh Daffodil Institute of It Dept. of Computer Science and Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh American International University- Bangladesh Dept. of Computer Science Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Savar Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation's agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database. © 2023 IEEE.

关键词： Support vector machines

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Development of a Multimodal Machine Learning Model for Seizure Detection Using Wearable Devices 1

Development of a Multimodal Machine Learning Model for Seizu...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh Rathore, Saurabh Pratap Magotra, Sumedha Sruthy, K.V. Sharma, Sonu Kumar Kaur, Gaganpreet Singh, Sailendra ICAPSR Dept. of Management New Delhi India Chandigarh University Dept. of Computer Science Engineering Chandigarh India Dhanalakshmi Srinivasan College Dept. of Computer Science & Engineering Coimbatore India Mangalayatan University Dept. of Comp. Eng & Applications Aligarh India Lovely Professional University Punjab India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

This research explores the transformative potential of wearable devices designed to automatically detect & predict epileptic seizures, offering continuous monitoring & early detection capabilities. The study focuses on developing a machine learning model tailored for seizure detection in such devices, leveraging multimodal sensors such as Electrodermal Activity (EDA) & Accelerometer (ACC). By measuring skin resistance and identifying irregular heartbeats, which are signs of upcoming seizures, these sensors allow for precise seizure detection. The study intends to demonstrate the effectiveness of this machine learning model in wearable technology with the goal of improving patient outcomes and seizure management. © 2024 IEEE.

关键词： Accelerometers

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A Proposed Model for Machine Learning-Based Early Chronic Kidney Disease Detection 3

A Proposed Model for Machine Learning-Based Early Chronic Ki...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Bhandurge, Sonam Sambrekar, Kuldeep Allagi, Shridhar Rajpurohit, Vijay S. Sannakki, Sanjeev Angadi Institute of Technology and Management Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Gogte Institute of Technology Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Kle Institute of Technology Hubli Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering India

ISBN: (纸本)9798350354171

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient's death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector Machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of 100% by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness. © 2024 IEEE.

关键词： Support vector regression

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Using IoT Enabled Predictive Maintenance to Enhance Business Operations: Benefits and Challenges 7

Using IoT Enabled Predictive Maintenance to Enhance Business...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Ansari, Seema Ansari, Adeel Institute of Business Management Dept. of Electrical Engineering Karachi Pakistan Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798331534271

In today's changing world incorporating Internet of Things (IoT) technologies, into maintenance strategies is seen as a crucial innovation to improve business operations. This study delves into the use of driven maintenance systems and their notable impact on enhancing business efficiency cutting costs and ensuring operational dependability. By making use of real time data from sensors and devices these systems can predict equipment failures in advance reducing downtime and prolonging the lifespan of vital assets. The study provides a look at how maintenance strategies have evolved over time the technology behind powered predictive maintenance and how it revolutionizes business processes. The insights gathered from case studies and analysis of current executions reveals how business have transformed and achieved enhancement in operations. The paper also reflects challenges with their related solutions and suggested strategies to solve them. The findings show that implementing IoT for maintenance is not a technology hop but a necessity for companies struggling to stay in today's competitive market. In addition to this it investigates developments and possible innovations in this area emphasizing the evolvement and rising significance of IoT, in the upkeep of business industry. This research has explored into the competences of this technology highlighting its capability to reduce prices, improve efficiency, lift equipment reliability and upkeep making decision depending on data. © 2024 IEEE.

关键词： Predictive maintenance

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Robotic Arm Dataset (RoAD): A Dataset to Support the Design and Test of Machine Learning-Driven Anomaly Detection in a Production Line 49

Robotic Arm Dataset (RoAD): A Dataset to Support the Design ...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Mascolini, Alessio Gaiardelli, Sebastiano Ponzio, Francesco Dall'ora, Nicola MacIi, Enrico Vinco, Sara Di Cataldo, Santa Fummi, Franco Polytechnic of Turin Dept. of Control and Computer Engineering Italy University of Verona Dept. of Engineering for Innovation Medicine Italy Polytechnic of Turin Dept. of Regional and Urban Studies and Planning Italy

ISBN: (纸本)9798350331820

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community. © 2023 IEEE.

关键词： Process monitoring

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Meteorology Predicting Technology Based on Artificial Intelligence Algorithms

Meteorology Predicting Technology Based on Artificial Intell...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Abitova, Gulnara Mukashev, Daniyar Aitmukhanbetova, Elvira Rakhimzhanova, Mira Uskenbayeva, Gulzhan Kulniyazova, Korlan Binghamton University Dept Electrical Engineering and Computer Science Binghamton United States Astana It University Dept. of Computer Engineering Astana Kazakhstan L.N. Gumilyov Eurasian National University Dept. System Analysis and Management Astana Kazakhstan

ISBN: (纸本)9798350372229

The research explores the transformative impact of Artificial Intelligence in meteorology, weather forecasting. The study presented historical prediction methods overview, evolution highlighting from traditional models to advanced AI-driven systems. It delves into the integration of machine and deep learning techniques in data analysis, emphasizing technologies that significantly enhanced the accuracy and efficiency of weather predictions. Key aspects covered include the neural networks application in interpreting complex atmospheric data, the big data role in providing comprehensive training sets for AI models, the predictive analytics utilization for short-term and long-term weather forecasts. The study also examines case-study with AI implemented in weather forecasting, capability demonstrating in handling extreme weather events and climate anomalies. The study addresses the challenges and limitations faced in the meteorology AI integration, such as data quality concerns, computational requirements, and the specialized expertise need. It proposes potential solutions, future directions for research domain, suggesting a multidisciplinary approach involving meteorologists, data scientists, and AI experts. The conclusion underscores the revolutionary impact of AI-technology in meteorology, projecting how continuous advancements in AI-technology could redefine the predicting weather patterns approach. The work highlights the AI current state in weather forecasting but also sets the future innovations stage. © 2024 IEEE.

关键词： artificial intelligence data analysis machine learning neural networks predictive analytics weather Forecasting

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An Empirical Analysis of Relationship between Economic Growth and Freedom: A Cross Country Study using Machine Learning Approach 14

An Empirical Analysis of Relationship between Economic Growt...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shivkumar, S. Gupta, Deepa Deepika, M.G. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita School of Management Dept. of Management Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350335095

While there is sufficient exploration on the role of labour, capital and technology as determinants of economic growth, less is analysed on the role of economic and political freedom as factors influencing the economic growth of a country. The economic growth of some countries is high and in some low. The reason for this variation has been a matter of discussion in economic literature. In this paper, the relationship between economic growth and economic freedom along with labour and capital is analysed. The multi-linear regression model is used to estimate the relationship. The results obtained indicate that there exists a significant relationship between economic growth, economic freedom, labour and capital. The R2 score was 0.929 which states that the dependent variable is explained well by the independent variables. © 2023 IEEE.

关键词： Machine learning

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