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Digital Platforms and Societal Harms (DPSH), IEEE

作者： Diya Saraf Yuhong Liu Hooria Jazaieri Dept. of Computer Science & Engineering Santa Clara University Santa Clara California USA Dept. of Management Santa Clara University Santa Clara California USA

ISBN: (数字)9798350351309

ISBN: (纸本)9798350351316

This paper investigates the impact of emojis on user engagement in trolling content on a social media platform. Utilizing a comprehensive dataset collected from Reddit, this study categorizes emojis by emotional content and popularity to analyze their effects on user comments and upvotes. Findings indicate that user engagement can be significantly enhanced by more popular emojis and emojis that convey emotionality while reduced by excessive or misplaced informational emojis. This research offers critical insights for strategic emoji use to foster positive online interactions and mitigate trolling, benefiting content moderators and digital communicators.

关键词： Social networking (online) Media Emojis

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Machine Learning Approaches To Predict The Stability of Smart Grid

Research Square

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Research Square 2024年

作者： Satu, Shahriare Khan, Imran Dept. of Management Information Systems Noakhali Science and Technology University Noakhali3814 Bangladesh Dept. of Computer Science and Engineering Gono Bishwabidyalay Dhaka1344 Bangladesh

The smart grid is a modern solution to generate, distribute, and use energy effectively and efficiently. Ensuring the stability of the smart grid is critical to guarantee safe and consistent operation. This study proposes a machine learning model to identify smart grid stability more efficiently. To accomplish this, we collected the electrical grid stability dataset from a public machine learning repository and analyzed the correlation of individual features. Different feature selection and transformation techniques were employed to generate subsequent feature sets. Then, various classification techniques were applied to the primary and its subsets to identify the best machine-learning model for predicting smart grid stability more efficiently. The results indicated that MLP showed the highest accuracy (97.06%) for the Z-score transformed data set using Grid Search Optimization. This study demonstrates the potential of using machine learning to predict smart grid stability accurately and efficiently, providing a critical tool for improving the reliability, efficiency, and sustainability of the energy industry. © 2024, CC BY.

关键词： Smart power grids

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Machine Learning Based Hybrid Technique for Heart Disease Prediction

Machine Learning Based Hybrid Technique for Heart Disease Pr...

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2022 International Conference on Advances in Computing, Communication and Materials, ICACCM 2022

作者： Negi, Sourav Singh Memoria, Minakshi Kumar, Rajiv Joshi, Kapil Pandey, Shiv Dayal Gupta, Ashulekha Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India Graphic Era University Dept. of Management Studies Dehradun India

ISBN: (纸本)9781665474399

There is a lot of information in the medical services industry. With such a big amount of data, the illness can often be identified, predicted, or reduced. Infections such as cardiovascular sickness, malignant development, tumours, and so on pose a significant threat to humanity. In this research, we try to focus on coronary sickness prediction using AI approaches. Coronary illness is frequently predicted. Information such as pulse, hypertension, diabetes, and cigarette smoking is collected, and these highlights are then displayed for forecasting. The calculations like K-nearest neighbor, Random Forest and Decision tree are used. We have also proposed a hybrid model of combining Decision Tree and Random Forest. The precision of the model is to investigate utilizing every one of the calculations. At that point with the more exactness is taken as a result of the model for anticipating the daringness infection. © 2022 IEEE.

关键词： Decision trees

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

Using IoT Enabled Predictive Maintenance to Enhance Business...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

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

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

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.

关键词： Industries Wireless communication Technological innovation Wireless sensor networks Sensor systems Real-time systems Maintenance Internet of Things Business Predictive maintenance

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Exploring the Integration of Generative AI in Modern Education Systems: A Comprehensive Analysis

Exploring the Integration of Generative AI in Modern Educati...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Avick Kumar Dey Vineet Kumar Chauhan Pradeep Kumar Singh Ananya Sarkar Dept. of Computer Applications Durgapur West Bengal Dept. of Information Technology Galgotias College of Engg. & Tech Greater Noida School of Computer Science and Engineering Galgotias University Greater Noida Dept. of Computer Applications DSMS College of Tourism and management West Bengal

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

This paper aims at filling this gap through dissecting the applicability of the Generative AI in the current learning and teaching system. The paper offers an introduction to Generative AI technologies: GPT and DALL-E, and describes the chronological history and current progress of its educational applications. Other objectives include discussing about the uses of Generative AI in adaptive learning environment, smart learning systems, automation of organizational work and in talented minds improvement. Analyzing key successful AI cases, the paper reveals the challenges implementers encountered, and provides debates about the appropriate practices of AI. Research evidence points to the prospect of Generative AI benefiting students and educators in teaching and learning, which indicates the innovative potential of the technology, yet at the same time reveals the potential risks and challenges that need to be addressed concerning the ethical and privacy implications of Generative AI in education. Others may follow general recommendations for policymakers and educators on how to integrate artificial intelligence responsibly, as well as future work for this quickly burgeoning domain.

关键词： Ethics Privacy Quantum computing Automation Generative AI Law enforcement Education Educational technology Filling History

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Modeling Hot Rolling Process Forces Using Bio-Inspired Metaheuristic Search Algorithm 2

Modeling Hot Rolling Process Forces Using Bio-Inspired Metah...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Sheta, Alaa Braik, Malik Oznergiz, Ertan Elashmawi, Walaa H. Rausch, Peter Othman, Emad S. Southern Connecticut State University Department of Computer Science New HavenCT United States Al-Balqa Applied University Computer Science Department Salt Jordan Istanbul Technical University Mechanical Engineering Faculty Istanbul Turkey Misr International University Dept of Computer Science Cairo Egypt George Simon Ohm Univ. of Applied Sciences Computer Science Department Nuremberg Germany AL-Shorouk Academy Management Information System Dept. Cairo Egypt

ISBN: (纸本)9798350362633

This research introduces the application of an innovative bio-inspired metaheuristic technique, termed the Crow Search Algorithm (CSA), to model a crucial industrial process - hot rolling manufacturing. Inspired by the foraging patterns of crows, the CSA algorithm has demonstrated its prowess in solving diverse optimization challenges. In the context of this study, the CSA algorithm is harnessed to fine-tune the parameters of a simulation model focused on predicting the force exerted during a hot rolling procedure. The proposed model takes into consideration a range of influential factors, including the initial temperature (Ti), width (Ws), carbon equivalent (Ce), gauge (hi), draft (i), and roll diameter (R). The findings underscore the CSA's capability to deliver an exceptional modeling performance characterized by swift convergence and high solution quality. By getting along very well with the proposed model with the CSA algorithm, a robust and efficient avenue to optimize the hot rolling process emerges, with the potential for expansion into other manufacturing domains. The computational and simulation results demonstrated that the proposed approach-based CSA outperformed different meta-heuristic search algorithms, such as the Salp Swarm Algorithm (SSA), Dandelion Optimizer (DO), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Moth-Flame Optimization (MFO), in all test cases. The CSA has achieved the highest coefficient of determination (R2), equal to 0.97244, and the lowest mean squared error (MSE), equal to 1904.97, compared to its opponent algorithms. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Exploration of Ripe Raw and Defective Tomatoes with Deep Learning Techniques 2

Exploration of Ripe Raw and Defective Tomatoes with Deep Lea...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Vanitha, C.N. Sathyamoorthy, Malathy Dhanaraj, Rajesh Kumar Rai, Priti Geetha, V. Manikantan, M. Kongu Engineering College Science and Engineering Department of Computer Erode India Kpr Institute of Engineering and Technology Department of Information Technology Coimbatore India Pune India Gl Bajaj Institute of Management Greater Noida India Reva University School of Applied Sciences Department of Computer Science Bangalore India Kongu Engineering College Dept. of CT-UG Perundurai India

ISBN: (纸本)9798350325799

Due to great need of agricultural operations, there was a shortage of workers during the harvest period. This results in a bottleneck in the operations of agricultural organizations. One of the most common problems in these sectors is the hazardous practice of farming. This type of farming operation makes vegetables very costly and unattractive. The reduction of costs is controlled by the need for labour. This issue is solved by adapting to new technology and compensating the labour deficiency. The algorithms that are processed in this method are Convolutional Neural Network (CNN), Object Detection and Generative Adversarial Network (GAN). These algorithms will enable the model to recognise faulty tomatoes. This model accurately identifies raw, ripe and defective tomatoes using deep learning methods and it is highly helpful for the betterment of agriculture. © 2023 IEEE.

关键词： Object detection

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Automated Healthcare Medical Imaging through EOA Optimized Hyperparameter in CNN

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Procedia computer Science 2025年 259卷 1106-1114页

作者： Umang Kumar Agrawal Nibedan Panda Debashreet Das Asish Kumar Dalai B.V. Ramana Ayush Mishra School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India-751024 Dept. of Computer Science and Engineering Centre for UG and PG studies BPUT Rourkela Odisha School of Computer Science and Engineering VIT-AP University Amaravati AP India Department of IT Aditya Institute of Technology and Management Tekkali AP India-532201

Medical imaging (MI) is the prefatory field of healthcare that plays a vital role in the patient’s clinical and medical intervention. Recent developments in MI help healthcare professionals such as doctors and radiologists diagnose patients with improved quality of MI methods such as X-ray, CT and MRI scans. The upsurge of the Convolutional Neural Network (CNN) offers the latent for timely detection and classification of the disease with deeper insights. It adapts the spatial features and learns the input data hierarchically, which makes well-suited for this task. To improve the robustness of the CNN, the parameters are optimized through a nature-inspired dynamic mass balance-based metaheuristic approach named the Equilibrium Optimizer Algorithm (EOA). As a real-life implication, the suggested EOA-CNN approach has been verified by utilizing two benchmark datasets. The competence of EOA-CNN is assessed by present-day methods in terms of parameters namely accuracy, error rate, min, avg, std, accuracy, sensitivity and specificity. By streamlining the workflow, the said approach is a valuable asset conforming to the reliability to resolve healthcare imaging complications with advanced diagnostics support. This framework is being used to optimize the constraints meanwhile bridging the gaps in healthcare convenience.

关键词： EOA CNN Optimizing Parameters Healthcare Imaging Brain MRI Chest X-ray

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Automated Assessment of Medical Rehabilitation of Gait by Means of Video Processing in Cloud Environment

Automated Assessment of Medical Rehabilitation of Gait by Me...

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E-Health and Bioengineering Conference (EHB)

作者： Elena-Claudia Maftei Otilia Zvorişteanu Hariton-Nicolae Costin Silviu Bejinariu Florin Rotaru Vasile Manta Ramona Luca Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iaşi Institute of Computer Science Romanian Academy Iaşi Branch Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Human gait analysis provides qualitative and quantitative information concerning different characteristics of walking of a certain person. Cloud computing proves to be a modern and valuable technology when dealing with data scalability, security and efficiency of processing and storage. Also, this paradigm has some key-benefits like real-time data sharing, fast computation as well as collaborative research. The applications gamut which uses cloud computing is getting bigger and bigger, and healthcare sector is an important domain in this respect. Our approach proposed a cloud-based service designed for the automated assessment of medical rehabilitation of human gait by using video processing. We have chosen this processing technology because it is marker-less, allows fast computation and offers a very good precision of the gait analysis. Cloud computing approach proved to be encouraging and underscores remarkable advantages in comparison with traditional on-site video processing. In this way the patient, after a short training session, can himself/herself make tests and assessments of his/her mobility status, avoiding many consultations of medical or kineto-therapeutic staff.

关键词： Legged locomotion Training Cloud computing Scalability Medical services Gain measurement People with disabilities Real-time systems Security Personnel

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A Web and Mobile Application to Identify the Availability of Drinking Water Information During a Water Crisis

A Web and Mobile Application to Identify the Availability of...

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2023 Intelligent Computing and control for engineering and Business Systems, ICCEBS 2023

作者： Razeeth, Suhail Shihara, Fathima Pavithra, Kasuni Kaliyamoorthy, Maran Nawaz, Samsudeen Sabraz Kariyapper, R.K.A.R. South Eastern University of Sri Lanka Dept. of Information and Communication Technology Sri Lanka Uva Wellassa University of Sri Lanka Dept. of Computer Science and Informatics Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India South Eastern University of Sri Lanka Department of Management and Information Technology Sri Lanka

ISBN: (纸本)9798350394580

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers. © 2023 IEEE.

关键词： crowdsourcing GIS mobile application water availability web application

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