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Innovations and Challenges in Emerging Technologies (ICICET), International Conference on

作者： Devendra Kumar Arnav Kothiyal Raj Kumar C Hemantha Ramya Maranan Department of Electronics and Communication Engineering GLA University Mathura Uttar Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Department of Mechanical Engineering BGS Institute of Technology BG Nagar Mandya Karnataka India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350319019

ISBN: (纸本)9798350319026

This study explores the application of Random Forest, a popular ensemble learning technique, in predicting student dropout behavior. The Random Forest algorithm is optimized using the Grid Search process to fine-tune its parameters, enhancing its ability to discern patterns indicative of dropout and non-dropout students. Dropout prediction in educational settings is of paramount importance for early intervention and support, aiming to mitigate the negative consequences of dropout on both individuals and society. By leveraging large-scale educational datasets containing diverse student attributes and academic performance metrics, the Random Forest model is trained to effectively classify students into dropout and non-dropout categories. Through rigorous evaluation and validation procedures, the optimized Random Forest model demonstrates promising performance in terms of predictive accuracy, sensitivity, and specificity. The findings suggest that Random Forest, empowered by parameter optimization through Grid Search, holds significant potential as a reliable tool for identifying students at risk of dropout, thereby enabling timely interventions to promote student retention and academic success. This research contributes to the advancement of predictive analytics in education and underscores the utility of machine learning algorithms in addressing critical challenges in student retention and success initiatives.

关键词： Technological innovation Education Refining Predictive models Sensitivity and specificity Prediction algorithms Data mining

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Modern Computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Design of Sensor Based Walnut Cracking Device and By-Product Classification via K-Means

Design of Sensor Based Walnut Cracking Device and By-Product...

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Kanwalpreet Kour Jhansi Bharathi Madavarapu Mudita Deepali Gupta Rajesh Kumar Dhanaraj Shilpa Saini Department of Computer Science and Engineering Lovely Professional University Punjab India University of Cumberlands United States Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of CSE Chandigarh University Punjab India

Kashmir is a leading producer of walnuts in India. They are considered the healthiest nuts and are very costly owing to their nutritional value. Kashmir is one of the leading producers of walnuts, contributing to about 90% of production in India. However, the farmers don't get much higher rates for these organically produced walnuts as the separation of kernel and shell is a laborious task. This paper proposes an automatic device to separate walnut shells and their hulls making them market ready at a faster processing speed. The walnuts are passed through a conveyor belt where they are broken down by using automated hammer, applying the perfect pressure so as not to break the walnuts completely and ensure the kernel is taken out of the green and brown shell. The shell and the kernel are separated by the data from the color sensor and image processing technology with classification techniques. The device provides a sustainable solution for post harvest agricultural practices in walnut cultivation.

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Design of a Secure Machine Learning-Based Malware Detection and Analysis Scheme

Design of a Secure Machine Learning-Based Malware Detection ...

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2023 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2023

作者： Kumar, Pankaj Obaidat, Mohammad S. Pandey, Piyush Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computing Srm University Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Algorithmic Research International Institute of Information Technology Center for Security Theory Hyderabad India

ISBN: (纸本)9798350302561

Today's world is rapidly growing towards the advanced concept of technology. technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score. © 2023 IEEE.

关键词： Machine learning

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An Advanced Strategy for Efficient Agricultural Production using Machine Learning

An Advanced Strategy for Efficient Agricultural Production u...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： S. Athinarayanan R Selvi K Rajakumari R Arunadevi M Robinson Joel Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Information Technology Dr MGR Educational and Research Institute Chennai Department of Computer Science BIHER (Bharath University) Chennai Department of Computer Science and Engineering Prince Shri Venkateshwara padmavathy Engineering College Chennai Tamil Nadu Department of Information Technology Kings Engineering College Chennai India

Agriculture is very important to the prosperity of the country. Climate change has created a variety of problems for the agricultural academic system. Since machine learning (ML) provides effective and affordable solutions, it is the most effective method for solving problems. Crop yield prediction involves estimating the crop yield based on historical data and a variety of other factors, including the climate, sometimes the environment, the water source, and warmth. This study analyses and discusses the use of the linear regression technique for evaluating agricultural production based on data from the earlier study. This study uses geographic and meteorological data to anticipate winter wheat production. It is motivated by this data science issue. The project’s goal is to find a solution to the cost loss issue. Actual agricultural data is used to build the models, which are then evaluated on samples. Using the vegetable yield forecasting model, end users (agriculturalist) may foresee crop production as they plant crops on agricultural land. The Linear Regression Algorithm method is used to forecast results with accuracy. The decision-making model’s creation will be aided by the availability of a sizable dataset.

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Out Domain Data Augmentation on Punjabi Children Speech Recognition using Tacotron 1

Out Domain Data Augmentation on Punjabi Children Speech Reco...

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1st International Conference on Mechatronics and Artificial Intelligence, ICMAI 2021

作者： Hasija, Taniya Kadyan, Virender Guleria, Kalpna Centre of Excellence for Speech and Multimodal Laboratory Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The performance of Automatic Speech Recognition (ASR) is directly proportional to the quality of the corpus used and the training data quantity. Data scarcity and more children's speech variability degrades the performance of ASR systems. As Punjabi is a tonal language and low resource language, less data is available for Punjabi children's speech. It leads to poor ASR performance for Punjabi children speech recognition. To overcome limited data conditions, in this paper, two corpora of different domains are evaluated for testing the feasibility of ASR performance. We have implemented Tacotron as an artificial speech synthesis system for Punjabi Language. The speech audios synthesized by Tacotron are merged with available speech corpus and tested on Punjabi children ASR using Mel Frequency Cepstral Coefficients (MFCC) + pitch feature extraction, and Deep Neural Network (DNN) acoustic modeling. It is noticed that the merged data corpus has shown reduced Word Error Rate (WER) of the ASR system with a Relative Improvement (RI) of 9-12%. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS

arXiv

引用

arXiv 2023年

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center 2 Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies "Alessandro Faedo" Italy Hacettepe University Department of Computer Engineering 6 Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA1 (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs’ grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2023, CC BY.

关键词： Computational linguistics

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Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

arXiv

引用

arXiv 2023年

作者： Saifullah, Shoffan Drezewski, Rafal Yudhana, Anton Pranolo, Andri Kaswijanti, Wilis Suryotomo, Andiko Putro Putra, Seno Aji Khaliduzzaman, Alin Prabuwono, Anton Satria Japkowicz, Nathalie Institute of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Center of Knowledge Engineering and Data Science Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang65145 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia College of Computer and Information Hohai University Nanjing211100 China Department of Informatics Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia Faculty of Agricultural Engineering and Technology Sylhet Agricultural University Sylhet3100 Bangladesh Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science American University WashingtonDC20016 United States

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate *** Codes CS-Class: 68T07, 68T45, 68U10, ICT-Class: 94A08 © 2023, CC BY-SA.

关键词： Image classification

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm 1

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Indriyono, Bonifacius Vicky Pamungkas, Natalinda Abbas, Fatima Hashim Mintorini, Ery Almohamadi, Mohammed Mahmood, Sarmad Nozad Alani, Sameer Alkhayyat, Ahmed H. Rachmawanto, Eko Hari University of Dian Nuswantoro Information System Kediri Indonesia Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah51001 Iraq University of Dian Nuswantoro Visual Communication Design Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq University of Mashreq Research Center Medical Laboratories Techniques Baghdad Iraq College of Technical Engineering University of Mashreq the Islamic University Najaf Iraq University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments;they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD. © 2023 IEEE.

关键词： Energy efficiency

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Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review

arXiv

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arXiv 2024年

作者： Lopez-Ramos, Luis M. Leiser, Florian Rastogi, Aditya Hicks, Steven Strümke, Inga Madai, Vince I. Budig, Tobias Sunyaev, Ali Hilbert, Adam Holistic Systems department Simula Metropolitan Center for Digital Engineering Oslo Norway Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Germany Department of Neuroradiology University Hospital Bonn Germany Charité - Universitätsmedizin Berlin Germany Department of Computer Science Faculty of Informatics Norwegian University of Science and Technology Trondheim Norway QUEST Center for Responsible Research Charité - Universitätsmedizin Berlin Berlin Germany School of Computing and Digital Technology Birmingham City University Birmingham United Kingdom ETH Zurich Switzerland Germany

The joint implementation of federated learning (FL) and explainable artificial intelligence (XAI) could allow training models from distributed data and explaining their inner workings while preserving essential aspects of privacy. Toward establishing the benefits and tensions associated with their interplay, this scoping review maps the publications that jointly deal with FL and XAI, focusing on publications that reported an interplay between FL and model interpretability or post-hoc explanations. Out of the 37 studies meeting our criteria, only one explicitly and quantitatively analyzed the influence of FL on model explanations, revealing a significant research gap. The aggregation of interpretability metrics across FL nodes created generalized global insights at the expense of node-specific patterns being diluted. Several studies proposed FL algorithms incorporating explanation methods to safeguard the learning process against defaulting or malicious nodes. Studies using established FL libraries or following reporting guidelines are a minority. More quantitative research and structured, transparent practices are needed to fully understand their mutual impact and under which conditions it happens. © 2024, CC BY.

关键词： Federated learning

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