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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Debbarma, Shayani Pavithra, Nitturu Prakash, B. Srm Institute of Science and Technology Ktr Dept.of Ctech Chennai India Srm Institute of Science and Technology Faculty Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350353068

A new method of data compression called deduplication helps make better use of storage space and works better when handling duplicate data. Original, unique data copies can be uploaded to storage thanks to deduplication, and subsequent copies are given a linking pointer to the genuine stored copy. Data privacy is an important issue in cloud computing that needs to be taken into account. This study reviews and assesses the methods of the most common deduplication algorithms. The resulting survey is tabulated to highlight the precise nature of the deduplication's evolution and varieties. This survey has shown us that deduplication compromises data confidentiality on a number of levels, which opens the door for more unique ideas. © 2024 IEEE.

关键词： Data privacy

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Flood Detection on Adverse Natural Conditions using CatBoost algorithm 7

Flood Detection on Adverse Natural Conditions using CatBoost...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Bharath, D. Singh, Sushant Kumar Smriti, Saloni Arun, A. SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu India SRM Institute of Science and Technology School of ComputingFaculty of Engineering and Technology Department of Networking and Communications Tamil Nadu India

ISBN: (纸本)9798350372816

This study shows a good way to find floods when the weather is bad by using the CatBoost algorithm. It is very important to have accurate and quick tools for finding floods so that people, property, and the environment are safe from this huge danger. The CatBoost algorithm is used in this work to improve the accuracy of flood warning models. This algorithm is known for its ability to handle categorical data and make good estimates. The suggested way looks at places that are likely to flood by using source saturation pressure, inside width, mass flux, heat flux, and refrigerant properties. Our results, which came from a lot of testing and confirmation, show that the CatBoost algorithm can correctly find and predict floods, even when the environment is very bad. This work improves flood monitoring technology and offers a viable weather early warning tool. © 2024 IEEE.

关键词： Environmental monitoring

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Predictive Salary Modelling: Leveraging Data science Skills and Machine Learning for Accurate Forecasting 9

Predictive Salary Modelling: Leveraging Data Science Skills ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Haseeb, Mohammed Ayman Viswanathan, Rishi Iyer, Kaushik Hota, Anish Raj Prathaban, Banu Priya School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur Chennai India

ISBN: (纸本)9798350377972

In approaching predictive salary modeling, this article identified important job positions and skills in the data science business environment. They show the need for a model that would allow employers to calculate the required starting salary while considering sectors, Python known languages, SQL, and ML. It also analyzes linear regression models, Random Forests, and neural network point of view about salary prediction. Analyzing the findings, it is proved that skill-based salary models should be further developed to meet industry requirements, advise students, and support employers. We have applied various models as follows with random forest performing the best giving out an R2 score of 0.971. The ideas for building faultless, precision, and skill-based salary estimation models are presented to the readers at the end of the article. © 2024 IEEE.

关键词： Prediction models

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Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

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Computer Modeling in engineering & sciences 2025年第2期142卷 1233-1288页

作者： Mohamad Khairulamirin Md Razali MasriAyob Abdul Hadi Abd Rahman Razman Jarmin Chian Yong Liu Muhammad Maaya Azarinah Izaham Graham Kendall Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Center for Artificial Intelligence Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Medicine Universiti Kebangsaan Malaysia Medical CentreCheras56000Kuala LumpurMalaysia School of Engineering and Computing MILA UniversityNilai71800Negeri SembilanMalaysia School of Computer Science University of Nottingham-Malaysia CampusSemenyih43500SelangorMalaysia

The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem *** hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search *** selection hyper-heuristics have different imple-mentation ***,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different *** question is how to effectively employ them to produce an efficient search ***,the algorithms that competed in the inaugural CHeSC have not been collectively *** work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is *** analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter *** review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these *** effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic *** are also compared with recent trends in *** work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

关键词： Hyper-heuristics search algorithms optimization heuristic selection move acceptance learning diversification parameter control

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Deep Reinforcement Learning Based Bus Stop-Skipping Strategy

Deep Reinforcement Learning Based Bus Stop-Skipping Strategy

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2023 International Technical Conference on Circuits/Systems, Computers, and Communications, ITC-CSCC 2023

作者： Tham, Mau-Luen Tay, Bee-Sim Khor, Kok-Chin Phon-Amnuaisuk, Somnuk Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Department of Electrical and Electronic Engineering Malaysia Department of Internet Engineering and Computer Science Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Malaysia School of Computing and Informatics Universiti Teknologi Brunei Brunei

ISBN: (纸本)9798350326413

Stop-skipping strategy can benefit both bus operators and passengers if the control is intelligent enough to adapt to the changes in passenger demands and traffic conditions. This is possible via deep reinforcement learning (DRL), where an agent can learn the optimal policy by continuously interacting with the dynamic bus operating environment. In this paper, one express bus lane followed by one no-skip flow is treated as one episode for bus route optimization. The objective is to maximize the passenger satisfaction level while minimizing the bus operator expenditures. To this end, a reward function is formulated as a function of passenger waiting time, passenger in-vehicle time, and total bus travel time. By training an agent of a double deep Q-network (DDQN), simulation results show that the agent can intelligently skip the stations and outperform the noskip method, under different passenger distribution patterns. © 2023 IEEE.

关键词： Reinforcement learning

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Effect of an inquiry-based teaching sequence on secondary school students’ understanding of wave optics

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Physical Review Physics Education Research 2024年第1期20卷 010156-010156页

作者： Maja Planinic Katarina Jelicic Karolina Matejak Cvenic Ana Susac Lana Ivanjek Department of Physics Faculty of Science Department of Applied Physics Faculty of Electrical Engineering and Computing School of Education

Wave optics is a mandatory part of Croatian secondary school physics curriculum for students in the final year of secondary school (age 18–19). Many physics education research studies have shown that it is a difficult physics topic for both university and secondary school students. An inquiry-based teaching sequence on wave optics, designed for eight 45-min teaching periods, was developed by the authors. The sequence included four investigative students’ experiments on the topics of interference, diffraction, and polarization of light, as well as several teacher demonstrations. The experimental group included six classes of students from six different Croatian urban secondary schools, who underwent the teaching intervention with the new inquiry-based sequence on wave optics, whereas the control group consisted of six classes from the same schools, taught in a predominantly lecturing way. Both groups were post-tested with the same instrument, the Conceptual Survey on Wave Optics (CSWO), to evaluate the research hypothesis that the new sequence might improve students’ conceptual understanding better than the traditional teaching. The results of the experimental and control groups were analyzed and compared using the Rasch analysis. The results show that the experimental group outperformed the control group in four out of five conceptual areas probed by the CSWO, suggesting that the new inquiry-based teaching sequence may contribute to stronger development of secondary school students’ conceptual understanding of wave optics, especially concerning typical wave optics patterns, reasoning from experiments, and explaining basic wave optics phenomena. A questionnaire on attitudes toward the teaching intervention was administered to students and it was found that students generally liked the inquiry-based teaching intervention and expressed positive attitudes to interactive, experimental, and collaborative aspects of physics teaching. The results are very promising, but the

关键词： Concepts & principles Instructional strategies Scientific reasoning & problem solving

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education 5

Enhancing Early Childhood Learning using Image Processing fo...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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Unveiling Ethical Challenges and Malpractice in Academic Research and Integrity 3

Unveiling Ethical Challenges and Malpractice in Academic Res...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Singh Bist, Ankur Rawat, Bhupesh Rahardja, Untung Hardini, Marviola Kamal, Mustofa Graphic Era Hill University Faculty of Computer Science & Engineering Uttarakhand India Graphic Era Hill University Faculty of Computing Uttarakhand India University of Raharja Faculty of Information Technology Tangerang Indonesia University of Raharja Faculty of Computer System Tangerang Indonesia

ISBN: (纸本)9798350367492

In the pursuit of knowledge and innovation, academic research stands as a cornerstone of intellectual progress. However, the integrity of this process is susceptible to malpractice, raising profound ethical concerns. This research paper delves into the nuanced landscape of malpractice and ethical considerations within the realm of academic research, aiming to shed light on the challenges that undermine the credibility and reliability of scholarly endeavours. The study explores various forms of malpractice, including plagiarism, data fabrication, and authorship disputes, which have the potential to erode the foundations of academic integrity. Through a combination of literature reviews, case studies, and expert interviews, the research seeks to identify patterns and motivations behind such malpractices. The findings reveal that systematic solutions are necessary to address these issues, and propose the integration of advanced technological tools to enhance ethical oversight. This study contributes to the ongoing discourse on ethics in academic research by providing comprehensive insights and recommendations to uphold ethical standards. © 2024 IEEE.

关键词： Confidentiality Ethics Informed Consent Plagiarism Professional Misconduct

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Systematic Literature Review on Early Diagnosis of Oral Squamous Cell Carcinoma by Deep Learning Techniques

Systematic Literature Review on Early Diagnosis of Oral Squa...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sumathy, V. Pretty Diana Cyril, C. Department of Computing Technologies SRM Institute of Science and Technology SRM Nagar TN Kattankulathur603203 India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar TN Kattankulathur603203 India

ISBN: (纸本)9798350305708

Oral Squamous Cell Carcinoma (OSCC) is the seventh most prevalent type of cancer in the neck and head. The prognosis and survival rate of the patient is significantly improved by early identification of OSCC. Due to tumor heterogeneity, such a diagnosis requires much time and a high-efficiency human experience. As a result, artificial intelligence systems assist professionals and physicians in making precise diagnoses. Recent advances in computer vision-based techniques and Computational Intelligence (CI) improve accuracy in medical images. This study aims to develop hybrid methodologies based on fused features to produce excellent outcomes for the early detection of OSCC. This systematic review aims to estimate deep learning (DL) based algorithms for early diagnosis OSCC to assist clinicians in oral cancer diagnosis and screening. The terms "squamous cell carcinoma, " "early diagnosis, " "oral cavity, " "histopathological image, " "biomarker, " "Optical Coherence Tomography (OCT) image, " and "deep learning" were used in a Google Scholar Cochrane and MEDLINE (PubMed), Embase and WoS databases (January 2018 to July 2023) to find relevant articles. The inclusion criteria were the use of deep learning approaches for early diagnosis of OSCC, articles older than 5 years, and publications written in English. Case reports and studies written in foreign languages met the exclusion criteria. 70 publications were chosen to be included in the systematic review out of the 194 studies that were initially found through the search. Deep learning techniques based on hybrid features are examined in evaluating performance metrics and superior results of the present systems employing biomedical images for OSCC diagnosis. It has been established that the deep learning-based early identification method for biomedical images has the ability to offer decision support for efficient oral cancer diagnosis and screening. © 2023 IEEE.

关键词： convolutional neural network deep learning hybrid method OCT Oral Squamous Cell Carcinoma

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Quantile-Based Maximum Likelihood Training for Outlier Detection 38

Quantile-Based Maximum Likelihood Training for Outlier Detec...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Taghikhah, Masoud Kumar, Nishant Šegvić, Siniša Eslami, Abouzar Gumhold, Stefan Faculty of Computer Science Technische Universität Dresden Dresden Germany Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Translational Research Lab Carl Zeiss Meditec AG Munich Germany

ISBN: (纸本)1577358872

Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like autonomous driving and video surveillance systems. Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning. Furthermore, unsupervised generative modeling of inliers in pixel space has shown limited success for outlier detection. In this work, we introduce a quantile-based maximum likelihood objective for learning the inlier distribution to improve the outlier separation during inference. Our approach fits a normalizing flow to pre-trained discriminative features and detects the outliers according to the evaluated log-likelihood. The experimental evaluation demonstrates the effectiveness of our method as it surpasses the performance of the state-of-the-art unsupervised methods for outlier detection. The results are also competitive compared with a recent self-supervised approach for outlier detection. Our work allows to reduce dependency on well-sampled negative training data, which is especially important for domains like medical diagnostics or remote sensing. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Maximum likelihood

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