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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Arumuga Arun, R. Saravanan, K. Manju, J. Suresh, A. Priyanka, N. Islabudeen, M. Vellore Institute of Technology School of Computer Science and Engineering Vellore India R.M.D. Engineering College Department of Information Technology Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798331541170

Weather detection is vital in many fields, including disaster recovery, farming, transportation, and climate studies. Making educated decisions and reducing potential dangers requires the quick and precise observation of weather patterns. As CNN-based deep learning models automatically train and extract significant features from raw image data, they often achieve higher accuracy in a variety of image classification tasks and exhibit good data generalization. In addition to the accurate detection, it should be lightweight to deploy to edge locations. To address this, we have developed a Smart lightweight deep-learning model to detect the weather accurately and it can be conveniently deployable in resource-constrained devices. This study uses several deep lightweight learning models, including MobileNetV2, MobileNetV3_Small, and NasNetMobile, where MobileNetV3_Small is the most efficient regarding weather detection and computational complexity reduction. The proposed approach utilized a Multi-class Weather Dataset for Image Classification for building Smart lightweight deep-learning models. Besides the weather classification, the proposed approach also gives the confidence score for the predicted climate, which provides a numerical indicator to evaluate how likely the predicted climate is to occur. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Modeling of a Photonic Crystal Multiplexer Using Artificial Intelligence

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 59-64页

作者： Karami, Pouya Yahya, Salah I. Palash, Behnam Chaudhary, Muhammad Akmal Assaad, Maher Parandin, Fariborz Roshani, Saeed Hazzazi, Fawwaz Roshani, Sobhan Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University-Erbil Erbil44001 Iraq Department of Software Engineering Faculty of Engineering Koya University KOY45 Koya Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj11492 Saudi Arabia

In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference between the output powers in logical states, which is critical for enhancing the system ability to distinguish between these states. In this study, an ANN model is employed to accurately predict the normalized output power of the designed photonic crystal multiplexer, providing a time-efficient alternative to conventional simulation methods for analyzing multiplexer behavior across various logical states. The results demonstrate significant improvements in signal separation and overall performance compared to previous works. Additionally, a detailed comparison of the normalized output power for different logic states is provided, highlighting the advantages of the proposed design. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Silicon photonics

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Development of Deepfake Detection Techniques for Protecting Multimedia information using Deep Learning 3

Development of Deepfake Detection Techniques for Protecting ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Siva Rama Lingham, N. Devakanth, J Jude Moses Anto Raj, Gowtham Gayathri, K. Janani, R. Dhanapal, R. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Acharya Institute of Technology Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technolgy Department of Information Technology Erode India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The development of proposed deepfake detection techniques plays a prominent role in the safeguarding the multimedia information. The various challenges are addressed using the aid of deep learning techniques. This involves hybrid optimization techniques such as particle swarm optimization and genetic algorithms. This helps to improve the accuracy and efficiency in the detection of deepfake. PSO helps in optimizing the weights and parameters of the neural network to obtain faster convergence. GA helps in obtaining potential solutions to obtain the robust deepfake detection model. The integration of deep learning with hybrid optimization involves the collection and preprocessing of the multimedia information and proceeds to an optimization algorithm. The manipulated content in the online platform are detected during the training process. The hybrid optimization tcehniques helps in obtaining model generability and achieving resilience in various attacks occurring in the deepfake generation. The integration of PSO and GA helps in the accurate detection of deepfake content through continuous training and evaluation. Thus the proposed system forms a safeguarding tool for multimedia content and helps to eliminate various risks. © 2024 IEEE.

关键词： Genetic algorithms

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Sampled-Data Controller Design with Exponential Time-Varying Gain for Complex Dynamical Networks 5

Sampled-Data Controller Design with Exponential Time-Varying...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Qian, Cheng Mao, Shangchun Wan, Zhiwei Wang, Xi Yan, Zhilian College of Electrical and Information Engineering Anhui University of Technology Ma'anshan243032 China School of Computer Science & Technology Anhui University of Technology Ma'anshan243032 China

ISBN: (纸本)9781643684840

The article is dedicated to the design of a sampled-data controller (SDC) with exponential time-varying gain (ETVG) for a kind of complex dynamical networks. It establishes a set of sufficient conditions, expressed as linear matrix inequalities, for the design of SDC with ETVG using coordinate conversion and Lyapunov function. Finally, the validity of the presented results is demonstrated through a practical example. © 2024 The Authors.

关键词： Linear matrix inequalities

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Chili Disease Detection and Classification using Various Machine Learning Techniques 1

Chili Disease Detection and Classification using Various Mac...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Sambrani, Yashodha Rajashekarappa Bhairannawar, Satish Computer Science & Engineering SDM College of Engineering and Technology Dharwad India Information Science & Engineering SDM College of Engineering and Technology Dharwad India Electronics & Communication Engineering SDM College of Engineering and Technology Dharwad India

ISBN: (纸本)9798350323795

For many developing countries like India the role of the agricultural sector is very significant. Chili has a very high economic value. India is ranked 5th in the production and 1st in exporting chili. Chili is one of the important cash crops of our country but they are vulnerable to various fungal, bacterial, and viral diseases. Among these diseases, the leaves of chili plants are more susceptible to harm, it's one main reason we have considered chili leaves for our study. This paper presents a detailed overview of various chili diseases and the work done in this field till now. Manual detection of diseases is a time consuming and strenuous task for the farmers. In this paper we are introducing various image processing and machine learning techniques for early and efficient detection of diseases in chili leaves. The diagnosis of chili disease by capturing leaf images is a very efficient and affordable system, especially for helping farmers to keep an eye on large plantations. © 2023 IEEE.

关键词： Developing countries

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Question Tags or Text for Topic Modeling: Which is better

Question Tags or Text for Topic Modeling: Which is better

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Prabha, Sneh Sardana, Neetu Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Topic modelling is a probabilistic based statistical model used to find the latent topics that best depicts the content of the documents. Community Question Answering websites such as Quora, Stack Overflow and Yahoo! Answers have been prevalently in use, performs topic modeling as lot of queries pour in on daily basis which make it challenging to understand, summarize and synthesize the main topic of discussions. On these websites there are basically two sources of information that are available to analyze the key latent topics: questions text and tags. Questions are in textual format and tags are the keywords or tokens that are related to the question being asked which describes the content of the question. In past studies, most of the researchers have used question text for the purpose of topic modeling. It is still unclear why tag is not being considered for topic modeling. To combat this issue, this paper performs topic modeling using both question tags and text. The topic modeling based on tags has been compared with text based on two metrics namely coherence and perplexity. Experiment has been conducted on three real time datasets namely Artificial intelligence, Software engineering and quantum computing from Stack exchange website. At high level tag-based topic modelling looked promising but closer observation revealed the opposite. It has been found that topic modeling using question text is preferable as topic modelling using tags collapses after a certain number of topics. © 2022 Elsevier B.V.. All rights reserved.

关键词： Websites

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Exploratory Data Analysis and Predicting Indian Crime Against Women Using Machine Learning Approaches 3

Exploratory Data Analysis and Predicting Indian Crime Agains...

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3rd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2025

作者： Gupta, N. Srinivasa Valarmathi, B. Santhi, K. Chellatamilan, T. Nanda, Sameeksha Vellore Institute of Technology School of Mechanical Engineering Department of Manufacturing Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Analytics Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Information Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331521691

Crime forecasting is a critical endeavor aimed at preventing future criminal actions, specifically the rising rate of violence against women in India. This study proposes the model to predict crimes committed against women using data from the National Crime Records Bureau's (NCRB) monthly crime report. The dataset encompasses district and state-wise crime activities from 2001 to 2014. The proposed work objectives are identifying factors influencing these crimes and developing predictive models using machine learning (ML). The study employs Exploratory Data Analysis (EDA), to enhance the predictive power of the models. The suggested study uses XGBoost, Random Forest (RF), and Neural Network (NN). The neural network algorithm outperforms the Random Forest and XGBoost techniques among these three. The Random Forest algorithm has the worst performance. After comparing the accuracies of various methods, such as XGBoost (92%), and Random Forest (89.84%) classifiers using imbalanced datasets (without employing Synthetic Minority Oversampling Technique (SMOTE) technique), it was found that the neural network algorithm (95.81%) delivers the highest accuracy. This study looks at model accuracies and finds that, after applying the SMOTE algorithm, neural network algorithms performed best, with an accuracy of 96.76%, outperforming the accuracies of the remaining models, which included the Random Forest (93.08%) and XG-Boost (95%). © 2025 IEEE.

关键词： Crime prediction Exploratory Data Analysis (EDA) Neural Networks Random Forest SMOTE XGBoost

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XGBoost for Novel Predictive Model to Classify Student Performance Based on Academic Metrics

XGBoost for Novel Predictive Model to Classify Student Perfo...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Sinha, Sweta Singh, Om Prakash Baruah, Hemanta Biswas, Ujjwal Indian Institute of Information Technology Bhagalpur Department of Computer Science and Engineering Bhagalpur India Indian Institute of Technology Guwahati Department of Centre for Linguistic Science and Technology Guwahati India

ISBN: (纸本)9798331540821

Understanding and predicting student performance with academic metrics is essential for better education and learning. Making precise estimates, selecting appropriate measures, and using suitable predictive algorithms may be tricky. In this research, we investigate the significance of these measurements and predictive models. We built XGBoost predictive model to predict student performance. We deployed a novel educational dataset structured from a macro perspective, which has rarely been used in previous research, in place of using specific students as case studies. We employed data prepossessing, feature selection, and feature building to increase the model's generalizability and prediction accuracy. The XGBoost model outperformed the five traditional machine learning models (Decision Tree, Support Vector Machine, Random Forest, Naive Bayes, Neural Network, and K-nearest Neighbor). These models will predict student performance and be classified according to their function and importance. This research shows how these novel measures can be used to predict and assess student performance. Our goal is to recommend machine learning models for predicting academic performance. The encouraging results show how accurately the proposed model predicted students' academic progress. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Face Mask Detection Using Data Augmentation Techniques

Enhancing Face Mask Detection Using Data Augmentation Techni...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Kumar, P. Vinodh Kumar, S. Priya, L. Senthilpandi, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350306927

In the ongoing pandemic that we are in general confronting, a cover has turned into a significant extra that we want to wear at whatever point we step out to safeguard ourselves and everyone around us. People who wear masks are to blame for the success of every nation that escaped the pandemic and resumed normalcy. Great reconnaissance estimates that guarantee that the general population is wearing covers consistently can assist us in beating this pandemic. Our existing camera systems can be used by computer vision systems to examine enclosed spaces and recognise individuals without masks. Best-in-class Item Recognition calculations can run at high derivation speeds, which will make this framework basically deployable progressively. Subsequently, further developing the acknowledgment execution of the current face acknowledgment innovation on the veiled faces is exceptionally critical. The latest high-level face acknowledgment approaches are planned in view of profound realisations, which rely upon countless face tests. The training of object detection models that are capable of locating both the unmasked and masked faces in an image is the objective of this paper. These indicators will likewise be hearty to commotion and give as little room as conceivable to oblige bogus up-sides for veils because of the possibly desperate results that they would prompt. © 2023 IEEE.

关键词： Object detection

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A Novel Approach for Bug Triaging Using TOPSIS 1

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10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Rathoriya, Pavan Panda, Rama Ranjan Nagwani, Naresh Kumar Department of Information and Technology National Institute of Technology Raipur India Department of Computer Science and Engineering National Institute of Technology Raipur India

ISBN: (数字)9789811975240

ISBN: (纸本)9789811975233

In the field of software development life cycle, the maintenance phase is one of the focused steps. Normally, thousands of bugs are reported daily by testers. So, it is very important to fix that bug as soon as possible. In the current era, various project are there that work on the same project and if bug not fixed timely the other product can easily overtake the company business, so to fix the newly arrived bug the project manager finds the best developer to fix it and assign to the developer, and this process is called bug triaging. For bug triaging task automation, various methods had been carried out by various researchers machine learning, information retrieval, deep learning, etc., but the cons with that method were that they were not able to fix the problem simultaneously like bug tossing, load balancing, and developer availability. Hence to overcome that we have proposed a method called technique for order of preference by similarity to ideal solution (TOPSIS) which is based on multi-criteria decision-making (MCDM), which will consider the developer metadata with various criteria to automate the task of bug triaging. Based on the criteria, the parameter (closeness ratio) will be calculated, and based on the parameter value, the developer will be ranked for bug triaging. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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