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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Sakshi Gojaria Vedika Kolambkar Aditi Pandey Pratibha Sajwan Department of Artificial Intelligence and Data Science Thakur College of Engineering and Technology Mumbai India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This compilation introduces a series of coding challenges and activities strategically crafted to nurture computational thinking skills across different proficiency levels. These exercises accommodate both individual and collaborative learning preferences targeting essential elements like problem-solving, algorithmic design, pattern recognition abstraction, and logical reasoning. These coding challenges are for everyone, from beginners to advanced learners, and can be used with different programming languages. They help build skills starting from basic problem-solving to more complex tasks like breaking down advanced problems. These challenges specifically draw inspiration from practical domains like data science, artificial intelligence, and software engineering which encourages learners to explore and understand the practical connections between theoretical concepts and real-world applications which gives a thorough grasp of computational thinking in action.

关键词： Systematics Focusing Information processing data science Encoding Teamwork Pattern recognition Problem-solving artificial intelligence Software engineering

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Deep Neural Networks for Image-Based Indian Sign Language Recognition: A Comprehensive Review and Practical Analysis 1

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2nd International Conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Kaur, Harleen Bedi, Sanjam Kaur Rao K, Mallikharjuna Lekhana, M.A. Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Chhattisgarh Naya Raipur India Electronics and Communication Engineering International Institute of Information Technology Naya Raipur Chhattisgarh Naya Raipur India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Addressing the communication barriers faced by individuals with vocal and hearing disabilities, sign language serves as a visual means of expression. However, the limited understanding of sign language among the general population creates significant challenges in various settings, such as banks, airports, and supermarkets. To overcome this, a sign language recognition (SLR) system is essential. This study focuses on developing a real-time word-level sign language recognition system that translates sign language into text, with a specific emphasis on Indian sign language (ISL) to cater to the needs of the deaf and hard-of-hearing community in India. Our research presents an ISL-based sign language recognition system, enabling users to capture hand movement images through a web camera, with the system accurately predicting and displaying the corresponding sign name. The captured images undergo several processing stages, incorporating computer vision techniques like grayscale conversion, dilatation, and masking. To achieve robust recognition, a convolutional neural network (CNN) is trained on the collected dataset, demonstrating exceptional performance with a 99% accuracy rate. This research contributes to the advancement of assistive technologies by providing an effective solution for real-time ISL recognition, promoting inclusive communication and accessibility for individuals with hearing and vocal impairments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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Implementing Catboost Algorithm for Allergen Cross Contamination Detection in Food Industry 1

Implementing Catboost Algorithm for Allergen Cross Contamina...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Sudharson, D. Kumar, D. Satheesh Dubey, Aman Kumar Kumar, B. Arun Vaishali, V. Balavedhaa, S. Kumaraguru College of Technology Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Hindusthan College of Engineering &technology Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Master of Bussiness Administration Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Cse Tamil Nadu Coimbatore India Kumaraguru College of Engineering Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359756

In the food industry, allergen cross-contamination is a serious problem that necessitates innovative approaches to detection and prevention. In order to tackle this problem, this paper investigates the possibilities of machine learning (ML) techniques, focusing on CatBoost, XGBoost, and LightGBM in particular. Through an analysis of datasets from food processing establishments, the research develops prediction models that leverage on the distinct advantages of these machine learning methods. The models' comparative evaluations demonstrate how well they identify allergen cross-contamination risks, improving food safety protocols. This paper demonstrates the significant role of CatBoost, XGBoost, and LightGBM in strategically controlling allergen cross-contamination and offers insightful information about the applicability of ML. In the final analysis, these results provide a framework for the advancement of risk-mitigating intelligent systems that ensure consumer safety and support the industry's broader food safety regulations. © 2023 IEEE.

关键词： Food safety

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Classification of Brain Tumours in Medical Images Through the Application of Machine Learning

Classification of Brain Tumours in Medical Images Through th...

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data, Computation and Communication (ICDCC), International Conference on

作者： Heena Kuntal Ebtasam Ahmad Siddiqui Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Jaipur India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Early detection of brain tumors is crucial, yet existing segmentation methods have not yielded satisfactory results. Brain tumors are deadlier than many other cancers, primarily because their complex structures often merge with normal tissues, complicating classification. This study explores machine learning techniques to classify brain tumors—specifically Glioma, Meningioma, and Pituitary tumors—using an MRI scan dataset. We employ convolutional neural networks (CNNs) and ensemble approaches, integrating advanced pre-processing, feature extraction, and model optimization for improved accuracy. Our models achieve state-of-the-art precision, recall, and F1 scores, outperforming traditional methods. Future work will expand the dataset and incorporate multimodal imaging and federated learning to improve generalization and privacy.

关键词： Support vector machines Training Accuracy Magnetic resonance imaging Brain tumors Nearest neighbor methods Brain modeling Convolutional neural networks Reliability Medical diagnostic imaging

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Cultivating Archipelago of Forests: Evolving Robust Decision Trees Through Island Coevolution 39

Cultivating Archipelago of Forests: Evolving Robust Decision...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Zychowski, Adam Perrault, Andrew Mańdziuk, Jacek Faculty of Mathematics and Information Science Warsaw University of Technology Poland Department of Computer Science and Engineering The Ohio State University United States Faculty of Computer Science AGH University of Krakow Poland Center of Excellence in Artificial Intelligence AGH University of Krakow Poland

ISBN: (纸本)157735897X

Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The paper proposes a novel island-based coevolutionary algorithm (ICoEvoRDF) for constructing robust decision tree ensembles. The algorithm operates on multiple islands, each containing populations of decision trees and adversarial perturbations. The populations on each island evolve independently, with periodic migration of top-performing decision trees between islands. This approach fosters diversity and enhances the exploration of the solution space, leading to more robust and accurate decision tree ensembles. ICoEvoRDF utilizes a popular game theory concept of mixed Nash equilibrium for ensemble weighting, which further leads to improvement in results. ICoEvoRDF is evaluated on 20 benchmark datasets, demonstrating its superior performance compared to state-ofthe-art methods in optimizing both adversarial accuracy and minimax regret. The flexibility of ICoEvoRDF allows for the integration of decision trees from various existing methods, providing a unified framework for combining diverse solutions. Our approach offers a promising direction for developing robust and interpretable machine learning models. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Adversarial machine learning

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Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

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Computers, Materials & Continua 2024年第6期79卷 4741-4762页

作者： Zhenyu Qian Yizhang Jiang Zhou Hong Lijun Huang Fengda Li Khin Wee Lai Kaijian Xia School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China Department of Medical Imaging The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Department of Neurosurgery The Changshu Affiliated Hospital of Soochow UniversityChangshu215500China Department of Scientific Research The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Changshu Key Laboratory of Medical Artificial Intelligence and Big Data Suzhou215500China Department of Biomedical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical *** deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical *** MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature ***,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering *** address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss *** experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC *** results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these ***,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

关键词： Deep subspace clustering multiscale network structure automatic hyperparameter tuning semi-supervised medical image clustering

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Machine Learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges 4

Machine Learning for Real-Time Language Translation: A Serve...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

The evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-The-Art results in language translation, a universal solution remains elusive. This research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. The process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). This approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Monkeypox Image Recognition: Leveraging ResNet-MobileNet Fusion for Enhanced Performance 9

Monkeypox Image Recognition: Leveraging ResNet-MobileNet Fus...

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9th International Conference on Information science, Computer technology and Transportation, ISCTT 2024

作者： Liu, Yi-Zhi Lo, Tsung-Ming Zhang, Wan-Ying Zhang, Hong-Tao Lin, Zi-Jing Liu, Xin-Yan Chen, Yu Nanfang College-Guangzhou School of Electrical and Computer Engineering Guangzhou China Dongguan University of Technology Guangdong-Taiwan College of Industrial Science & Technology Dongguan China Guangzhou Xinhua University Department of Artificial Intelligence and Data Science Dongguan China

ISBN: (纸本)9798350388435

In recent years, the monkeypox virus has become a global health concern. Monkeypox is highly transmissible and usually results in severe skin damage and systemic complications. Therefore, accurate and rapid detection of monkeypox skin symptoms is crucial for timely diagnosis and treatment, as well as for informing outbreak control strategies and vaccine development efforts. In this paper, we propose an efficient method for monkeypox image recognition using deep learning techniques that integrate ResNet and MobileNet models. By integrating the residual learning architecture of ResNet and the deep detached convolutional design of MobileNet, our method achieves an ultrahigh classification accuracy of 99.3%, along with a mean absolute error (MAE) of 0.0119 and a root mean square error (RMSE) of 0.1558. ©2024 IEEE.

关键词： Adversarial machine learning

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Multilingual and tunisian dialect analysis of banking app reviews using LLMs

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Expert Systems with Applications 2025年 290卷

作者： Mahouachi, Rim Mahersia, Hela LR - IDEA [Artificial Intelligence Data Engineering and Applications Faculty of Science of Bizerte University of Carthage Zarzouna7021 Tunisia

Digital technology is redefining the model of industry in banks and thus the future of banking. Accordingly, Tunisian banks have found in mobile applications, digital payment methods and other tools, a way to respond more effectively to their customers’ *** research examines whether these digital products provide a true fit to customer expectations and needs as experienced by actual users. Despite various techniques for the analysis of app reviews being previously established, feedback in Arabic and dialects of Arabic seem to have been neglected. The lack of studies in this area is what drives our study, which aims to explore reviews written in more than one language, focusing in particular on Arabic and Tunisian dialect. Through text mining and machine learning methods, such as sentiment analysis and topic modeling, this research reveals the gaps in mobile banking products. It also look into how large language models (LLMs) can be used to classify app reviews across different languages something that hasn't really been studied much so *** findings are intended to help banks shape more user-focused digital strategies, ultimately making the banking experience smoother and more engaging for customers. © 2025 Elsevier Ltd

关键词： Natural language processing systems

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Investigation of Red Fox Algorithm for Solving Task Assignment Problem in Heterogeneous Unmanned Aerial Vehicle Swarm 3

Investigation of Red Fox Algorithm for Solving Task Assignme...

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3rd International Conference on artificial intelligence For Internet of Things, AIIoT 2024

作者： Manaswitha, K. Issac, Titus Silas, Salaja Terance, J Sebastian Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350372120

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications. © 2024 IEEE.

关键词： Energy utilization

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