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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Srikrishnan, S. Pushparaj, Ashwin Suresh, Advaith Nair, Akash Krishnan George, Teena Sharika, T.R. Adi Shankara Institute of Engineering and Technology Kalady Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350381689

This survey reviews the current state of personalized voice assistant systems that utilize natural language processing, speech recognition, and AI techniques. It examines methodologies for developing intelligent assistants that can securely access local data, learn preferences, and control IoT devices via voice. The paper analyzes the capabilities, advantages, and limitations of existing voice assistant solutions. It also explores their integration with web technologies and discusses challenges like privacy, speech accuracy, and language modeling constraints. The goal is to provide an overview of personalized voice assistant technologies, their potential impact, and future research directions toward enabling seamless voice-based interactions. © 2024 IEEE.

关键词： Speech recognition

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A Survey on AI based Chatbots for Emotional Health Support 15

A Survey on AI based Chatbots for Emotional Health Support

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Baby, Vadlana Pimpalshende, Anjusha Sunil, Manthena Rithin, Meesala Vamshidhar, Mothukuri Saket, Palarapu Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331300579

The revolutionary potential of emotional chatbots in improving human health is explored in this research. We research the operation of various chatbot kinds in order to reveal important qualities necessary for the development of highly efficient systems. Our examination, which ranges from rule-based to machine learning-based models, provides light on the intricacies of each kind, leading to a better understanding of their mechanics. We highlight critical factors for creating emotionally intelligent chatbots, emphasizing the necessity of adaptive learning, natural language processing, and context awareness. This investigation serves as a starting point for developers, providing insights into the inner workings of various chatbot designs and paving the way for the creation of smart, user-centric, and efficient chatbots poised to revolutionize healthcare support. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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A Systematic Analysis of Multi-Property Prediction Using Deep Learning in the Field of Drug Development

A Systematic Analysis of Multi-Property Prediction Using Dee...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Uriti, Archana Sethy, Abhisek Yalla, Surya Prakash GMR Institute of Technology Rajam Department of Information Technology AP Srikakulam India Silicon Institute of Technology Department of Computer Science and Engineering Odisha India MVGR College of Engineering Department of Computer Science and Engineering AP Vizianagaram India

ISBN: (纸本)9798350369175

In the rapidly changing field of pharmaceutical development, it is crucial to speed up clinical trials in order to introduce effective drugs in a timely manner. Conventional methods of forecasting molecular characteristics are often slow and time consuming, causing difficulties in the drug discovery process. However, Advancements in deep learning have streamlined pharmaceutical testing, enabling precise prediction of key drug properties like solubility, toxicity, reactivity, and bioactivity before synthesis. By employing different Simplified Molecular Input Line Entry System (SMILES) representations and training them with various deep learning algorithms like Graph Neural Networks (GNN) and Graph Convolution Networks (GCN). In this, a comparative study done on various technologies used in predicting molecular properties that helps to develop faster clinical trials in pharmaceutical industry. By identifying the limitations in the existing technologies, proposed an improved GCN which helps in better estimation of molecular properties. © 2024 IEEE.

关键词： Drug discovery

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iWBFCxr: improved weighted box fusion for analyzing chest x ray images for enhancing thoracic abnormalities

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International Journal of Information technology (Singapore) 2025年第2期17卷 711-720页

作者： Verma, Sonia Devarajan, Ganesh Gopal Sharma, Pankaj Kumar Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India Department of Computer Science ABES Engineering College UP Ghaziabad 201009 India

Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes is required, rather than image-level labeling. Accurate interpretation of chest radiographs is essential for diagnosing thoracic abnormalities. This paper introduces a novel method for abnormality detection in chest radiographs using the VinDr-CXR dataset. Labeled training sets are improved by incorporating data augmentation and advanced pre-processing techniques. To improve the accuracy of anomaly identification, the study makes use of ensemble object detection models, such as the modified weighted boxes fusion (iWBFCxr) technique. With an average precision (AP) of 0.2434 for class 14, experimental results on the VinDr-CXR dataset show an overall mean average precision (mAP) of 0.2791. The iWBFCxr approach, when combined with ensemble predictions, has the potential to improve abnormality localization. By integrating enhanced label merging with ensemble object detection models, our method improves abnormality localization in chest radiographs significantly, improving thoracic abnormality diagnosis accuracy and possibly leading to new medical interventions. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Automated classification Automated localization Chest radiographs computer vision Deep learning Machine learning Thoracic anomalies

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National institute of Health vehicle tracking

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Advancing Brain Tumor Detection in MRI: Leveraging NAFNET Denoising for Enhanced CNN Performance 3

Advancing Brain Tumor Detection in MRI: Leveraging NAFNET De...

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

作者： Ramu, Srikumaran Sanjay Kumaran, V. Vijay, M. Balamurugan, A. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

Image denoising is a vital process in the realm of medical imaging and the open-source image denoising algorithms such as NAFNET (Non-Linear Activation Free Network) can be used in improving the quality of brain MRI scans. The objective is to reduce noise in MRI images using NAFNET, thus improving the ability of subsequent CNN models to detect brain tumors. A comparative analysis was conducted between NAFNET and various traditional denoising algorithms. Different evaluation metrics were employed to demonstrate NAFNET's effectiveness in reducing image noise while preserving important details. Additionally, the impact of NAFNET-enhanced denoising on CNN-based brain tumor detection models was evaluated using various assessment metrics. The results highlight the potential of leveraging existing open source denoising algorithms like NAFNET to improve the detailing, accuracy in scan images and enhance the overall quality of CNN models, particularly in detecting brain tumors from MRI scans. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algor

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computers, Materials & Continua 2024年第4期79卷 1727-1755页

作者： Parth Khandelwal Harshit Indranil Manna Metallurgical&Materials Engineering Department Indian Institute of TechnologyKharagpurWest Bengal721302India Computer Science&Engineering Department Indian Institute of TechnologyKharagpurWest Bengal721302India Vice Chancellor Office Birla Institute of Technology(BIT)MesraRanchiJharkhand835215India

Metallic alloys for a given application are usually designed to achieve the desired properties by devising experimentsbased on experience, thermodynamic and kinetic principles, and various modeling and simulation ***, the influence of process parameters and material properties is often non-linear and non-colligative. Inrecent years, machine learning (ML) has emerged as a promising tool to dealwith the complex interrelation betweencomposition, properties, and process parameters to facilitate accelerated discovery and development of new alloysand functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles,to design novel copper alloys for achieving seemingly contradictory targets of high strength and high electricalconductivity. Initially, we establish a correlation between the alloy composition (binary to multi-component) andthe target properties, namely, electrical conductivity and mechanical strength. Catboost, an ML model coupledwith GA, was used for this task. The accuracy of the model was above 93.5%. Next, for obtaining the optimizedcompositions the outputs fromthe initial model were refined by combining the concepts of data augmentation andPareto front. Finally, the ultimate objective of predicting the target composition that would deliver the desired rangeof properties was achieved by developing an advancedMLmodel through data segregation and data *** examine the reliability of this model, results were rigorously compared and verified using several independentdata reported in the literature. This comparison substantiates that the results predicted by our model regarding thevariation of conductivity and evolution ofmicrostructure and mechanical properties with composition are in goodagreement with the reports published in the literature.

关键词： Machine learning genetic algorithm solid-solution precipitation strengthening pareto front data augmentation

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A Progressive Approach of an Augmented Steganography for Network Peers

A Progressive Approach of an Augmented Steganography for Net...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, B. Pavan Monika, N. Sravani, T.C. Sasi Preetham, C. Varuntej Reddy, V. Kumar Gupta, Manish Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350378092

In order to interface and exchange data with various devices and frameworks via the internet, real objects or things implanted with sensors, programming, and other advancements are referred to as the Internet of Things (IoT). While there are IoT devices [13] that are publicly accessible to everyone in the company, it is still important to understand the risks associated with digital attacks and security threats. Before a message is sent, it is entirely converted from plain to encoded in cryptography. The reverse also applies to plaining messages received following exchanges of letters. One method of hiding sensitive data is to embed it within a text, image, video, or sound file via a technique called steganography. Information can be hidden using bits that address comparable variety pixels that are repeatedly rehashed in an image record. A picture record that seems identical to the original image but contains 'commotion' samples of typical, decoded information will be produced by subtly applying the encoded information to this repeating information. The goal of this project is to jumble up the information on IoT networks by using an image information concealing technique to hide the message inside a picture record. We plan to integrate the use of convolutional neural networks with standard image information hiding techniques in order to significantly increase the amount of data that can be transmitted through an image. We will investigate various convolutional bounds in order to achieve the highest payload. The information will be encrypted and unscrambled using the recently developed profound learning computation. As a result, the convolutional organizations involved in this project will be set up to increase the payload of the data that needs to be encrypted and safely decoded in order to view the initial message. © 2024 IEEE.

关键词： Convolution

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Blockchain Integration with Internet of Things (IoT)-Based Systems for Data Security: A Review

Blockchain Integration with Internet of Things (IoT)-Based S...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Kaur, Gagandeep Shrivastava, Rajesh Gupta, Umesh Department of Computer Science and Engineering Madhav Institute of Technology and Science Gwalior India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789819965434

The blockchain technology offers a secure channel for communicating between entities without the role of any third party. It is a digital ledger of transactions in a computer network that makes it hard for hackers to attack or alter the information. Banking, supply chain, precision agriculture, smart city, cyber-physical systems, industrial IoT, and health care are the various sectors in which blockchain technology has been adopted to enhance security. In recent times, these sectors are being revolutionized by digital transformation using sensor-aided physical devices forming Internet of Things (IoT) systems. Blockchain-based IoT system plays a vital role in replacing the conventional methods of storage and sharing data with a more reliable method. The integration of the two technology results in a secure, reliable, and smart system. This paper exhibits the background and working principle of blockchain technology. Also, it discusses the need of security and security challenges in IoT-based systems. Furthermore, it discusses briefly about smart contracts and motivation behind integrating blockchain technology with IoT-based systems. Finally, it proposes a secure IoT-based land registry architecture. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Improved optimizer with deep learning model for emotion detection and classification

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Mathematical Biosciences and engineering 2024年第7期21卷 6631-6657页

作者： Joseph, Willson C. Kathrine, G. Jaspher Willsie Shanmuganathan, Vimal Sumathi, S. Pelusi, Danilo Valencia, Xiomara Patricia Blanco Verdú, Elena Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Adi Shankara Institute of Engineering and Technology Kerala India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Sri Eshwar College of Engineering Coimbatore India Communication Sciences University of Teramo Coste Sant’agostino Campus Teramo Italy Universidad Internacional de La Rioja La Rioja Logroño Spain

Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods. ©2024 the Author(s), licensee AIMS Press.

关键词： Emotion Recognition

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