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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 710-724页

作者： Dalvi, Prachi Kalbande, Dhananjay R. Rathod, Surendra Singh Dalvi, Harshal Agarwal, Amey Bharatiya Vidya Bhavans Sardar Patel Institute of Technology Department of Computer Engineering Mumbai400058 India Fr. Conceicao Rodrigues College of Engineering Department of Electronics and Computer Science Mumbai400050 India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai400056 India

Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

Today's marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women's clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation. © 2023 IEEE.

关键词： Long short-term memory

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A hybrid convolutional neural network and support vector machine classifier for Amharic character recognition

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Neural Computing and Applications 2024年第27期36卷 16839-16856页

作者： Tsegaye, Muluken Zemed Shashi, Mogalla Department of Information Technology Andhra University College of Engineering AP Visakhapatnam530003 India Department of Computer Science Andhra University College of Engineering AP Visakhapatnam530003 India

Optical character recognition is a way of converting scanned images of printed or handwritten documents into machine-encoded text, making it easier to store, browse, retrieve, and process electronic data. In this research, a Printed Amharic Characters Recognition dataset is prepared to train and test a model. Images in the dataset only contain 231 basic Amharic characters that are normalized to 32 × 32 pixels. In this work, a hybrid model of the two super classifiers is developed: the convolutional neural network (CNN) and the support vector machine (SVM). In this novel hybrid CNN-SVM model, CNN works as an automatic feature extractor from the raw images, and then, the extracted feature vectors are given as input to SVM for classification and recognition. A 99.84% accuracy was achieved on the own-prepared dataset and 95.59% accuracy on the benchmark Amharic Optical Character Recognition Database in classifying the testing dataset images. The proposed hybrid CNN-SVM model gave better results than the CNN with a fully connected layer. Moreover, the proposed model outperforms previously existing works attempted by others to recognize printed Amharic characters on the same and different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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Interpreting Doctors' Notes Using Handwriting Recognition and Deep Learning Techniques 9

Interpreting Doctors' Notes Using Handwriting Recognition an...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Maheswari, G. Ramya, R. Babitha, S. Sriram, R. Jennis, M. Ram Prasad, P. Hindustan Institute of Technology And Science Department of Information Technology Chennai India Psna College of Engineering And Technology Department of Artificial Intelligence And Data Science India

ISBN: (纸本)9798350365092

This research provides a deep learning approach and handwriting recognition to understand medical notes. The system processes and interprets textual medical notes using connectionist temporal classification (CTC) and optical character recognition (OCR) techniques. The written text is transcribed using the OCR component, and then the CTC algorithm is utilized to analyze the text and give significance to particular note portions. The suggested system's ability to correctly understand and arrange written medical information is demonstrated by training it on prescription notes from doctors taken from a dataset. According to the final forecast, the system can read medical notes with high accuracy, making medical professionals' usage and accessibility of these records easier. The suggested paradigm offers fast and precise access to patient information, which could increase the efficacy and efficiency of medical care. © 2024 IEEE.

关键词： Optical character recognition

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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Leveraging Machine Learning Algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness Data 5

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Clarinda, K. Deepa, N. Dhamotharan, R. Krishnan, R. Santhana Raj, J. Relin Fransis Anchitaalagammai, J.V. SCAD College of Engineering and Technology Department of Management Studies Cheranmahadevi India PSNA College of Engineering and Technology Department of Artificial Intelligence and Data Science Dindigul India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331530013

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating Pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of Artificial Neural Networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

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computers, Materials & Continua 2024年第6期79卷 4127-4148页

作者： Abdulelah Alwabel Chinmaya Kumar Swain Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Institute of Management and Information TechnologyCuttackBPUTOdishaIndia

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud *** approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing *** approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of *** this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing *** consider four cost-types for application deployment:Computation,communication,energy consumption,and *** proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the *** extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art *** results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

关键词： Placement mechanism application module placement fog computing cloud computing genetic algorithm flamingo search algorithm

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Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 2353-2366页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Hyperspectral(HS)image classiﬁcation is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classiﬁcation ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classiﬁcation(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identiﬁcation and classiﬁcation of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.

关键词： Hyperspectral images image classiﬁcation deep learning adagrad optimizer nasnetlarge model red deer algorithm

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

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