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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Sharma, Swati Tomar, Vivek Arora, Sangeeta Gupta, Nishant KIET Group of Institutions Delhi - NCR Computer Science & Engineering Department Ghaziabad India University Computer Science & Engineering Department Dehradun India KIET Group of Institutions Delhi-NCR Computer Applications Department Ghaziabad India SET Department of Computer Science & Engineering Sharda University Greater Noida India

ISBN: (纸本)9798350368109

Floods are the unforeseen situations to the society which causes high risk to the society. For proactive action, it is important to identify locations based on past results. It is necessary to take measures before the situation gets worst. For the benefit of society, a novel approach to map flood risk usinga balanced dataset and an Extra Tree Classifier. The Extra Tree Classifier performs better with the Shapely Additive explanation values. It shows improvement towards decision making processes to reduce the causality at the time of floods. The proposed method achieves the accuracy of 93.61% for the estimating flood danger effects on a particular location. In this paper quantitative study was done to handle the challenges to predict the flood danger using the artificial intelligence ethically. © 2024 IEEE.

关键词： Risk assessment

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Bioacoustics Signal Authentication for E-Medical Records Using Blockchain

Bioacoustics Signal Authentication for E-Medical Records Usi...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Sowjanya, S. Reddy, Induru Sunnihith Muralikrishna, Cheruku Prasad, Thota Sai Lalith Reddy, Pundru Chandra Shaker Sharma, Vikas Vardhaman College of Engineering Computer Science and Engineering Telangana Hyderabad India Rmk Engineering College Computer Science and Business Systems Tamilnadu Tiruvallur India Telangana Hyderabad India Teegala Krishna Reddy Engineering College Computer Science and Engineering Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Iimt University School of Computer Science & Applications Uttar Pradesh Meerut India

ISBN: (纸本)9798350359688

Many are wary of storing and processing data in the cloud because of the prevalence of hostile assaults on mobile and wireless communication networks, which raises serious privacy and security concerns. Using Blockchain as an example, this article investigates the feasibility of developing a trustworthy decentralized authentication system. Utilizing a blockchain technique to enhance the heftiness of multiple data checks and an optimized number of secured features from the bioacoustics signal, in place of traditional biometric features, ensure high security for the bioacoustics signal authentication mechanism. Verified authentication and monitoring of terminal activities are both made possible by it. Then, to provide security at every terminal and edge node, lightweight cryptography (LWC) is created. Lastly, the bastion of the catching approach is the belief-propagation(BP) strategy, which retrains the bioacoustics signal's attributes. The hit ratio is improved and the delay time is decreased. With a blockchain paradigm for data openness, the investigational setup utilizes bioacoustics signals for authentication instead of standard biometric features, and the efficiency of numerous checks is improved. Both security and privacy are enhanced when this occurs. The association between MFCC and LPCC was 0.9517. © 2024 IEEE.

关键词： Electronic health record

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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Detection of Acute Lymphoblastic Leukemia Using a Novel Bone Marrow Image Segmentation

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Tsinghua science and Technology 2025年第2期30卷 610-623页

作者： M.Anline Rejula Ben M Jebin Ravi Selvakumar S.Amutha Eberlein George Department of Computer Applications-PG Scott Christian College(Autonomous)Nagercoil 629003India Department of Computer Science Malankara Catholic CollegeMariagiri 629153India School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Pediatrics Government Medical College and HospitalGovernment of Tamil NaduNamakkal 637001India

In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation techniques,our approach enhances the dual threshold segmentation process,optimizing the isolation of nucleus and cytoplasm *** is achieved by adapting threshold values based on image characteristics,resulting in superior segmentation outcomes compared to previous *** address challenges,such as noise and incomplete white blood cells,we employ mathematical morphology and median filtering *** methods effectively denoise the images and remove incomplete cells,leading to cleaner and more precise ***,we propose a unique feature extraction method using a hybrid discrete wavelet transform,capturing both spatial and frequency *** allows for the extraction of highly discriminative features from segmented images,enhancing the reliability of *** classification purposes,we utilize an improved Adaptive Neuro-Fuzzy Inference System(ANFIS)that leverages the extracted *** enhanced classification algorithm surpasses traditional methods,ensuring accurate identification of acute lymphoblastic *** innovation lies in the comprehensive integration of segmentation techniques,advanced denoising methods,novel feature extraction,and improved *** extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase(ALL-IDB)for Image Processing database using MATLAB 10.0,our method demonstrates outstanding classification *** segmentation accuracy for various cell types,including Band cells(96%),Metamyelocyte(99%),Myeloblast(96%),***(97%),***(97%),and Neutrophil cells(98%),further underscores the potential of our approach as a high-quality tool for ALL diagnosis.

关键词： Adaptive Neuro-Fuzzy Inference System(ANFIS) blood cells segmentation leukemia classification

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Data Mining Approach for Early Detection of Anemia Using Predictive Apriori Algorithm and Boolean Matrix Analysis 3rd

Data Mining Approach for Early Detection of Anemia Using Pre...

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Bhargava, Ritu Dixit, Pooja Rathore, Pramod Singh Goswami, Ankur Ajmer India Department of Computer Science Dezyne College Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Governmentt Engineering College Bikaner India

ISBN: (纸本)9789819751457

Paper deals with the predictive apriori algorithm for a disease related to hemoglobin blood test data. The dataset has been collected from public dataset. The dataset was pre-processed to remove missing and unwanted data. In this paper, Boolean matrix is defined for eliminating the candidate from the subset, which is not frequent. In the Boolean matrix, frequent patterns are generated using "AND" operation. Since the anemia is a common health problem which affects mostly women worldwide. The approach is used in this paper for detecting the anemia disease from the result investigation. Predictive apriori algorithm and Boolean matrix give the better results. This paper is divided into four sections. Section one describes introduction of anemia and apriori algorithm. Section two deals with literature review. Section three describes the prediction algorithm and results, and lastly, section four discusses the conclusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Segmentation of Water Bodies from Satellite Images Using Spatial-Channel Attention UNet

Segmentation of Water Bodies from Satellite Images Using Spa...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Anitha, M. Chitra, P. Meenaragavi, P. Thiagarajar College of Engineering Department of Computer Applications Madurai625 015 India Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350378177

Precise division of aquatic areas is essential for efficient environmental supervision and administration. It enables the precise delineation and tracking of aquatic ecosystems, facilitating the assessment of water quality and the identification of environmental hazards such as pollution and habitat degradation. By offering high-resolution data and enabling thorough coverage of large areas, advanced technologies like satellite images play a crucial role in improving water body segmentation and expanding the capacity to monitor and manage water resources effectively and sustainably. In this study, the focus is on leveraging Sentinel-2 satellite imagery for accurate water body segmentation. The dataset is meticulously curated and comprises Sentinel-2 satellite images. Masks are then created to isolate water bodies from the original images using advanced deep-learning architectures, including UNet, Deeplabs, and SegNet. Furthermore, the performance of UNet is enhanced by incorporating attention mechanisms such as channel attention, spatial attention, and combined spatial and channel attention. These algorithms are used to generate output masks, which are then compared with the existing masks from the dataset. Subsequently, the evaluation was performed using metrics such as accuracy, binary cross- entropy loss, and mean intersection over union, providing comprehensive insights into their efficacy. Notably, the findings highlight the remarkable accuracy achieved by the UNet model enhanced with spatial and channel attention mechanisms, reaching an impressive 96.7%. This notable progress emphasizes the possibility of achieving better accuracy in segmenting water, which in turn aids real-time monitoring and informed decision-making in environmental management. © 2024 IEEE.

关键词： Decision making

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Energy-efficiency Analysis of Different Scheduling Algorithms for Mobile Crowd Computing 15

Energy-efficiency Analysis of Different Scheduling Algorithm...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pramanik, Pijush Kanti Dutta Biswas, Tarun Galgotias University School of Computer Applications and Technology Greater Noida India Indian Institute of Information Technology Department of Computer Science & Engineering Ranchi India

ISBN: (纸本)9798350370249

Mobile crowd computing (MCC) has emerged as a promising paradigm to leverage the underutilised computational resources of smart mobile devices (SMDs). However, the energy constraints of these devices pose a significant challenge in efficiently executing computational tasks. This study analyses the energy efficiency of various scheduling algorithms for MCC environments. The study compares the performance of seven scheduling algorithms: PPIA, RAS, PSO, GA, MaxMin, MinMin, and MCT. These algorithms were evaluated in terms of their energy efficiency when executing different sets of tasks with varied instruction lengths. The experiment used a simulated MCC environment with real data from five SMDs. The results demonstrate that the PSO algorithm consistently outperformed the other scheduling schemes regarding energy efficiency across all task sets. The GA algorithm also showed promising results. The MinMin algorithm exhibited the lowest energy efficiency. The findings of this study provide valuable insights for designing and implementing energy-efficient task scheduling strategies in MCC systems. Analysing the different algorithms under varying task loads can help MCC service providers make informed decisions when selecting appropriate scheduling techniques to optimise the energy utilisation of SMDs. © 2024 IEEE.

关键词： Crowd computing Energy-efficiency Mobile cloud Particle swarm optimisation Task scheduling

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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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Performance Analysis of Deep Learning Pretrained Image Classifiation Models 1

Performance Analysis of Deep Learning Pretrained Image Class...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Deepa Alli, A. Sheetal Prasad Km, Shiva Christ University Department of Computer Science Bengaluru India Presidency College Department of Computer Applications Bengaluru India Presidency College Department of Computer Science and Engineering Bengaluru India Rao Bahadur y Mahabaleswarappa Engineering College Department of Computer Applications Karnataka Ballari India

ISBN: (纸本)9798350316469

Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. © 2023 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deep Learning Image classification

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Dragonfly Algorithm and Variants for Feature Selection: A Review

Dragonfly Algorithm and Variants for Feature Selection: A Re...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Prinson, P.T. Geetha, Angelina Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314946

Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions © 2023 IEEE.

关键词： Swarm intelligence

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