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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Machine Learning for Recognition of Handwritten Devanagri Character 1

Machine Learning for Recognition of Handwritten Devanagri Ch...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tyagi, Shilpa Dutta, Chiranjit Singh, Manu SRM Institute of Science and Technology Department of Computer Applications Uttar Pradesh Ghaziabad201204 India SRM Institute of Science and Technology Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201204 India ABES Engineering College Department of Computer Science Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350353648

A framework known as optical character recognition is capable of converting handwritten or printed pictures into machine-editable structures. Several Indian dialects, including Hindi, Nepali, Marathi, Sindhi, and others, use the Devanagari script. In India, Hindi is the national language and the language that is spoken by most people. This script organises the development of the language. Nowadays, people are interested in gathering data in sophisticated configurations that are available in paper archives and then employing a search process to reuse this data later. In this study, we provide a novel approach to Devanagari script character recognition for printed Hindi characters. The principal objective of this research is to identify the consonants and the vowels, which can subsequently be used for recognizing the complicated inferred words. The primary focus of this project is on the identification of each individual consonant and vowel. Several pre-processing techniques, including feature extraction, segmentation, and classification, have been researched and used in this project to create an advanced Hindi OCR system. The K-NN classification technique was used in earlier research;however, in the work that is being suggested, we have employed a hybrid technique that combines neural networks with K-NN. The recognition rate of the suggested approach is 97.4%, whereas that of the existing techniques is 94.5%. This suggests that the approach which proposed here is superior to techniques employed in the method which already exist. © 2024 IEEE.

关键词： Support vector machines

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Utilization of hesitant and intuitionistic fuzzy sets (HFS-IFS) in computational intelligence for decision modeling

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International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Sharma, Suvarna Dangi, Dharmendra Dixit, Dheeraj Kumar Gupta, Rashmi Kumar, Jeetendra Bhagat, Amit Department of Computer Science and Applications Dr. Vishwanath Karad MITWorld Peace University Pune India Information Technology Indian Institute of Information Technology (IIIT) C/O- Maulana Azad National Institute of Technology (MANIT) Bhopal India Computer Science and Engineering Madhav Institute of Technology & Science (MITS) Madhya Pradesh Gwalior India Computer Science and Application Atal Bihari Vajpayee University Chhattisgarh Bilaspur India Computer Applications Maulana Azad National Institute of Technology Bhopal India

This paper focuses on the utilization of hesitant and intuitionistic fuzzy sets (HFS & IFS) in a computational intelligent approach, mainly for decision modelling under complex vague surroundings. Indeed, classical fuzzy sets can be powerful in some applications, but they may not describe the broad range of epistemic imprecision and non-stationary uncertainty involved in making decisions at any given time. HFS & IFS overcome it by providing the ability to quantify degrees of hesitation and membership uncertainty. HFS allows a decision maker to express the multiple reference degrees associated with an option and also show uncertainty about how to assign that precise level of degree or real value. On the other hand, IFS allows for more possibilities of representing ambiguity. In this research, we incorporate the proposed model into a computational intelligent approach for improving decision-making, particularly in the context of multi-criteria decision analysis (MCDA), resource allocation and task completion parameters. The results show that the proposed HFS-IFS method presents improved performance in terms of accuracy and uncertainty treatment across a series of case studies. © Bharati Vidyapeeth's institute of computer applications and Management 2025.

关键词： Computational Intelligence Decision modeling Hesitant fuzzy set (HFS) Intuitionistic fuzzy set (IFS) MCDA

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Patterns of Widespread Structural Abnormalities in Autism Spectrum Disorder using Diffusion Tensor Imaging

Patterns of Widespread Structural Abnormalities in Autism Sp...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Bharti, Dilip Kumar Singh, Sanjay Kumar Kumar, Mukesh Kanaujia, Vishal Kumar Aeri, Manisha Abes Engineering College Computer Science And Engineering-Data Science Ghaziabad India Kashi Institute Of Technology Department Of Computer Science & Applications Mirzamurad U.P Varanasi India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun248002 India Abes Engineering College Computer Science And Engineering Data Science Department Ghaziabad India

ISBN: (纸本)9798350366846

Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an essential tool for investigating the underlying neurobiological mechanisms of autism spectrum sickness (ASD), a neurodevelopmental disorder characterized via problems in social communication and repetitive behaviors. Through the usage of DTI, researchers have identified good sized styles of structural abnormalities in the brains of individuals with ASD. these abnormalities encompass changes in the microstructure of white remember fibers, along with decreased fiber density, changes in fiber directionality, and decreased connectivity between mind areas. Additionally, there may be evidence of bizarre mind development in early formative years, with a few research finding that these structural variations are present even in very younger children with ASD. One constant finding in DTI studies of ASD is reduced white rely integrity inside the corpus callosum, a large fiber package deal that connects the two hemispheres of the mind. This abnormality has been connected to deficits in social communiqué and language in people with ASD, as well as difficulties in integrating records from distinctive brain areas. The results showed extensive structural abnormalities in people with ASD in comparison to TD individuals. © 2024 IEEE.

关键词： Diffusion tensor imaging

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Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware Strategies in Wireless Sensor Networks

Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Priya, Naincy Sinha, Priyanka Singh, Pranita Jain, Arpit Kumar, Munish GLA University Department of Computer Engineering & Applications U.P. Mathura India Muzaffarpur Institute of Technology Department of Computer Science & Engineering Bihar Muzaffarpur India Government Engineering College Khagaria Department of Computer Science & Engineering India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram AP India

ISBN: (纸本)9798350394412

In today's surplus world, wi-fi sensor networks are essential for many systems. This network encapsulates sensor nodes powered by irreplaceable batteries, preserving a fixed topology for displaying specific locations. Their efficiency makes it crucial for large data packages. Wireless sensor networks continuously analyze physical factors such as temperature, pressure, ambient soft, sunlight and humidity, dominating the process Energy consumption monitoring is a key task in this area. To address this framework, this paper proposes a different approach: a modified controlled, privacy-controlled approach to record collection. The method aims to reduce redundant sensitivity, thus increasing system lifetime by reducing the frequency of data transfer between nodes and large sinks A comparative study with existing methods shows how the proposed method is effective. Performance findings show that the proposed bundling method (MEBPP) provides high packet transmission values while consuming less power and extending network lifetime. © 2024 IEEE.

关键词： Data aggregation

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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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Mitigating Denial Based Service Attacks in Heterogeneous Sensor Networks: Strategies and Solutions 11

Mitigating Denial Based Service Attacks in Heterogeneous Sen...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Desai, Jaynesh H Bagle, Neha S Rami, Khyati Verma, Shanti Bhagwan Mahavir University Department of Computer Applications Gujarat Surat India Sal Institute of Technology & Engineering Research Department of Computer Science Gujarat India L J University Department of Computer Applications Gujarat Ahmedabad India

ISBN: (纸本)9798350350357

With the development of heterogeneous sensory networks in various applications of domains for ensuring the security against denial based service (DoS) attacks will become a paramount. In this research study, a proposal of a novel system named SecureSenseGuard(SSG) for effectively detecting DoS attacks in heterogeneous sensor networks. SecureSenseGuard(SSG) leveraging the Decision Tree (DT) algorithm augmented with Recursive type Features Elimination (RFE) for achievinga robust and efficient attack detection. The proposed system may operate by first Collection of data from diverged sensors within the network. The data, representing various network attributes, are then preprocessed and fed into the DT algorithm for classifications process. RFE will be employed as a feature selection techniques to enhance the performance of DT algorithm by iterative process for removing less relevant features, thus improving in detection accuracy and reducing computational overhead. The proposed system SecureSenseGuard(SSG) will offer several advantages over existing approaches. By employing DT, it will provide interpretable and transparent decision rules for facilitating understanding and trust in the detection process. Moreover, the integration of RFE will enhance the system's scalability and adaptability for dynamic network environments. To evaluate the effectiveness of SecureSenseGuard(SSG) an extensive experiment may be conducted by using various ranges of datasets and synthetic attack scenarios. The Results demonstrate that SecureSenseGuard(SSG) achieved high detection rates. while maintaining a low false alarm rate, outperforming traditional approaches. Overall, SecureSenseGuard(SSG) may present a promising solution in mitigating DoS attacks in heterogeneous sensory networks by offering a balance between detection accuracy, interpretability, and computational efficiency. © 2024 IEEE.

关键词： Heterogeneous networks

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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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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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