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4th International Conference on Electrical, computer, communications and Mechatronics engineering, ICECCME 2024

作者： Kyselak, Martin Vavra, Jiri Vylezich, Zdenek Grenar, David Slavicek, Karel University of Defense Faculty of Military Technology Department of Electrical Engineering Brno Czech Republic University of Technology Faculty of Electrical Eng. and Communication Dept. of Telecommunications Brno Czech Republic Institute of Computer Science Masaryk University IT Infrastructure Division Brno Czech Republic

ISBN: (纸本)9798350391183

Optical fibre sensors are currently used to detect many non-electrical phenomena. The latest sensors analyze the polarization properties of light inside an optical fiber. Unlike interferometric methods, this method uses the interference of two planes of polarization at the output of the optical fibre. The sensors are divided into linear polarizer sensors or multi-fibre interference sensors based on different ways of converting the polarization state into a change in the intensity of the optical radiation. These sensors are particularly suitable for low frequency vibration measurements due to the transformation of beats into the longitudinal deformation of the optical fibre. The aim of the presented measurements was to demonstrate that these polarization vibration sensors have excellent sensitivity and are thus suitable for vibration detection. This paper demonstrates that polarization sensors have excellent sensitivity, are convenient and inexpensive to install, and, due to their non-electric nature, are suitable for hazardous environments. In addition, it is possible to employ these sensors as intrusion detection and prevention systems and remotely power them at a distance of tens of kilometers. The motivation for this research was the lack of information and equipment for vibration testing of optical fibres and the associated polarization changes. © 2024 IEEE.

关键词： Tensile testing

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Augmenting Content Retrieval Through Machine Learning 3

Augmenting Content Retrieval Through Machine Learning

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Pavan, P.S. Sripriya, T. Vikas, B. Shivank Parmar, Yuvraj Acharjya, Kalyan Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

gadget getting to know is a subset of synthetic intelligence that focuses on making predictions via reading and getting to know patterns from records. In current years, machine studying has been increasingly more carried out to numerous fields consisting of natural language processing, laptop vision, and records analytics. in the field of content material retrieval, system gaining knowledge of strategies are used to enhance the effectiveness and efficiency of search algorithms, making it less complicated for customers to discover the information they ***, content retrieval structures depend on indexing and key-word-based totally strategies to retrieve applicable information. even as these strategies have been powerful, they have barriers in dealing with complicated queries and information the context of the facts being searched for. this is where machine learning is available in, providing the functionality to research and learn from big amounts of facts to enhance the accuracy and relevance of search consequences. through the usage of machine getting to know, content retrieval systems can better recognize the purpose at the back of a person's query and generate more correct outcomes. that is accomplished through strategies such as natural language processing, which allows the system to recognize and extract which means from text and speech. moreover, gadget gaining knowledge of can be carried out to study from past person interactions and feedback to continuously enhance the search enjoy. © 2023 IEEE.

关键词： Machine learning

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Optimizing Large-Scale Data Processing with Artificial Intelligence 3

Optimizing Large-Scale Data Processing with Artificial Intel...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Kumar, Ritesh Beemkumar, N. Shukla, Anupam Nirmala, D. Meshram, Vidula V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

massive-scale statistics processing presents precise demanding situations in extracting insights, as the sheer amount of records can be overwhelming for classic analytics systems. Artificial Intelligence (AI) can offer a powerful answer for coping with large-scale records units, because it leverages system mastering algorithms to construct sophisticated fashions and automate complex evaluation. AI algorithms are also adaptive and incredibly green, which reduces the time it takes to mine valuable insights from big information units. This paper will study the diverse approaches that AI can enhance large-scale information processing and explore the numerous advantages of deploying AI in statistics processing workflows. The dialogue will include the advantages of leveraging AI for both supervised and unsupervised getting to know tasks in addition to the technical concerns around deciding on the proper AI technology for information processing jobs. Subsequently, the paper will present actual- world applications of AI in huge-scale records processing and evaluate the ability for groups to gain an aggressive aspect by deploying AI of their information pipelines. © 2023 IEEE.

关键词： Aggressive Applications Information Numerous Present

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Developing a Framework for Utilizing AI for Data Access Optimization 3

Developing a Framework for Utilizing AI for Data Access Opti...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Bogiri, Nagaraju Sinha, Sanjay Kumar Kaur, Ashmeet Singh, Vaishali Bahri, Dimple Raajini, X.Mercilin Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

The use of artificial intelligence for data access optimization is becoming increasingly important in the field of data science. AI algorithms can be used to create complex data structures that can be used to facilitate faster and more efficient access to data. Additionally, AI can be used to create frameworks capable of handling large amounts of data and optimizing the speed and accuracy of query results. This paper presents a framework for utilizing AI for data access optimization. In this framework, AI algorithms are used to create a structure for data storage and user interfaces that allow users to interact with the data more efficiently. Data mining techniques are used to identify patterns in the data that can be used to generate optimized queries. Additionally, the framework includes tools and libraries to automate processes and enable easy implementation of the developed framework. The proposed framework has been evaluated using a case study, and results show that the AI-based query optimization proposed in this framework can significantly reduce the query result time compared to traditional methods. © 2023 IEEE.

关键词： compare framework optimization techniques traditional

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Investigating the Use of Data Mining for Knowledge Discovery 3

Investigating the Use of Data Mining for Knowledge Discovery

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Sunil, M.P. Pandey, Megha Garima Janakiraman, V. Maral, Vikas B. Das, Sidhant Department of Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

Fact mining has emerged as one of the most promising information discovery strategies in tremendous and complex databases. The tourism quarter advantages highly from records mining, as it can provide specific insights into how customers view and transact with the industry. In recent years, fact mining has grown exponentially within the tourism region, making it vital for researchers to apprehend how it can be implemented to discover meaningful information and inform strategic choice-making. This paper examines the potential of fact mining for know-how discovery in tourism, along with supervised and unsupervised learning techniques, artificial intelligence, predictive modeling, and information visualization. The authors talk about the results of technological improvements in information mining and the ability of tourism organizations to benefit from its use. They then speak of realistic examples of statistics mining programs inside the tourism area, from tracking client reviews to forecasting vacationer trends. An assessment of the prevailing literature is presented to assess knowledge discovery strategies inside the field of tourism. Ultimately, the authors recommend avenues for destiny studies in this location. © 2023 IEEE.

关键词： conclusion forecasting knowledge presented technology

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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Analyzing the Potential of Deep Learning for Enhancing Data science Practices 3

Analyzing the Potential of Deep Learning for Enhancing Data ...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Mercilin Raajini, X. Deshmukh, Gopal B. Prabha, Savita Singh, Jagtej Agarwal, Ankita Manu, S.E. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

Deep learning is a progressive new field of artificial intelligence that has drastically changed the landscape of technology over the last few years. It has enabled the creation of clever packages, including self-using motors, facial recognition, natural language processing, and others. It has also affected information science, with deep getting to know presenting a new set of tools to system and examine information. This paper discusses the capacity of deep gaining knowledge for enhancing records technological know-how practices, consisting of predictive analytics, visible insights, and self-studying structures. For each subject, the paper provides a short evaluation accompanied by an evaluation and discussion. Subsequently, it provides an outline of cutting-edge developments and guidelines for how records scientists can OK use their abilities and revel to capitalize on the capacity of deep learning. © 2023 IEEE.

关键词： capitalize discussion learning recommendations scientists

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Privacy-Secure and Decentralized Biometric Authentication Models Using Federated Learning Frameworks

Privacy-Secure and Decentralized Biometric Authentication Mo...

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IEEE International Conference on system, Computation, Automation and Networking (ICSCAN)

作者： Mathivanan. P K. Mahalakshmi Faculty of Information and Communication Engineering Anna university Chennai Department of Computer Science and Engineering KIT - Kalaignarkarunanidhi Institute of Technology Coimbatore India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

A biometric identifier, in contrast to other identifiers used for authentication, is a quantitative evaluation of an individual's physical attributes that is successfully used to confirm or validate the identification. Because of its remarkable and consistent texture variation, iris recognition is thought to be the most dependable biometric recognition. Concerns about security, authentication, and identification are growing in importance across all domains as technology advances daily. High-security applications use these unique patterns for iris recognition. To increase the security of biometric applications without jeopardizing individual privacy, this research suggests a novel FL technique for privacy-preserving iris detection. Traditional biometric systems rely on centralised data storage and processing, which threatens data security and misuse. Federated Learning: decentralized model training that allows multiple federated clients (like mobile devices or edge servers) to collaboratively train a global model without sharing their raw iris data. We explore how this architecture can be applied to iris detection systems, maintaining accuracy while mitigating privacy risks. Our method ensures that iris features are learned locally on individual devices, and only encrypted model updates are shared with the central server. To strengthen privacy, we incorporate differential privacy and secure aggregation techniques to prevent data leakage during model training. Experimental results demonstrate the effectiveness of the proposed framework in maintaining high detection accuracy, low communication overhead, and robust privacy guarantees.

关键词： Training Privacy Differential privacy Accuracy Federated learning Biological system modeling Data models Object recognition Servers Iris recognition

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Speech Emotion Recognition Using yet Another Mobile Network Tool

Speech Emotion Recognition Using yet Another Mobile Network ...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Oyediran, Mayowa Oyedepo Ojo, Olufemi Samuel Bharany, Salil Adeniyi, Abidemi Emmanuel Imoize, Agbotiname Lucky Farhaoui, Yousef Awotunde, Joseph Bamidele Department of Computer Science Ajayi Crowther University Oyo Oyo Nigeria CET Department GNDU Amritsar India Department of Computer Sciences Precious Cornerstone University Ibadan Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria STI Laboratory T-IDMS Faculty of Sciences and Techniques Moulay Ismail University of Meknès Errachidia Morocco Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria

ISBN: (纸本)9783031262531

Recognizing and reacting to the emotions of others is a key necessity for successful human engagement since emotions are such an important and vital component of being human. Speaking is the most intuitive method by which individuals ex-press themselves. It follows that this communication medium should be extended to computer programs. Affective computing focuses on the automated perception, understanding, and synthesizing of emotional responses, consequently, speech emotion recognition seeks to recognize a speaker's core emotional state derived from the voice signal. The paralinguistic information conveyed by speech expressions has been identified to be beneficial in speech processing in a variety of ways, most notably as a significant component of computer emotional intelligence and contributing to Human – computer Interaction. In this regard, this study uses a spoken emotion recognition technology to deliver "Yet Another Mobile Net-work Tool" where 1295 primary dataset acquired from Bells University of Technology. The dataset consists of five emotions (anger, fear, neutral, happy, and sad) that were used for the experiment. The experimental results show that the study obtained a recognition accuracy of 85.5% and a rejection rate of 10.5%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human computer interaction

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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computer systems science & engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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