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Hybrid System with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： K. Thinakaran M. Anand T.Rajesh Kumar Saira Banu Atham K.M. Subramanian Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering School of CSE & IS Presidency University Bangaluru Department of Computer Science Engineering Shadan College of Engineering and Technology Hyderabad

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error.

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Dynamic Lane Management with IoT for Real-time Lane Configuration and Traffic Flow 2

Dynamic Lane Management with IoT for Real-time Lane Configur...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Pasha, Mohammed Rehaman Latha, S. Usha, R. Marin, V. Bini Ishwarya, M.V. Muthulekshmi, M. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Department of Department of Biotechnology Tamil Nadu Salem India S. A. Engineering College Department of Mathematics Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Ramapuram Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Traffic may be reduced and transportation efficiency improved by properly controlling traffic flow in urban areas. This research provides a new method for managing lanes in real-time by using IoT connectivity. To optimize traffic flow in response to shifting demand patterns, the suggested system prioritizes making lane design changes in real time depending on the current traffic conditions. Using information collected from Internet of Things (IoT) sensors and linked automobiles, the system may dynamically allot lanes in response to variations in traffic volumes and congestion. It describes developing and deploying a dynamic lane control system based on the Internet of Things, stressing the technology's potential to improve traffic flow in congested metropolitan areas. The simulation results show its efficiency in decreasing traffic and increasing throughput. It demonstrates the potential of intelligent transportation systems for creating smarter and more efficient cities by combining IoT, real-time data processing, and traffic control. © 2024 IEEE.

关键词： Internet of things

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AI based Stock Market Analysis and Decision Making System using Design Thinking Approach 8

AI based Stock Market Analysis and Decision Making System us...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kumar, M.Venkatesh Umamaheswari, M. Bharathi, C. Hema Maruthaveni, R. Devi, M.Nirmala Prasanna, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Dr. Sns Rajalakshmi College of Arts and Science Department of Business Administration Tamilnadu Coimbatore India Tamil Nadu Agricultural University Department of Physical Science and Information Technology Tamilnadu Coimbatore India Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science with Data Analytics Tamilnadu Coimbatore India Dr. Sns Rajalakshmi College of Arts and Science Department of Management Studies Tamilnadu Coimbatore India

ISBN: (纸本)9798350386578

Stock market is a dynamic and ever-changing environment that can be both exciting and challenging for investors. Equities, primarily referred to as stocks, are traded on stock exchanges around the world and reflect ownership in a business. The stock market is an essential tool for companies to raise capital, and investors use it to grow their wealth. Investing in stocks requires a thorough understanding of the market and the individual companies in which you are interested. The financial health of a company must be taken into account and can be analyzed using financial statements and other data. Another critical factor is the overall economic climate, as market conditions can significantly impact stock prices. It's crucial to have a long-term outlook and avoid being influenced by momentary market changes when making equity investments. A random walk is a mathematical model used to describe a sequence of steps or movements where each step is determined by chance. It is often used in various fields, including physics, finance, and statistics. Random walk theory has important implications in various fields, including the efficient market hypothesis in finance and the modeling of diffusion processes in physics. To effectively predict the future stock price using conventional and modern machine learning models. Compare the performance of different models and identify a higher accuracy prediction model using the selected important factors from disparate data sources. © 2024 IEEE.

关键词： Decentralized finance

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Real-Time Speech Emotion Recognition Using Hybrid Transformer networks for Mental Health Monitoring

Real-Time Speech Emotion Recognition Using Hybrid Transforme...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Durga, P. Janani Basha, C. H. Hussaian Christopher, Nelson Kennedy Babu Senthilkumar, S. Kumar, N. Rathina Kavitha, M. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Sr University Department of Electrical and Electronics Engineering Telangana Hanumakonda50637 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore641107 India

ISBN: (纸本)9798331509828

This study proposes a hybrid method to multi dialect speech recognition using the transfer learning with transformer-based architecture. The primary objective of this method is to enhance the automatic speech recognition (ASR) across the different dialectal variations of specific language. The transfer learning methods are utilized to modify the base model that was trained on high resource language to manage the multiple dialects with limited labelled data for each dialect. This is achieved by using pre trained transformer methods such as Wav2Vec2.0 and Whisper. An approach which employs dialect specific embeddings is presented. It enables the method to capture data and differentiate between prosodic elements and phonetic that are distinctive to a variety of dialects. data augmentation technique are utilized in order to overcome the limited availability of labelled data for low resource dialects. These methods are utilized to increase the efficiency of training set and enhance the robustness of the method. Evaluation of the method is carried out using multi dialect speech datasets which include Arabic MGB-3 and common voice. These datasets represent a variety of dialects that are spoken in various linguistic regions. The result of this study indicates that the transformer-based method is effective in managing difficulties of dialectal variances while the reliability is reduced on different datasets. Different real-world applications are provided by the proposed method for improving voice enabled applications. Furthermore, this method leads the way for the development of ASR method that are more accurate and independent in locations that have a significant linguistic variation. © 2025 IEEE.

关键词： Transfer learning

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Strengthening Authentication: The Role of Multi-Level Hashing in OSPA Protocol

Strengthening Authentication: The Role of Multi-Level Hashin...

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IoT Based Control networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Hariharasudhan Vineetha Varghese K Malarvizhi S.P. Santhoshkumar T. Nalini D. Vikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India Department of Computer Science al Business Systems Jct College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of computer science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Artificial Intelligence Data Science Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture.

关键词： Hash functions Resistance Protocols Heuristic algorithms Authentication Passwords Universal Serial Bus computer crime Intelligent systems Eavesdropping

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AI-Driven Intelligent IoT Systems for Real-Time Food Quality Monitoring and Analysis

AI-Driven Intelligent IoT Systems for Real-Time Food Quality...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Prabhakar Krishnan U. Samson Ebenezar R. Ranitha N. Purushotham T. Suresh Balakrishnan Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Mohan Babu University AP India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The system will monitor and analyse food quality along the supply chain in real time. The system has high performance due to the CNN algorithms. In terms of accuracy, CNN has a high performance (95.2%), precision (92.8%), recall (94.5%) and F1 score (93.6 %). These features enable the model to predict food quality under good and poor conditions, thus demonstrating its effectiveness in assessing quality. The accuracy of real-time monitoring was evaluated across the supply chain to prove system reliability. Accuracy ratings for transport, storage and processing were 96.3%, 94.7% and 92.1%. These results show the robustness of this system within dynamic supply chain environments. Further testing evaluated environmental resilience capabilities. Under normal conditions, the accuracy of this system was 95.2%, while it reduced to 92.6% and rises again up to a level of 94.3%. The adaptability of the system under harsh conditions demonstrates that it can be applied to ensure food quality in various cases. This study reveals how proposed AI-based intelligent Internet of Things system may be used for real application using an appropriate dataset selected properly. This approach can help improve the quality assurance of food in an industry that is complex by uniting advanced algorithms with real-time data monitoring.

关键词： Temperature sensors Heuristic algorithms Supply chains Transportation Prediction algorithms Real-time systems Internet of Things

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Detection of Indian Regional Sign Language Through Convolutional Neural Network

Detection of Indian Regional Sign Language Through Convoluti...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Mohit Baroliya Himanshu Srivastava Aishwarya Iyer Jayanthi M. G Prashanth Kannadaguli Computer Science and Technology Cambridge Institute of Technology Bengaluru India Department of CSE Bengaluru India Senior Data Science Trainer Dhaarini Academy of Technical Education Bengaluru India

The objective of this endeavor is to create a specialized sign language recognition system created specifically for Kannada, an indigenous language spoken in India. While there are existing recognition systems for widely spoken languages like Hindi and English, there is a lack of resources and options for regional languages like Kannada. By leveraging a dataset of 50,000 Kannada sign language samples, The objective of this initiative is to close the communication divide experienced by individuals who rely on Kannada sign language, effectively addressing the need for improved interaction. The project focuses on exploring and comparing various AI and DL models to identify the most effective approach for Kannada sign language recognition. The ultimate goal is to enhance accessibility, inclusivity, and understanding for individuals communicating in Kannada sign language, thereby promoting cultural diversity and equal opportunities for the Kannada-speaking sign language community in India. The results and consequences of this endeavor have the potential to lay the groundwork for the advancement of location-specific sign language recognition systems. This would significantly enhance communication and promote the self-determination of individuals with hearing impairments residing in the Kannada-speaking areas of India.

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Cocoon morphological Features Based Silk Quality Prediction Using XG Boost Algorithm

Cocoon morphological Features Based Silk Quality Prediction ...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： M. Nivetha I. Sudha Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Silk quality is a critical determinant in the silk industry, significantly influencing the market value of silk products. This study proposes an innovative method for forecasting silk quality by leveraging cocoon morphological features through the application of the XG Boost algorithm. The morphological characteristics of the cocoon, including dimensions, shape, and colour, are identified as key indicators of silk quality. The XG Boost algorithm, known for its effectiveness in handling complex datasets, is employed for building a predictive model. A comprehensive dataset is assembled, encompassing a diverse set of cocoon morphological features obtained from a representative sample of silkworms. These features comprise cocoon size, length, width, shape parameters, and colour attributes. The dataset is then utilized to train and validate the XG Boost model, fine-tuning its parameters to ensure accurate prediction of silk quality. The outcomes of this study showcase the effectiveness of the proposed approach, demonstrating high predictive accuracy in determining silk quality based on cocoon morphological features. Moreover, the XG Boost algorithm facilitates the identification of crucial features that significantly contribute to the prediction, offering valuable insights into the factors influencing silk quality. In conclusion, the integration of cocoon morphological features and the XG Boost algorithm presents a promising avenue for accurate silk quality prediction. This research contributes to the advancement of silk production methodologies, introducing innovative technologies to the silk industry and fostering a pathway for the adoption of cutting-edge practices.

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Indian Driving Scene Description Generator Using Deep Learning

Indian Driving Scene Description Generator Using Deep Learni...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Bhuvan L Poojari Tulika Paul Vivek Singh Jayanthi M G Prashanth Kannadaguli Computer Science and Engineering Cambridge Institute of Technology Bengaluru India Department of CSE Bengaluru India Senior Data Science Trainer Dhaarini Academy of Technical Education Bengaluru India

Driving scene description generation plays a crucial role in various applications, including driver assistance systems, traffic monitoring and autonomous driving. This study proposes a comprehensive approach for driving scene description generation using deep learning (DL) techniques. The methodology involves the collection and preprocessing of a large-scale driving scene dataset, which consists of diverse driving scenarios captured from dashcam videos. The dataset is carefully labelled for different scene categories, such as highways, urban areas, night scenes, and rural roads. Frames from video datasets are collected and are captioned for DL-Model for various scene. In addition, DL-based approaches are employed to leverage the power of convolutional neural networks (CNNs) for scene detection. The CNN architecture, such as VGGNet-16 is evaluated to extract high-level features and enable accurate scene captioning from recurrent neural network (RNN) architectures such as LSTM and GRU for caption generation. The evaluation of the proposed DL models is done using BLEU score. The scores depict the level of effectiveness of the approach in accurate generation of captions for driving scenes, achieving high performance across various scene categories. The system can be deployed on embedded platforms or integrated into existing driver assistance systems to enhance situational awareness and improve decision-making processes.

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Utilizing Deep Convolutional Neural Network and Local Binary Pattern Fusion for Plant Leaf Disease Identification: An Integrated Approach

Utilizing Deep Convolutional Neural Network and Local Binary...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： S. Rahamat Basha Pratibha C. Kaladeep Yalagi Balaram Puli Pandian Sundaramoorthy Rajesh Daruvuri S Vinayagapriya Department of computer science and engineering Malla reddy college of engineering and technology Department of Computer Science and Engineering Walchand Institute of Technology Solapur Pratibhayalagi Engineering and Data Science Everest Computers Inc Application Developer EL CIC-1W-AMI IBM Technical Account Manager Cloud Data and AI Google Dept of ece St.josephs college of engineering

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Precise detection of plant diseases is essential in agriculture to maximize crop management and yield. The present study presents an innovative approach that integrates convolutional neural networks (CNNs) with local binary patterns (LBPs) in order to enhance the categorization of plant leaf diseases. The intricacy of plant disease images complicates the evaluation of their many features by conventional approaches. To solve this, the suggested technique utilizes the strengths of both CNNs and LBPs. The CNN component is highly effective in extracting discriminative features from raw pictures using hierarchical representations, which are crucial for differentiating between different illness categories. Simultaneously, the LBP descriptor enhances its usefulness by acquiring intricate texture information from specific areas of the images, so providing more effectiveness in distinguishing small differences resulting from various diseases. Utilizing a feature fusion approach, the merging of CNN and LBP features merges spatial and textural data, resulting in a more thorough representation of the input images. The fusion described herein significantly improves the precision of plant disease classification. The analysis of benchmark datasets of plant leaf diseases showed that this integrated method outperforms the use of CNNs or LBPs individually in terms of classification accuracy. The research findings indicate that the suggested approach, which combines deep Convolutional Neural networks (CNNs) with Linear Bayesian Processes (LBPs), provides a more efficient approach for detecting plant leaf illnesses. This, in turn, makes a substantial contribution to crop management and agricultural productivity.

关键词： Productivity Plant diseases Accuracy Merging Crops Benchmark testing Feature extraction Spatial databases Bayes methods Convolutional neural networks

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