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Multilingual and Cross Lingual Audio Emotion Analysis Using RNN 1

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International Conference on Advances in IoT, Security with AI, ICAISA 2023

作者： Bhattacharya, Sudipta Mishra, Brojo Kishore Borah, Samarjeet Department of Computer Science and Engineering GIET University Gunupur India Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Gangtok India

ISBN: (数字)9789819950881

ISBN: (纸本)9789819950874

Speech and language are necessary for a person’s development of their emotional and social skills. Reading, writing, and verbal apprehensions are vital components of the overall learning process, and hence, instruction or some linguistic representation is necessary for academic achievement. This study has been highlighted using several audio files based on various linguistic methodologies, and it has been compared using multilingual and cross-lingual methods. For our experiments with the English, German, Italian, and Canadian-English emotion datasets, we have decided to use CNN and LSTM as our models of choice. We obtained the most outstanding results in Canadian English during the trial with a single language. Here, we obtained CNN at 99.12% and LSTM at 98.96%. During the experiment with several languages, only English test data was found to be secure at 98.19% in CNN and 93.41% in LSTM. Lastly, in the cross-lingual domain of Canadian English, namely 93.25%, we found 98.19% using the CNN and LSTM models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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Robust Fingerprint Minutiae Extraction and Matching Using Fully Connected Deep Convolutional Neural Network and Improved SIFT 2

Robust Fingerprint Minutiae Extraction and Matching Using Fu...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Husain, Saif O. Reddy, R.Archana Pareek, Piyush Kumar Jagannathan, Srinivasan Jyothi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computer Science and Engineering Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Aiml & Ipr Cell Bengaluru India Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India G.Narayanamma Institute of Technology and Science Department of Information Technology Hyderabad India

ISBN: (纸本)9798350373110

Fingerprint is a most well-known biometric based authentication system that gives a unique identity for each person. In this paper, an authentication framework to enhance the Fingerprint Minutiae Extraction and Matching (FMEM) technique is proposed. A Fully Connected Deep Convolutional Neural Network with Improved Scale-Invariant Feature Transform (FCDCNN-ISIFT) is proposed for feature extraction and feature matching. Initially, the fingerprint images are collected from Fingerprint Verification Competition 2004 (FVC2004) which are first pre-processed using Histogram Equalization (HE) and Normalization. The images are segmented and enhanced by estimating ridge orientation and ridge frequency, and performing Gabor filtering, binarization and thinning. The fingerprint feature extraction and matching test is done using FCDCNN-ISIFT. The FCDCNN is built for detecting minutiae and the ISIFT is applied to the high contrast images to enhance the quality of latent fingerprints. Finally, the loss function is measured to determine the minutiae detection loss. The experimental results show better accuracy of 99.41%, f1-score of 97.73%, and Equal Error Rate (EER) of 1.75% when compared to results of existing approaches like Contrast Limited Adaptive Histogram Equalization - Random Sample Consensus (CLAHE - RANSAC) and Deep Convolutional Neural Network (DCNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Utilizing Neural Networks for Image and Sound Signal Recognition in Autonomous Driving 2

Utilizing Neural Networks for Image and Sound Signal Recogni...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Chaudhary, Akshita Chaudhary, Gaurav Chattopadhyay, Saumitra Verma, Akhilesh Kumar Sagar, Pramod Kumar Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Mount Litera Zee School Department of Science & Technology UP Greater Noida India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350371055

This research focuses on exploring the use of neural networks for photograph popularity in autonomous driving. Mainly, the research is investigating how semantic segmentation may be progressed with convolution neural networks (CNNs) to discover avenue objects with higher accuracy and quicker reaction. CNNs are skilled on a large-scale dataset of road scenes, employing complex architectures designed to perceive meaningful photo functions from the information. A contrast of outcomes is then made to standard techniques, which include k-nearest associates and random forest, to assess the performance of the CNNs for street scene segmentation. The goal is to offer a progressed expertise of the capabilities of neural networks for self sustaining driving photo popularity and to expand a framework for deploying such structures in the actual world. © 2024 IEEE.

关键词： architectures performance progressed research world

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Self-moving Window with Long-Short Term Memory based Anomaly Detection in Edge-Assisted IoT Environments

Self-moving Window with Long-Short Term Memory based Anomaly...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Chanti, Yerrolla Adnan, Myasar Mundher Devi, R. Renuga Lakshmanan, M. Madhavi, Arelli Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally India

ISBN: (纸本)9798350375442

Internet of Things (IoT) systems is widely used in many applications which generate huge amounts of sensory signals that involves to signify the state of systems. However, due to changes in IoT environment impact the detection of anomalies accurately which lead to risk of losing information. To overcome this limitation, a Self-moving Window with Long Short-Term Memory (SW-LSTM) is proposed for accurate detection of anomalies. The self-moving window improved the ability of LSTM to identify subtle anomalies that evolve over time, leading to more accurate detection of unusual patterns or device behavior in IoT environments. Initially, the data about attacks and malicious nodes are acquired from the UNSW-15 dataset and preprocessed by score normalization technique. Then, the optimal set of features are selected by Particle Swarm Optimization (PSO) algorithm. Finally, the proposed SW-LSTM model detects the anomalies in edge assisted IoT network effectively. The experimental results of proposed method achieved accuracy of 93.4% which is greater when compared to existing detection approaches such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Deep Learning for Precision Medicine: Advancing Personalized Treatment Plans in Oncology

Deep Learning for Precision Medicine: Advancing Personalized...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Kotiyal, Arnav Jain, Alok Devi, V.S Anusuya Rao, Bolleddu Devananda Bansal, Saloni Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Applied Sciences Bangalore India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331512965

A new approach to cancer care, precision medicine tailors treatments to each patient's specific genetic, molecular, and clinical characteristics. In order to analyze complicated, high-dimensional data and create individualized treatment plans, deep learning - a potent subfield of AI - has arisen as a game-changing tool in this area. Discover how deep learning methods are being used in precision oncology to improve cancer diagnosis, prognosis, and treatment planning in this groundbreaking article. A few examples of notable developments are the use of CNNs to medical image tumor identification and segmentation, RNNs to treatment outcome prediction, and LSTM models to patient survival. More effective and less harmful treatments are possible because too deep learning models that integrate multimodal data, such as genetic sequences, histopathological pictures, and electronic health records. These models can detect new biomarkers and forecast how different medications will affect individual patients. The clinical translation of deep learning models is hindered by a number of obstacles, despite these encouraging improvements. Problems including data scarcity, unequal distribution of classes, and unintelligibility are major roadblocks. Also, to make sure these models are accurate and reliable, they need to be standardized and validated thoroughly before they can be used in clinical processes. Fostering trust and acceptance in therapeutic contexts also requires addressing ethical problems, such as data security, possible biases in algorithmic predictions, and patient privacy. This work offers a thorough analysis of existing approaches, delves into the difficulties and moral concerns, and suggests avenues for further study to improve the use of deep learning in precision oncology. Personalized treatment regimens, made possible by deep learning, have the potential to revolutionize cancer care by vastly increasing patient outcomes. © 2025 IEEE.

关键词： Electronic health record

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An IOT-Based Soil Moisture Management System for Precision Agriculture: Real-Time Monitoring and Automated Irrigation Control 4

An IOT-Based Soil Moisture Management System for Precision A...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Laha, Suprava Ranjan Pattanayak, Binod Kumar Pattnaik, Saumendra Mishra, Debashree Kumar Nayak, Debasish Swapnesh Dash, Bibhuti Bhusan Iter Siksha O Anusandhan University Department of Computer Science and Engineering Odisha Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350300888

Managing soil moisture is crucial for optimizing crop growth and yield in agriculture. With the advancements in Internet of Things (IoT) technology, innovative soil moisture monitoring and control solutions have emerged. This paper presents a pioneering IoT -based soil moisture management system tailored for precision agriculture within Siksha 'O' Anusandhan (SOA) University. The system utilizes sensor nodes, wireless communication, and cloud computing to enable real-time monitoring and automated irrigation control, addressing the limitations of conventional manual approaches. Providing continuous, remote, and precise measurements of soil moisture levels revolutionizes how soil moisture is managed within the university's agricultural practices. Low-cost sensor nodes are strategically placed throughout the fields to collect data, which is wirelessly transmitted to a central hub for analysis and decision-making. A cloud-based platform processes the data, generating actionable insights and triggering automated irrigation when necessary. Extensive testing and validation demonstrate the system's reliability and ability to improve water use efficiency, surpassing traditional methods. Furthermore, the IoT architecture of the system offers scalability and flexibility, making it adaptable to different crop types and field conditions present within SOA University. This innovative research contributes to the field of precision agriculture by providing an efficient and tailored solution for soil moisture management within the university's agricultural practices. This IoT -based system enhances crop productivity by enabling real-time monitoring and automated irrigation control, promoting sustainable water resource utilization. Its implementation within SOA University fosters the integration of cutting-edge technology into agricultural practices, facilitating optimized crop growth and contributing to the university's commitment to environmental sustainability and resource managem

关键词： Soil moisture

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Swarm Intelligence-Based Clustering Algorithms for Wireless Sensor Networks

Swarm Intelligence-Based Clustering Algorithms for Wireless ...

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

作者： Nainwal, Ankita Shnain, Ammar Hameed Kumar, Rakesh Boob, Nandini Shirish Verma, Vikas Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This work studies the use of swarm intelligence-based clustering algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) Bee Algorithm (BA), and Firefly Algorithm (FA), to optimize Wireless Sensor Networks. (WSNs). It evaluated their performance by strict simulations on energy consumption level, network lifetime, and clustering quality. It is observed that ACO and PSO consistently showed better results with moderate energy consumption by achieving long network lifetimes as well as precise clustering. However, BA and FA have been competitive but had lower efficiency when looking at certain metrics. The comparative analysis highlights the need for algorithmic selection according to specific demands in WSN deployments. The study complements related research on the application of swarm intelligence in WSN by focusing on routing challenges, energy-aware protocols, and centralized clustering solutions. The results give useful information to practitioners who are looking for the best algorithms, especially in real WSN situations. © 2024 IEEE.

关键词： Wireless sensor networks

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Integrating Embedded Systems with Natural Language Processing: Innovations and applications in Intelligent Systems 4

Integrating Embedded Systems with Natural Language Processin...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Rawat, Ruchira Al-Fatlawy, Ramy Riad Bansal, Saloni Sehgal, Archana Gupta, Priyanka Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

The amalgamation of Natural Language Processing (NLP) with Embedded Systems signifies a revolutionary progression in the creation of intelligent systems, permitting them to participate in interactions that are akin to those of humans. This study looks into the creative ways that NLP and embedded technology can be used in a variety of industries, such as healthcare, automotive, and smart homes. The paper illustrates how NLP enables embedded devices to comprehend, interpret, and react to human commands, improving user experience and operational efficiency, by looking at the underlying principles. The main obstacles to this integration are thoroughly examined, including resource shortages, the need for real-time processing, and environmental restrictions. The study emphasises how important it is to use low-power, compact NLP models that enable real-time language processing without sacrificing system efficiency. Additionally, the research discusses the fundamental approaches for improving these models' using methods like transfer learning, quantisation, and model reduction. In the end, this work seeks to shed light on the enormous potential of voice-enabled intelligence in embedded systems, opening the door to more interactive, intuitive, and adaptable technologies that can easily adjust to changing human needs. This study advances the continuous development of smart gadgets by bridging the linguistic and technological divide, increasing their usability and accessibility in daily life. © 2024 IEEE.

关键词： Hidden Markov models

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Optimizing Energy Consumption in Smart Buildings through Reinforcement Learning-Based Demand Response Strategies

Optimizing Energy Consumption in Smart Buildings through Rei...

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

作者： Kansal, Vipashi Al-Attabi, Kassem Kumar, Rakesh Boob, Nandini Shirish Anand, Sami Gupta, Priyanka Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research studies the feasibility of RL-based response strategies that can maximize energy consumption in a smart building. By the inclusion of recreational pondering and observational investigation, RL operators automatically calibrated HVAC setpoints lighting levels level machine usage to reduce energy costs without compromising human comfort. It is thus evident from the results that significant value energy used on savings, RL-based algorithms achieved an average cost reduction of 16% compared to pattern based techniques. And the reasonable limits of tenant consolation hold standard fluctuation in temperature at 0.4 °C and variances from normal lighting level up to ten percent are also allowed. The occurrence of imperative violation is also reduced as RL-based approaches exhibit a lower number in comparison to rule based control and passive planning methods. © 2024 IEEE.

关键词： Reinforcement learning

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