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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Roshika, R. Girish, Anjana Karthik, N. Vani, V. Department of Computer Science and Engineering National Institute of Technology Puducherry Puducherry Karaikal609 609 India

ISBN: (纸本)9783031699818

This music recommendation system focuses on the creation of an innovative music recommendation system designed to suggest songs tailored to users’ facial emotions and preferences derived from their online behavior. Extensive user details are scrutinized and summarized to comprehend individual musical tastes. Real-time facial emotion detection, coupled with social web content summarization, aids in the identification of singer names. The system recommends songs based on detected emotions and identified singer names, offering suggestions for popular songs and playlists. An insightful observation reveals users’ inclination toward happy songs during moments of joy, prompting a unique approach to address a recognized issue. Rather than exacerbating low moods with sad songs, the system recommends motivational or happy songs, showcasing its commitment to uplifting users. User studies affirm the precision and utility of the project, underscoring the overarching goal to provide a music recommendation system that not only caters to users’ musical preferences but also enhances their emotional well-being. The integration of continuous facial emotion detection and website summarization into the recommendation process adds a novel and dynamic facet to the system’s capabilities. © IFIP International Federation for Information Processing 2024.

关键词： Emotion Recognition

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Enhancing Speech Emotion Recognition Combining Silence Elimination and Attention Model with a Novel CNN Architecture 8th

Enhancing Speech Emotion Recognition Combining Silence Elimi...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Gawali, Piyush P. Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

A natural interface for human–computer interaction is automatic speech emotion recognition, but it encounters challenges in handling non-emotional speech segments, especially silence, since it is non-emotional speech segmentation that requires recognition. Using a carefully designed attention model and a silence elimination approach, this paper proposes a novel method of enhancing emotion recognition performance by combining silence elimination with attention. In the present study, an enhanced Convolutional Neural Network architecture has been proposed, comprising of nine layers, utilizing a combination of convolutional 1D As a result of the research, CNNs have been employed in a revolutionary speech-emotion detection system that reaches an impressive 96.00% accuracy rate. There is great potential in this breakthrough to develop social robots and conversational robots capable of conveying nuanced human emotions. This breakthrough has marked a significant improvement in accuracy compared to prior models based on the same dataset. In addition to the broader classifications of positive, negative, and neutral emotions, the model's accuracy is evaluated across a wide range of emotion classes, such as anger, calmness, fear, happiness, and sadness for both genders. There is considerable evidence to suggest that combined noise cancellation and attention models are more effective than individual models of noise cancellation or attention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Finance and the Low-Carbon Transition: Implications for Market Valuation and Investor Behaviour 2

Finance and the Low-Carbon Transition: Implications for Mark...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Bhuyan, Pragyan Bhoi, Shubh Guleria, Prashant Gauhati University Department of Accountancy India National Institute of Technology Department of Computer Science and Engineering India Birla Institute of Technology Mesra Department of Management India

ISBN: (纸本)9798350360523

The shift to low-carbon capital resources has significant implications. Our comprehensive macroeconomic framework, considering high-carbon and low-carbon tangible assets, reveals that adopting low-carbon practices induces oscillations in both the macroeconomy and financial sphere. Examining the impact of financial market sentiments on the transition trajectory, we highlight the pivotal role of the financial sector. Despite lower operational costs of low-carbon technologies, investor behavior favors established technologies. Our analysis shows that increased 'climate indifference' can prolong the transition. Therefore, we emphasize the importance of climate impact disclosures in finance and the need for strategies to assist investors in mitigating uncertainty. © 2024 IEEE.

关键词： Investments

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Comparative Analysis of YOLO Models for Plant Disease Instance Segmentation

Comparative Analysis of YOLO Models for Plant Disease Instan...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Singh, Agamjot Yadav, Aryan Verma, Anshul Rana, Prashant Singh Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India Institute of Science Banaras Hindu University Department of Science Varanasi India

ISBN: (纸本)9798350376876

Accurate detection and segmentation of plant diseases are essential for sustaining agricultural productivity and global food security. This report conducts a comparative analysis of three state-of-the-art YOLO models-YOLOv5, YOLOv7, and YOLOv8-emphasizing their efficiency in instance segmentation of plant diseases. The research employs a comprehensive dataset featuring various crops like rice, sugarcane, wheat, bell pepper, potato, and tomato, each suffering from different diseases. The methodology includes fine-tuning YOLO models with pretrained weights and optimizing them with the curated *** assessment of performance is based on crucial metrics such as recall, mean average precision (MAP) and precision. YOLOv8 exhibits superior performance, with over 9 0 % average precision across all disease categories, significantly outperforming YOLOv5 and YOLOv7. This report offers detailed insights into the architectural features, training procedures, and evaluation metrics of each model. It also addresses the implications of the findings for practical agricultural applications, highlighting the role of advanced deep learning techniques in improving crop protection and management strategies. Despite the positive results, the report acknowledges limitations like dataset dependency and challenges in real-world deployment. © 2024 IEEE.

关键词： Agriculture Comparative study Deep learning Plant disease instance segmentation YOLOv5 YOLOv7 YOLOv8

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A hybrid adaptive neuro-fuzzy approach for automatic spoken digit recognition

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International Journal of Speech technology 2023年第4期26卷 825-832页

作者： Hussain, Irshed Roy, Pinki Jagamara Odisha Bhubaneswar751030 India Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar788010 India

Nowadays, spoken digit recognition is widely used for various applications. It is a technique used in automatic speech recognition to identify and classify the language spoken by a particular speaker. Several techniques and methods for spoken digit recognition have been proposed. Most of the method includes artificial neural networks, Gaussian mixture models and neuro-fuzzy approach. However, there needs to be a more efficient algorithm that gives more satisfactory and better results. Therefore, to enhance the results in terms of accuracy and running time, a hybrid adaptive neuro-fuzzy approach called KANFIS-SCG is proposed in this paper. This approach combines the Adaptive Network-Based Fuzzy Inference System (ANFIS) and a fast supervised learning algorithm called the Scaled Conjugate Gradient (SCG). In addition, the K-means clustering algorithm is used to reduce the number of fuzzy inference rules. The features are extracted from speech samples using the Mel Frequency Cepstral Coefficient (MFCC) algorithm. Furthermore, the covariance matrix provides more precise information about the features. The experiment was carried out in two languages, namely Hindi and English, and the database was prepared for both languages where utterances were digits from zero to nine. The proposed method KANFIS-SCG gives average recognition rates of 99.12% and 98.54% for Hindi and English, respectively, for the speaker-dependent system, which is better than the existing ANFIS method. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Covariance matrix

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Urban Pluvial Flood Prediction Using Machine Learning Models 4

Urban Pluvial Flood Prediction Using Machine Learning Models

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Waseemullah Hassan, Tahir Majeed, Sajid Memon, Muhammad Qasim NED University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Institute of Space Technology Department of Remote Sensing & Geo-Informatics Karachi Pakistan University of Sufism and Modern Sciences Department of Information and Computing Bhitshah Pakistan

ISBN: (纸本)9798331504342

Urban pluvial flooding causes threats to human lives, economy, infrastructure, and ecosystems around the world, particularly in developing countries. Pakistan is among those developing countries that have become vulnerable to urban flooding due to global warming, climate change risks, rapid urbanization, and the occurrence of extreme and unusual rainfall events in the monsoon season. To effectively mitigate the adverse effects of pluvial flooding, there is a pressing need for an advanced and accurate predictive model that is capable of predicting urban pluvial flooding. The aim of this research is to utilize the potential of machine learning (ML) techniques and algorithms to accurately predict the occurrence of urban pluvial flooding, for this study we have worked on the monsoon rainfall data of multiple cities of Sindh province in Pakistan. We made an attempt to use multiple ML models especially logistic regression (LR), random forest (RF), k nearest neighbor (KNN), and support vector machine (SVM) to propose the best and accurate predictive model. The developed models were evaluated and assessed using various performance evaluation metrics which includes accuracy, recall, precision and F1 score. The best among proposed models was created using LR which achieved an accuracy of 97%, while the accuracy of other models which were created using SVM, KNN and RF were 96%, 93%, and 92% respectively. © 2024 IEEE.

关键词： Prediction models

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Tracking Individual in Kedarnath Trek Using LoRaWAN technology 3rd

Tracking Individual in Kedarnath Trek Using LoRaWAN Technolo...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Kumawat, Pushpak Newatia, Tanmay Tiwari, Sankalp Rai, Adarsh Parwekar, Pritee Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-Meerut Road Ghaziabad India

ISBN: (纸本)9789819739905

In remote trekking regions such as the Kedarnath trail, ensuring the safety and security of individuals remains a paramount concern due to the lack of reliable network connectivity and Internet services. Casualties and incidents of individuals losing connection with their groups often pose significant challenges for authorities in locating and providing assistance promptly. To address this issue, this research proposes the implementation of a Long-Range Wireless Local Area Network (LoRa WAN) system for tracking individuals during the Kedarnath trek (Mo et al., Proceedings of the 2020 15th IEEE conference on industrial electronics and applications (ICIEA), IEEE, 2020). By leveraging the long-range communication capabilities of LoRa technology, this system aims to establish a robust network infrastructure capable of tracking individuals and facilitating communication among trekking groups even in areas with minimal network coverage (Dragoni et al., Ad Hoc Netw, 56:122–140, 2016). Through the integration of GPS tracking devices and LoRa transceivers, real-time location data can be transmitted securely to a centralized monitoring system, enabling authorities to efficiently respond to emergencies and incidents. This paper outlines the technical design and implementation of the proposed LoRa WLAN system, discusses its potential benefits in enhancing safety and security during trekking expeditions, and presents a framework for future research and development in this domain (Abdelmaksoud et al., Proced Comput Sci 162:256–265, 2019). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Industrial electronics

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Violence Detection in Indoor Domestic Environment Using Multimodal Information 5th

Violence Detection in Indoor Domestic Environment Using Mul...

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5th International Conference on Machine Learning, Image Processing, Network Security, and Data Sciences, MIND 2023

作者： Aravinth, R. Shashwat, T. Yeshwanth, V. Sitara, K. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli620015 India

ISBN: (纸本)9783031622168

The recognition of violent activities has emerged as a prominent topic within the realm of computer vision research. This issue is of paramount importance within indoor domestic environments, given the heightened vulnerability of individuals residing in such conditions to potential attacks. Consequently, there exists a substantial imperative for the development of intelligent surveillance systems capable of autonomously monitoring individuals and identifying instances of violent behavior. While numerous techniques grounded in handcrafted and deep learning features have been proposed to address this challenge, it is noteworthy that many of these methodologies primarily focus on video data and often disregard the potential insights offered by audio information. In light of this, this paper presents a fusion model for violence detection, which integrates audio and video features to provide a comprehensive and nuanced assessment of potentially violent incidents. Our proposed approach leverages inflated 3D networks and pretrained models from the Visual Geometry Group to extract video and audio features, respectively. Subsequently, these extracted feature vectors are combined utilizing a joint cross-attention fusion model. The results of our proposed model are promising, with an achieved accuracy rate of 73.3%, coupled with a processing speed of 25 frames per second. This research contribution is instrumental in advancing violence detection technology, particularly within indoor domestic environments, where the safety and well-being of individuals are paramount concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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YOLOv8-Based Real Time Fish Detection and Classification Using Indigenous Bangladesh Fish and its Comparison

YOLOv8-Based Real Time Fish Detection and Classification Usi...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nathasarma, Rahash Mazumdar, Biswatosh Roy, Binoy Krishna National Institute of Technology Silchar Department of Electrical Engineering Assam Silchar India Sikkim Manipal Institute of Technology Department of Computer Science Sikkim India

ISBN: (纸本)9798331540821

Fisheries management has a significant role in fish farming. Fisheries managers can make informed decisions regarding stock assessment, resource allocation, and disease prevention, contributing to sustainable fishing practices and the preservation of aquatic ecosystems. This paper presents a case study for simultaneous fish detection and classifications using YOLOv8 neural network. The YOLOv8 is a real-time object detection algorithm that can be used to detect fish in images and classify the different categories of fish. We have taken an existing dataset for this study, i.e. indigenous Bangladesh fish dataset of eight different fish species. We trained a YOLOv8 model on that dataset. A detailed description of the training process is provided. The effectiveness of the model is evaluated through the normalised confusion matrix, mean average precision curve of the trained model and other loss plots. The designed model can detect fish and classify fish species with an accuracy of about 99% and comparatively better than previous works. © 2024 IEEE.

关键词： Invertebrates

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An Improved Blockchain Based Security Framework for IoT Enabled Wireless Network

An Improved Blockchain Based Security Framework for IoT Enab...

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

作者： Shrivastava, Kapil Singh, Sandeep Chaudhary, Preeti Singh, Ruchi Saraswat, Birendra Kumar Garg, Ashish Gla University Department Of Computer Engineering And Applications UP Mathura India Gl Bajaj Institute Of Technology And Management Department Of Information Technology UP Greater Noida India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Srm Institute Of Science And Technology Department Of Management UP Modinagar India Raj Kumar Goel Institute Of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

The net of factors (Iota) has opened new opportunities for connecting a diverse range of devices and making them handy over the net. However, it has additionally accelerated the vulnerability of these gadgets to cyberassaults. One capability technique to cope with It trouble is implementing Block chain generation, which offers allotted and relaxed facts storage and transaction validation. It paper advocates a progressed Block chain-based safety framework for Iota-enabled wireless networks. Our framework uses clever contracts, which might be self-executing virtual contracts that implement the agreed-upon policies and conditions among parties. Those clever contracts ease communication among Iota gadgets and the Wi-Fi community, ensuring that the most effective legal and demonstrated devices can access the network. Our framework incorporates a consensus mechanism for validating transactions on the Block chain, ensuring the integrity and immutability of statistics. Our proposed framework contains a dynamic key control gadget, which often updates and rotates keys to prevent unauthorized admission to the community. It selection also permits relaxed communication among devices, even in the case of a compromised key. In conclusion, our improved Block chainbased total protection framework presents a strong answer for securing Iota-enabled Wi-Fi networks. © 2024 IEEE.

关键词： Wi-Fi

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