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Parallel deep convolution neural network for speech-based sentiment recognition

Multimedia Tools and Applications 2024年 1-20页

作者： Adagale, Sushadevi Shamrao Gupta, Praveen CSMU Navi Mumbai India Trinity Academy of Engineering Pune India Department of Computer Engineering CSMU Navi Mumbai Mumbai India

Automatic speech sentiment recognition is crucial in different human-computer interaction systems (HMI). However, the efficiency of the SSR system is challenging because of lower feature depiction, inferior balance in the spectral and temporal characteristics of the speech, poor long-term dependency between the local and global characteristics of the acoustic features, and intricacy in the deep learning (DL) framework topology. Thus, this paper offers an efficient SSR scheme based on a parallel deep convolution neural network (PDCNN) and long short-term memory (LSTM) that accepts the multiple acoustic features (MAFs) of the speech. The MAFs depict the speech signal’s spectral-domain features (SDF), voice-quality feature (VQF), and time-domain features (TDF). The parallel DCNN having distinct convolution filters helps to enhance the generalization capability and feature representation of the MAFs. The LSTM network assists in capturing the long-term dependency and temporal representation of the MAFs. The Archimedes Optimization algorithm (AoA) is utilized to select the prominent features from the MAFs to minimize the computational intricacy of the PDCNN-LSTM framework. The Multi-Attribute Utility Theory (MAUT) scheme decides the AoA algorithm’s weights. The outcomes of the SSR scheme are estimated on the public Berlin Sentiment Database (EMODB). The feature selection using AoA helps to select the prominent features and minimize the computational intricacy of the network. The PDCNN-LSTM offers an improved overall accuracy of 99.22%, recall of 99.33%, precision of 99.67%, and F1-score of 99.49% over traditional techniques for the EMODB dataset. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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Cross domain recommendation using dual inductive transfer learning

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Multimedia Tools and Applications 2024年 1-25页

作者： Zaman, Nilufar Jana, Angshuman Computer Engineering Trinity Academy of Engineering Kondhwa-Saswad Road Maharashtra Pune411048 India Computer Science Indian Institute of Information Technology Guwahati Bongora Assam Guwahati781015 India

Cross Domain Recommendations have made a great impact on the field of online services. It helps the service provider of one domain to understand their users from the information of other domains to recommend the correct product to them. Unlike other traditional approaches, our approach finds both way latent relations between users and items considering the interactions between users and items. Traditional model normally avoids considering any implicit information about the user or item for calculating the latent relation between them. But our model Dual Inductive Transfer Learning(DITL) uses all possible user and item features and the ratings provided for recommendations. Our model learns the user’s preferences across the source and target domain and thus preserves the user’s taste across different but related domains. Our model auto-encodes the features in a feature vector to extract the user’s taste and thus transfers this across domains for efficient recommendation. The proposed model is tested on three huge benchmark datasets, the Douban dataset, the Amazon 5-crore dataset for the domain of movies, books and music whereas Tenrec dataset for the domain of articles and videos and it proves to outperform the classical cross-domain recommendation systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recommender systems

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"affectAlchemy": An Affective Dataset Based on Plutchik's Psychological Model for Text-Based Emotion Recognition and its Analysis Using ML Techniques 8

"affectAlchemy": An Affective Dataset Based on Plutchik's Ps...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Kapase, Ajay Uke, Nilesh Savant, Jatin Desai, Mrunali Ghatage, Shravani Rahangdale, Atul Vishwakarma Institute of Information Technology Computer Science & Engineering Pune India Trinity Academy of Engineering Computer Science & Engineering Pune India KITs College of Engineering Computer Science & Engineering Kolhapur India

ISBN: (纸本)9798350391770

A technique to identify people's attitudes, and sentiments towards specified targets such as things, services, and subjects, is called sentiment analysis. As a dedicated subset of NLP, it deals with predicting specific reactions broadly characterizing sentiments as positive, negative, or neutral. However, the accuracy and depth vary since there are a variety of human emotions that exist rather than just positive, negative, and neutral. In the past few decades, the term affective computing has become well-liked which involves the use of AI for identifying, interpreting, and responding to human emotions. A part of it called text-based affective systems are artificial emotional intelligent systems that often categorize the text into core wide range of emotions like fear, sadness, anger, and happiness. These affective systems use various psychological models for emotion recognition. Our study incorporates the development of a dataset based on Robert Plutchik's psychological model of emotions, which sets forth eight primary emotions which are combined to form more complex emotions, resulting in a total of 24 secondary emotions resulting total of 32 variety of emotions. The work introduces the "AffectAlchemy"dataset which is validated using multiclass classification machine learning techniques. This dataset can contribute to the field of affective computing for the development of models that can do enhanced emotion recognition, and improved emotional context understanding to develop emotion-aware applications and facilitating research in affective systems. © 2024 IEEE.

关键词： Intelligent systems

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Solarsense: Enhancing Energy Efficiency Through Iot-Enabled Solar Tracking 2

Solarsense: Enhancing Energy Efficiency Through Iot-Enabled ...

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

作者： Thopate, Kaushalya Gawade, Mayuri Kakade, Surabhi Bahir, Suvarna Kulkarni, Mukund Jaiswal, Krishna Nilesh Computer Engineering Vishwakarma Institute of Technology Pune India Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Computer Engineering Trinity Academy of Engineering Pune India Electronics and Telecommunication Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350360523

The surging global demand for energy is primarily fueled by a burgeoning world population and elevated living standards. However, with traditional fossil fuels like oil dwindling in availability, there is an urgent need to shift towards renewable energy sources. Given the rapid progress in renewable energy technologies, a key objective of the project is to develop a solar energy tracking panel that can rotate for enhanced power generation. This innovative panel, capable of adjusting its position to optimize energy output by tracking the sun, proves invaluable in monitoring power consumption. The design of this rotatable solar energy tracking panel, incorporating four essential components (solar energy tracking panels, light-dependent resistors (LDRs), an Arduino, and a motor), is intricately explained in this study. This configuration not only serves as an advanced educational tool but also provides engineering technology students with a comprehensive understanding of alternative energy sources like solar power. © 2024 IEEE.

关键词： Fossil fuels

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IoT Based Smart Home with a Thief Detection and Tracking System 7

IoT Based Smart Home with a Thief Detection and Tracking Sys...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Surana, Amruta Kendre, Priyanka K. Palkar, Jayashri D. Vaidya, Anusha O. Jain, Chetana H. Gopinath, N. Sinhgad Institute of Technology Computer Engineering Department Lonavala India Trinity Academy of Engineering Department of Information Technology Pune India Trinity Academy of Engineering Department of Electronics and Telecommunications Pune India B.S.Abdur Rahman Crescent Institute of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798350340600

As automation technology advances exponentially, human-operated systems are progressively replaced by automated ones. Massive adoption and pervasive impact of the Internet of Things have drastically altered how humans relate to and comprehend the physical world. The exponential expansion of internet-enabled devices is incredible, from the smallest sensors to the largest industrial robotics. In recent years, 'smart home' technologies have garnered considerable interest due to their potential to enhance people's daily lives. With the assistance of smart home technology, homeowners can swiftly and comfortably control the climate in their homes. Recently, increasingly sophisticated and inventive technologies have been developed to support 'smart living' systems. The primary objective of this article is to use the Internet of Things to design a smart home automation system that enables remote monitoring and administration of a conventional home via an Android-based smartphone application and cutting-edge security technology. The proposed research uses cutting-edge technologies such as machine learning and the Internet of Things to locate and track intruders. It substantially increases safety when compared to more traditional methods. © 2023 IEEE.

关键词： Internet of things

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Maximizing Crop Yields and Water Efficiency through Tailored Irrigation System Design and Crop Selection

Maximizing Crop Yields and Water Efficiency through Tailored...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Musale, Prajakta Jadhav, Balasaheb Bahir, Suvarna A Thopate, Kaushalya Anarase, Dipali Jaiswal, Krishna Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Trinity Academy of Engineering Department of Computer Engineering India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Wipro Technology India

ISBN: (纸本)9798350308167

Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integrated with the water-pump. While studying the existing system we came across a problem that, existing systems does not provide any solution for optimizing the usage of water and power. Optimizing the use of power and water in farming has become a real-time problem these days. To overcome this problem, we have proposed an IOT based system "Advanced Irrigation System". This system provides automation for operating the water pump in the farm by automatically turning ON and OFF the water-pump depending on the type of the crop and its moisture requirements. Also, proposed system provides farmer interface via a mobile application in his regional language, where he can check the moisture, humidity and temperature of his farm. Along with this proposed system has a Google Assistant integrated, using which famer can operate the water-pump remotely over a voice command. © 2023 IEEE.

关键词： Advanced Irrigation System Crop Selection Maximizing Crop Yields power Optimization Water Efficiency water-pump operating systems

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Optimizing MEMS Optical Arrays with Silicon Micro Mirrors using Machine Learning Techniques 3

Optimizing MEMS Optical Arrays with Silicon Micro Mirrors us...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Joshi, Vilas Ramrao Sharma, Surender Kumar Galande, Vijay Agatrao Sachin Patil, Pallavi Patil, Ushadevi Suttarwala, Hussain Sharma, Deepak K. Savitribai Phule Pune University Department of Computer Engineering Pune India Poornima University Department of Electrical Engineering Jaipur India KJEI's Trinity Academy of Engineering Department of Engineering Science Pune India Genba Sopanrao Moze College of Engineering Department of Civil Engineering Pune India American Express Senior Software Engineer PhoenixAZ85054 United States University of Technology Jaipur India

ISBN: (纸本)9798350361391

In the rapidly evolving landscape of optical microsystems, the infusion of Machine Learning (ML) techniques stands out as a catalyst for unprecedented progress. This research paper delves into the intricate interplay between Micro-Electro-Mechanical Systems (MEMS), optical arrays, and silicon micro mirrors, shedding light on the transformative prowess unleashed by Machine Learning in optimizing these intricate components. Focusing on the limitations posed by conventional designs, the paper not only illuminates the challenges encountered but also presents a compelling narrative of how ML algorithms emerge as formidable allies, poised to surmount these hurdles with ingenuity and precision. The exploration extends beyond a mere technical discourse;it unfolds as a narrative of innovation, where the convergence of MEMS, optical arrays, and silicon micro mirrors, guided by the intelligence of Machine Learning, transcends traditional boundaries, propelling optical microsystems into an era of unparalleled efficiency and adaptability. This research beckons a new epoch, where the intricate dance of silicon micro mirrors and the intricate mechanics of MEMS find harmony through the symphony of Machine Learning, reshaping the landscape of optical microsystems and inspiring future possibilities that were once deemed beyond reach. © 2024 IEEE.

关键词： Microchannels

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"AffectAlchemy": An Affective Dataset Based on Plutchik’s Psychological Model for Text-Based Emotion Recognition and its Analysis Using ML Techniques

"AffectAlchemy": An Affective Dataset Based on Plutchik’s P...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Ajay Kapase Nilesh Uke Jatin Savant Mrunali Desai Shravani Ghatage Atul Rahangdale Computer Science & Engineering Vishwakarma Institute of Information Technology Pune India Computer Science & Engineering Trinity Academy of Engineering Pune India Computer Science & Engineering KITs College of Engineering Kolhapur India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

A technique to identify people's attitudes, and sentiments towards specified targets such as things, services, and subjects, is called sentiment analysis. As a dedicated subset of NLP, it deals with predicting specific reactions broadly characterizing sentiments as positive, negative, or neutral. However, the accuracy and depth vary since there are a variety of human emotions that exist rather than just positive, negative, and neutral. In the past few decades, the term affective computing has become well-liked which involves the use of AI for identifying, interpreting, and responding to human emotions. A part of it called text-based affective systems are artificial emotional intelligent systems that often categorize the text into core wide range of emotions like fear, sadness, anger, and happiness. These affective systems use various psychological models for emotion recognition. Our study incorporates the development of a dataset based on Robert Plutchik's psychological model of emotions, which sets forth eight primary emotions which are combined to form more complex emotions, resulting in a total of 24 secondary emotions resulting total of 32 variety of emotions. The work introduces the "AffectAlchemy" dataset which is validated using multiclass classification machine learning techniques. This dataset can contribute to the field of affective computing for the development of models that can do enhanced emotion recognition, and improved emotional context understanding to develop emotion-aware applications and facilitating research in affective systems.

关键词： Emotion recognition Analytical models Affective computing Sentiment analysis Computational modeling Psychology Machine learning Long short term memory Context modeling Software development management

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Deep Fake Detection for Preventing Audio and Video Frauds Using Advanced Deep Learning Techniques 6

Deep Fake Detection for Preventing Audio and Video Frauds Us...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Vaidya, Anusha O. Dangore, Monika Borate, Vishal Kisan Raut, Nutan Mali, Yogesh Kisan Chaudhari, Ashvini Trinity Academy of Engineering E & T C Department Pune India G.H Raisoni Institute of Engineering and Management Aiml Department India Dr D Y Patil College of Engineering & Innovation Computer Engineering Department Pune India G.H Raisoni College of Engineering and Management Aiml Department India Nmims Center for Distance & Online Education Pune India

ISBN: (纸本)9798350381689

Deepfakes allow for the automated gen- eration of fake video content, often accomplished through the use of generative adversarial networks. To address the increasing issue of deepfakes, this study focuses on constructing a model that incorporates advanced techniques. The researchers combined the ResNeXt, Long Short-Term Memory (LSTM), and ResNet architectures, selecting them based on their effectiveness in handling complex visual data and capturing temporal dependencies. Prior to detection, the dataset underwent pre-processing using the Multi-Task Cascaded Convolutional Neural Network (MTCNN), which facilitated the accurate extraction of facial regions. Importantly, the study evaluated the model across three diverse and significant datasets: the Face Forensics++ dataset (FF-DF), the Celeb-DF dataset, and the Facebook Deepfake Detection Challenge (DFDC) dataset. This comprehensive evaluation en- sured the model's ability to generalize and its suitability for real-world scenarios, as demonstrated by its exceptional detection accuracy. The combination of models employed in this study yielded highly accurate results and remained robust in the face of evolving deepfake technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Early Detection of Breast Cancer Subtypes with Convolutional Neural Networks 1

Early Detection of Breast Cancer Subtypes with Convolutional...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kadam, Bhagyashree Swapnil Dewangan, Leelkanth Parida, Prasanta Kumar Chaterjee, Tanushree Nahak, Kirti Sadashiv, Ebhad Swagat Sinhgad Institute of Technology and Science Narhe Pune It Department India Swangi Dept of Computer Science & Engineering Wardha India Kiit University Dept of Computer Science & Engineering Odisha Bhubaneswar751024 India Raipur Institute of Technology Department of Biotechnology Raipur India Kalinga University Dept of Computer Science & Engineering Chhattisgarh Raipur India Trinity Academy of Engineering Department of Engineering Science Kondhwa Pune India

ISBN: (纸本)9798350330915

Breast cancer continues to be a major worldwide health issue, demanding accurate subtype categorization for personalized treatment approaches. In order to accurately identify subtypes, this work introduces a unique hybrid classifier that uses multi-modal data integration as well as cutting-edge machine learning algorithms. The model uses a Convolutional Neural Network (CNN) for image-based feature extraction alongside machine learning techniques for genomic and clinical features, combining mammographic, histopathological, genomic, and clinical data. The interpretative framework, which depends on an interpretivist philosophy, makes it less difficult to comprehend subtype discrimination on a more in-depth level. With an overall accuracy of 92.5% across HER2-positive, estrogen receptor-positive, and triple-negative subtypes, the results clearly demonstrate the hybrid classifier's better performance. The model's sensitivity and specificity ratings regularly outperformed those of existing techniques, demonstrating its aptitude for picking up on fine subtype distinctions. Particularly, the classifier demonstrated a low incidence of false positives, which is critical for reducing pointless interventions. Key genetic markers, physical traits, and hormone receptor status have been recognized as important discriminators employing feature importance analysis. These discoveries broaden the understanding of the biological foundations of each subtype. The study offers a useful tool for accurate breast cancer detection and treatment planning in clinical practice. The hybrid classifier's integrative methodology makes use of several data sources in order to provide a thorough understanding of breast cancer subtypes. This work lays the groundwork for future oncology and multi-modal data analysis studies, with potential implications in personalized therapy and other cancer types. There is still much more to discover about ethical issues, model generalization, and therapeutic use in th

关键词： Breast Cancer Subtype Classification Convolutional Neural Networks (CNNs) Interpretivism Philosophy Multi-modal Data Integration Personalized Treatment Strategies

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