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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehensive Study using XGBoost Classifier and Feature Selection Techniques

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Procedia computer science 2024年 235卷 3458-3467页

作者： Rekha R Nair Tina Babu Kishore S Vishnu Vilashini S Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru Karnataka India

Healthcare systems around the world have faced challenges due to the Coronavirus disease (COVID-19) epidemic, which has taken resources and attention away from long-term diseases like liver cancer. To identify the effect of COVID-19 on liver cancer, proposed an effective research work with Extreme Gradient Boosting (XGBoost) classifier. The identification of the most suitable feature is executed through the utilization of SelectKBest, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques. A comparative analysis is carried out to ascertain the more precise feature selection, employing Decision Tree, K-Nearest Neighbors (KNN), CatBoost, LightGBM, and XGBoost algorithms. Further, the proposed work undertakes an analysis of the impact of COVID-19 on liver cancer, encompassing both the pandemic and pre-pandemic periods. In comparison to other feature selection techniques and models, SelectKBest feature selection with Xgboost classifier provided an accuracy of 0.92. The proposed research work provides valuable insights to healthcare professionals and policy makers, providing a better understanding of the challenges faced by liver cancer patients during the pandemic. By meticulously exploring feature selection techniques and employing advanced machine learning models, this study quantifiably demonstrates the effectiveness of the proposed methodology in addressing the impact of COVID-19 on liver cancer, contributing to a more comprehensive understanding of this critical issue.

关键词： XGBoost Classifier SelectKBest Singular Value Decomposition Principal Component Analysis hepatocellular carcinoma

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Industry 4.0 based Machine Learning Models for Anomalous Product Detection and Classification

Industry 4.0 based Machine Learning Models for Anomalous Pro...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Kumar, Sourabh Chandra, Saroj Kumar Shukla, Ram Narayan Panigrahi, Lipismita Computer Science and Engineering Op Jindal University Center of Excellence in Industry 4.0 Raigarh India Kalinga Institute of Industrial Technology Department of Computer Applications Bhubaneswar India

ISBN: (纸本)9781665492942

Automation has made tremendous changes in the industries. It has been used to automate the manual processes involved in different physical units of the industries. The purpose was to increase the production in the manufacturing. Now, computers are being used in the industries to monitor functionalities of different production units with the help of artificial intelligence and internet of things (IoT). The IoT has revolutionized the industries. It is an interconnected network system of physical units. The core purpose of it to gather and share information among different physical units. The IoT has great impact on the many areas such as business, industry, medicine, the economy, transport, industrial robots and automation systems. IoT with artificial intelligence has wide range of industrial applications. Industry 4.0 is used in the industries where different industrial units are connected over the internet and interacting to make decisions via machine-to-machine communication. It has increased the benefits of industries in terms of production and supply chain management. Manufacturing industry monitors its production units in every 10 milliseconds to capture features of the product that is being produced. The features generated in this process are huge in amount. Critical observation is performed on the generated features to categorize the product as anomalous or good. Product classification is difficult task in the labeled datasets due to human bias in labeling the final product as anomalous or good. In this work, machine learning models is being used to detect and classify faulty product produced by manufacturing industry. Both qualitative and quantitative study will be carried out to compare various machine learning models. © 2023 IEEE.

关键词： Internet of things

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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning Empowered IoT Toothbrush: A Paradigm Shift in Dental Health Monitoring 1

Deep Learning Empowered IoT Toothbrush: A Paradigm Shift in ...

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

作者： Lodha, Nikita Pal, Ankit Das, Sayan Roy, Shreyashi Chakraborty, Sanjay Pandey, Saroj Kumar Techno International New Town Computer Science and Engineering Kolkata India Techno International New Town Aiml Department Kolkata India Gla University Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350330915

The field of dentistry plays a crucial role in maintaining oral health and preventing dental problems. However, many people neglect their dental well-being because of busy schedules or limited access to dental care. Dental issues may go unnoticed until they become more serious and require complex and expensive treatments. To address this issue, we propose an innovative solution that uses dental selfies and advanced machine learning algorithms to identify and classify various dental conditions, such as cavities and gum diseases. These conditions will be categorized as either 'Healthy' or 'Unhealthy.' The main objective of this project is to create a convenient and affordable tool that empowers individuals to detect and diagnose dental problems early on. This tool encourages proactive measures to maintain oral health and seek timely treatment. Our project focuses on developing a technologically advanced toothbrush that incorporates a camera and sensors to classify dental selfies as either indicative of 'Healthy' or 'Unhealthy' oral conditions. To ensure accurate results, we conducted extensive experiments using various Convolutional Neural Network (CNN) models such as VGG16, MobileNet, DenseNet, and VGG19. Through rigorous testing, we achieved a maximum accuracy rate of 88%. This outcome demonstrates the effectiveness of our approach in accurately evaluating oral health conditions based on dental selfies. © 2023 IEEE.

关键词： Convolutional neural network Deep Learning Dentalcare IoT Smart Toothbrush

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Face Recognition using Adaboost Classifier For The Visually Impaired 8

Face Recognition using Adaboost Classifier For The Visually ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Avinash, N.J. Krishnaraj Rao, N.S. Rama Moorthy, H. Mangalore Institute of Technology and Engineering Department of Electronics and Communication Engineering Mangalore Karnataka Moodabidre India Nmam Institute of Technology Department of Information Science and Engineering Karnataka Nitte India Nitte Institute of Professional Education Department of Computer Applications Karnataka Mangaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The paper suggests an approach to facial recognition of physical challenges. The most difficult challenge for blind persons is identifying impediments outside and behind them. They mostly rely on other people to help them make up for their blindness. To overcome this, they need to be supported by someone else people. In their daily lives, they face many problems when it comes to moving from one place to another. There is no guidance to help them when they need it. This study suggests a facial recognition system for those with vision impairments utilizing the Raspberry Pi to help prevent these situations. There is a camera designed for face recognition and a microphone designed for voice recognition in this case, so these components can be combined and people with visual disabilities can use it easily and behaves reasonably. In this work, a method is presented for detecting, classifying, and locating objects within complex environments. The paper applies the techniques of image processing and Adaboot classifiers to achieve an accuracy of more than 90 © 2024 IEEE.

关键词： Face recognition

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Quantum-Inspired Neural Network For Enhanced Image Recognition

Quantum-Inspired Neural Network For Enhanced Image Recogniti...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Michael, Raj. T.F. Rmesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. Sujatha, D. Sclas Simats Department of Computer Applications Tamilnadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Computer Science and Engineering Easwari Engineering College Chennai Ramapuram India St.Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350394580

The field of quantum computing has seen significant progress in recent times, leading to the emergence of innovative methodologies across several areas, particularly in machine learning and neural networks. This research study introduces a quantum-inspired neural network (QINN) that has been particularly developed to improve image recognition capabilities. In contrast to conventional neural networks, the Quantum-inspired Neural Network (QINN) utilizes concepts derived from quantum mechanics, including superposition and entanglement, to process and encode information effectively. The paradigm we propose incorporates quantum bits (qubits) as computing units instead of traditional binary nodes, simultaneously enabling the representation of many states. Moreover, the use of the intrinsic parallelism of quantum mechanics is harnessed to provide expedited and more effective data processing, particularly within the concealed levels of the network. The researchers ran benchmark tests using well-recognized image datasets, including CIFAR-10 and ImageNet. The study's findings indicated that QINN displayed superior accuracy rates compared to traditional methods and revealed faster convergence throughout the training process. Additionally, our quantum-inspired methodology demonstrated significant efficacy in analyzing pictures containing nuanced characteristics, highlighting its potential in practical scenarios where precise identification is of utmost importance. In summary, integrating quantum principles with neural network topologies has the potential for groundbreaking advancement in image recognition skills. QINN is a groundbreaking advancement in this domain, emphasizing the unexplored capacity to use quantum phenomena for sophisticated machine learning objectives. Potential areas for future study are the expansion of network scalability to accommodate bigger datasets, exploration of security measures that are resilient against quantum threats, and investigating hybrid mo

关键词： image recognition neural network Quantum-inspired Qubits

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Harnessing Deep Learning and Time Series Models for Accurate Global Solar Radiation Prediction

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Procedia computer science 2025年 259卷 98-107页

作者： K. Meena A. Vadivel Tanvir Habib Sardar Arnav Kotiyal Guru Prasad M S Utsav Kumar Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bengaluru - 562112 India Department of Computer Applications Graphic Era (Deemed to be University) Dehradun India

For applications such as weather forecasting, power grid management, and solar energy integration, accurate solar radiation prediction is essential. Using real-time data from the EU science Hub’s Climate Monitoring Satellite Application Facility (CM SAF), this research examines the efficacy of several time series models for hourly solar radiation prediction. The data are subjected to stationarity testing using the Augmented Dickey-Fuller (ADF) test prior to the models being applied. This study uses well-known time series models, including Vector Auto-regression (VAR), Linear Regression, and Autoregressive Integrated Moving Average (ARIMA). The performance of the proposed model is evaluated using Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Absolute Percentage Error (MAPE). It is found that Vector Auto-regression (VAR) is encouraging for predicting solar radiation on hourly basis with least amount of overall error. This analysis has used residual error inspection, error distribution analysis and sample accuracy evaluation using MAPE. This paper highlights the importance of VAR on using time series model for predicting solar radiation.

关键词： Solar Radiation Prediction Time Series Models Autoregressive Integrated Moving Average (ARIMA) Vector Autoregressive (VAR) Regression Error Metrics performance analysis

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Statistical validations on energy efficient routing protocols to improve the performance of Wireless Sensor Network 1

Statistical validations on energy efficient routing protocol...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Chithaluru, Premkumar Jena, Lambodar Singh, Debabrata Nayak, Soumen Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India University Department of Computer Applications Odisha Bhubaneswar India University Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9781665454933

In a Wireless Sensor Network (WSN), there is a need for research in design a well-suited routing protocol used for remote applications, and it may resolve the issues related to energy, packet delay, throughput. However, the primary constraint of sensor networks has limited energy sources. An approach is required for enhancing the energy usage that leads to improving the network lifetime. An enhanced clustering protocol designed, and it has shown the energy -efficient in terms of reducing energy usage and extending the network lifetime. The proposed Adaptive Ranking based Energy Efficient Opportunistic Routing (AREOR) [8] protocol consists of two phases, one is an adaptive ranking mechanism to grade the Forwarder Nodes (FNs) and Cluster Head (CH), and the other one is the opportunistic forwarding scheme for optimal energy usage of each Sensor Node (SN). In the adaptive ranking technique, parameters highest residual energy and the minimum distance from the source node (Ns) are considered the electing CH in every transmission. The proposed protocol improves the network lifetime and leads to improvement in Message Success Rate (MSR), End-to-End delay (E2 delay), Packet Delivery Ratio (PDR), and Energy Consumption (EC). It was observed that the AREOR protocol showed the better statistical validation (rejects the null hypothesis) compared to traditional routing protocols with the total number of samples 30 and the Confidence Interval (CI) is 0.85 considered for simulation. © 2022 IEEE.

关键词： Energy utilization

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Classification and Discovery of Brain Tumor Using Ensemble Deep Learning 2

Classification and Discovery of Brain Tumor Using Ensemble D...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Srilatha, Kasanagottu Sravani, Chekka Vasudevarao, Mikkilineni Kiran, Ajmeera Chitra, D. Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Department of Data Science Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India Dayananda Sagar Business Academy Department Of Computer Applications Karnataka India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350372892

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor. © 2024 IEEE.

关键词： Convolutional neural networks

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