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Anodyne for Piracy by Hue Anatomization

Anodyne for Piracy by Hue Anatomization

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Data, Decision and systems (ICDDs), IEEE International Conference on

作者： K N Pranav srinivasan D N sujatha Dept. of Information Science and Engineering New Horizon College of Engineering Bangalore India Dept. of Computer Applications B M S College of Engineering Bangalore India

Piracy is a malignant and deleterious parasite in the world of digital content. Here we are specific to piracy that occurs in movies through cam recorders and mobiles. Our solution aims to find the source of piracy among screening theaters to remediate the film industry and expunge piracy altogether. The method we use to solve this problem is hue anatomization, which uses hue and its properties to locate a theater distinctively from other theaters. The proposed method is studied with four progressions, Further with structural similarity Index Measure (ssIM), the results are accurate only if the window size it searches for is correct, this needs to be rechecked manually.

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A Natural Language Processing system for Truth Detection and Text summarization

A Natural Language Processing System for Truth Detection and...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： H P Rohith Kavitha sooda Karunakara b Rai D b srinivas Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

In the futuristic world, differentiating between accurate information and false information would be challenging. To identify the fake news a truth detection system will be extremely useful. Different artificial intelligence models were compared, and the highest performing algorithm was chosen. This model was subsequently updated using a larger data-set in order to achieve better results in more real-world applications. The most essential assumptions are that the user will only utilize the subject field in which the model will work. Earlier works have implemented the process of fake news and truth detection separately from the process of summarizing the said news/information. No method was available for streamlining the process to detect whether given information was true or not and to then summarizes the given information automatically. This study has compared and contrasted the differences in accuracy and overall performance of each algorithm for fake news detection and summarization of text. Using smaller dataset, LsTM cells outperformed all other models with an accuracy of 99%. For the larger dataset, the modified LsTM has worked really well with 98.6 percent accuracy.

关键词： Industries Computational modeling Neurons Natural language processing Classification algorithms Fake news Artificial intelligence

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Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network for Autonomous vehicle Drivers 2

Automated Facial Emotion Detection using Arithmetic Optimiza...

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2nd International Conference on Automation, Computing and Renewable systems, ICACRs 2023

作者： saranya, R. vijayaragavan, M. Ismail Kalilulah, s. satyanarayana, K.N.v. suresh, M. srimathi, s. CSE Department R.M.K. College of Engineering and Technology India Department of EEE Mailam Engineering college Tindivanam T.kVillupuram Dist Mailam India Computer Science and Engineering Dr. MGR Educational and Research Institute Periyar E.V.R. High Road Maduravoyal Chennai600095 India Dept of ECE S.R.K.R.ENGINEERING COLLEGE Chinaamiram A.P. Bhimavaram India Department of ECE Rajarajeswari College of Engineering Bangalore560074 India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Facial Emotion Recognition (FER) utilizing Deep Learning (DL) is indeed employed in the context of autonomous vehicle drivers. by investigating the facial expressions of drivers, the vehicle system is gain insights into the emotional state of drivers. FER is given to whole safety by identifying critical emotions like fear, anger, or surprise. When the system identifies these emotions, it responds accordingly by altering the driving performance or taking preventive measures for avoiding potential risks or aggressive maneuvers. The training of the DL algorithms namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are done on the preprocessed data. These approaches learn to extract appropriate features in facial images and forecast the same emotions. This manuscript offers the design of Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network (AFED-AOADCNN) technique for Autonomous vehicle Drivers. The purpose of the AFED-AOADCNN technique is to detect various kinds of facial emotions in autonomous vehicle drivers. In this introduced AFED-AOADCNN technique, DCNN method is applied for procedure of feature extraction. Next, the AOA is employed for optimum hyperparameter tuning of the DCNN method. At last, quantized neural network (QNN) approach is exploited for the identification and classification of different kinds of facial emotions. The investigational evaluation of the AFED-AOADCNN method is assessed on facial emotion dataset. The experimental validation stated the improved outputs of the AFED-AOADCNN method over recent approaches. © 2023 IEEE.

关键词： Arithmetic optimization algorithm Autonomous vehicle drivers computer vision Deep convolutional neural network Facial emotion recognition

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Improving solar Efficiency via CNN-LsTM and Cloud-Integrated IoT Prediction

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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sentiment Analysis and Deep Learning (ICsADL), International Conference on

作者： P varun T. sunitha Mythili Nagalingam P. Nithiya s selvakumaran T A Mohanaprakash Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Department of Computer Science and Engineering St. Joseph's Institute of Technology OMR Chennai Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Department of Computer Science and Engineering P.B college of engineering Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Surapet Chennai Department of Computer Science and Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LsTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities.

关键词： Training Photovoltaic systems Cloud computing Predictive models Inverters Data models smart grids

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IPFs: An Off-Chain storage solution for blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, shikha Kumar, Deepak Raboaca, Maria simona Goyal, s.b. verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File system (IPFs) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFs that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFs, an efficient file sharing system would be created. This paper presents a comprehensive IPFs architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： blockchain

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Machine Learning Techniques For Twitter Data Using sentimental Analysis

Machine Learning Techniques For Twitter Data Using Sentiment...

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Innovation and Novelty in engineering and Technology (INNOvA), International Conference on

作者： Kanchan Wangi Anupama Inamdar Department of Computer Science and Engineering BLDEA's V. P. Dr. P. G. Halakatti College of Engineering and Technology Vijayapura Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

In current era, the expressive growth of social media platforms like Twitter has made it an invaluable source for understanding public opinion and sentiment. This paper discovers various machine learning techniques applied to twitter data for sentiment analysis. The study also investigates into the preprocessing steps essential for Twitter data, such as tokenization, stop-word removal, handling emojis and dealing with slang. The results suggest that machine learning models, especially those utilizing word embeddings and attention mechanisms, outperform traditional models in capturing the nuanced sentiment expressed in tweets. Feature extraction techniques such as bag-of-Words (boW), TF-IDF are to represent the textual data in format suitable for machine learning algorithms. Investigated the effectiveness of different machine learning models including Logistic Regression, bernoulli Naive bayes, support vector Machine, K Nearest Neighbors, and Random Forest for sentiment classification. Evaluated using recall and F1 score, experimental results show efficacy of proposed model for twitter data.

关键词： support vector machines sentiment analysis Logistic regression Machine learning algorithms social networking (online) blogs Machine learning Feature extraction bayes methods Random forests

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Enhancing Natural Language Processing with Deep Learning for sentiment Analysis 9

Enhancing Natural Language Processing with Deep Learning for...

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9th International Conference on science, Technology, engineering and Mathematics, ICONsTEM 2024

作者： Om Prakash, C. Kumar, s. vijaya Pallavi Abbas, Jamal K. Naser, sarmad Jaafar Haroon, Noor Hanoon School of Management Cmr University 5 Bhuvanagiri OMBR Layout Karnataka Bengaluru560043 India B S Abdur Rahman Crescent Institute of Science And Technology Chennai Tamil Nadu Vandalur India Chaudhary Ranbir Singh University Department of English Haryana Jind India AL-Nisour University College Baghdad Iraq College of Pharmacy National University of Science And Technology DhiQar Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq

ISBN: (纸本)9798350365092

Natural language processing (NLP) aids in the advancement of intelligent machines through its emphasis on etymologically grounded human-PC connections and a greater understanding of the human language. The demand and necessity to implement information-driven methodologies for automating semantic analysis have increased due to ongoing advancements in processing power and the accessibility of vast quantities of phonetic data. The application of deep learning methods has facilitated significant progress in fields including computer vision, programmed discourse recognition, and natural language processing, which has led to the pervasive adoption of information-driven techniques. This study investigates calculations for unimodal and multimodal sentiment analysis in interpersonal organisations and develops two models, one for message sentiment analysis and the other for picture-message multimodal sentiment analysis in informal communities. The two models demonstrated their electiveness by surpassing the benchmark models by 4.45% and 5.2%, respectively, when evaluated against the current models on distinct datasets. © 2024 IEEE.

关键词： sentiment analysis

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Deep Learning-based Diagnosis system for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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Artificial Intelligence and Emerging Technology (Global AI summit), International Conference on

作者： N. sabiyath Fatima Ramendra singh Akuthota swathi Amar Choudhary Azhar Ahmed Chhaya sharma Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India Department of Computer Science and Engineering (IoT) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science and Engineering CVR College of Engineering Mangalpalli Rangareddy India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering KCC Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, vGG 16, vGG 19, and Inception v3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that vGG 19 provides the highest diagnostic accuracy of 98.76 % , with vGG 16, Inception v3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted vGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies.

关键词： Deep learning Training Liver cancer Analytical models Accuracy Computational modeling Confusion matrices computer architecture Feature extraction Testing

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Design and Development of an IoT based Intelligent Energy Meter using Less Expensive Controller Unit

Design and Development of an IoT based Intelligent Energy Me...

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Intelligent systems for Cybersecurity (IsCs), International Conference on

作者： M. balasubramani b. Gohin J. Jayashankari v. samuthira Pandi Kiran Chand Ravi Deepak Arumugam Department of Computer Science and Engineering V.S.B Engineering College Karur Tamil Nadu India Department of Computer Applications Swarnandhra College of Engineering and Technolnogy Seetharamapuram Andhra Pradesh India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of Electrical and Electronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

With an eye towards improving the efficacy of energy monitoring and management, this research offers a thorough approach for the creation of an Intelligent Energy Metre that is based on the Internet of Things. A key component of the system is the use of specialized sensors to continuously measure electrical characteristics like voltage and current. These sensors generate analog signals, which are conditioned, digitized, and processed by a microcontroller. Algorithms within the firmware/software calculate energy consumption metrics, which are transmitted wirelessly to a central server or cloud platform using secure communication protocols. The collected data is then stored and analyzed to provide insights into energy usage patterns, facilitating informed decision-making for optimizing energy efficiency and reducing costs. The methodology encompasses various stages, including market research, hardware and software development, integration, testing, deployment, and maintenance. Through the implementation of this methodology, a robust and scalable IoT-based energy monitoring system can be developed, empowering users to make informed decisions for sustainable energy management.

关键词： Energy consumption Protocols voltage measurement sensor phenomena and characterization sensor systems Real-time systems Internet of Things

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HAR Using bi-directional LsTM with RNN

HAR Using Bi-directional LSTM with RNN

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2021 International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2021

作者： singh, Nikhil Kumar suprabhath, Koduru sriranga Computer Science Engineering M.V.S.R.Engineering College Hyderabad India Electrical and Electronics Engineering Mahindra University Hyderabad India

ISBN: (纸本)9781665415590

It is hard to monitor human activities in various contexts like security surveillance, healthcare, and human-computer interaction. Human Activity Recognition is the process of predicting what a person is doing based on the traces of their movement. We propose using deep recurrent neural networks (DRNNs) for building recognition models capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LsTM), DRNNs and evaluate their effectiveness on various benchmark datasets. Long shortterm memory (LsTM) is an artificial deep recurrent neural network (DRNN) architecture used in deep learning, especially for time series prediction. It can process single data points (such as images) and entire data sequences (such as speech or video). LsTM networks are well-suited for classifying, processing, and making predictions based on time series data since there can be lags of unknown duration between essential events in a time series in real-time. We proposed Human Activity Recognition (HAR) using a smartphone dataset and LsTM. Compared to a classical approach, using Deep Recurrent Neural Networks (DRNN) with Long short-Term Memory cells (LsTMs) require no or almost no feature engineering. Data can be fed directly into the neural network, which acts as a black box, modeling the problem correctly. This means that the neural networks are almost always able to identify the movement type correctly. We used jupyter notebook with python 3.7+. © 2021 IEEE.

关键词： smartphones

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