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A Review of Multimedia processing and its Application in MIDI

A Review of Multimedia Processing and its Application in MID...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Manan Gaurav Verma Shalini Singh Kamlesh Kukreti Department of Electronics and Communication Engineering Graphic Era University (Deemed to be) Dehradun India

ISBN: (纸本)9781665474177

The motive of Multimedia signal processing (MSP) is to integrate text, sound, image, and video information into a single communications channel, and to get high-quality communication. It also aims to provide an easy-to-use interactive platform to users worldwide. Musical Instrument Digital Interface (MIDI) is a digital language that allows the connection and interaction of multiple hardware and software instruments, computers and similar devices. This paper presents a review of multimedia processing and its application in MIDI technology to understand the processing of music signals, to find methods to extract desired parameters from those signals and convert them into digital MIDI data. The objective of this paper is to find existing research and scientific tasks performed by various researcher communities. Additionally, it seeks to discover novel uses for this technology in communications and to investigate uncharted territory in this area. Various collaborations of multimedia processing with technologies like Artificial Intelligence (AI), machine learning (ML), and Deep learning are currently under use and research. There are different types of multimedia, such as audio, video, text, graphics, etc. MIDI is mostly used for audio processing, but productive and efficient use of MIDI in other domains is still a challenge. This paper will help in finding new paradigm and possible solutions for the challenges of multimedia signal processing for MIDI applications.

关键词： Technological innovation Ultrasonic imaging Musical instrument digital interfaces signal processing algorithms Streaming media signal processing Speech enhancement

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2nd international conference on VLSI, Communication and signal processing, VCAS 2019

2nd International Conference on VLSI, Communication and Sign...

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2nd international conference on VLSI, Communication and signal processing, VCAS 2019

ISBN: (纸本)9789811568398

The proceedings contain 60 papers. The special focus in this conference is on VLSI, Communication and signal processing. The topics include: Rain Streaks Elimination Using Hybrid Median Filter and Contrast Stretching;design of Efficient Ternary Subtractor;A Multichannel Link-Layer Cooperation Protocol (MLCP) for Cognitive Radio Ad Hoc Network;Far Field Analysis of Defected Ground Structured Wideband Antenna for RF Energy Harvesting Applications;New LMI Criterion to the Robust Stability of Discrete-Time Systems with Time-Varying Delays and Generalized Overflow Nonlinearities;PAPR Reduction in OFDM for VLC System;performance Evaluation of Energy-Efficient Adiabatic Logic Circuit-Based Multiplexer for Low Power Applications;fault Detection and Classification in Microgrid Using Wavelet Transform and Artificial Neural Network;design and Performance Evaluation of Highly Efficient Adders in Nanometer Technology;α- η- μ/ IG Composite Fading Model for Body-Centric Communication;DTMOS-Based Low-Voltage Low-Power CCII+ and Biquad Filter Using ±0.25 V Supply;structural and Optical Analysis of Bulk-Hetero Interface Between MoS2: Pentacene;An Efficient Watermarking Process Based on Three-Level DWT and FFT Technique;blockchain-Enabled Traceable, Transparent Transportation System for Blood Bank;A Compendious Analysis of Advances in HE Methods for Contrast Enhancement;a Survey of Semantic Segmentation on Biomedical Images Using Deep learning;HT-IWT-DCT-Based Hybrid Technique of Robust Image Watermarking;Reliability and Circuit Timing Analysis with HCI and NBTI;Performance of 4H-SiC IMPATT Diode at Ka- and W-Band with Temperature Variation;realization of a Low Profile, Wideband Omni-directional Antenna for Ku-band Airborne Applications;effect of Structural Metal on Metamaterial-Based Absorber Performance;development of Cloud-Based Multi-Modal m-Cardiac Management System.

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machine learning Model to Predict Autism Investigating Eye-Tracking Dataset 2

Machine Learning Model to Predict Autism Investigating Eye-T...

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2nd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2021

作者： Akter, Tania Ali, Mohammad Hanif Khan, Md. Imran Satu, Md. Shahriare Moni, Mohammad Ali Jahangirnagar University Dept. of Cse Dhaka Bangladesh Gono Bishwabidyalay Dept. of Cse Dhaka Bangladesh NSTU Dept. of Mis Noakhali Bangladesh University of New South Wales Unsw Digital Health SydneyNSW2052 Australia

ISBN: (纸本)9781665415767

Autism spectrum disorder is a neurodevelopmental disorder that characterizes by reducing concentration on social activities and improving interest in non-social tasks. The aim of this work is to investigate eye gazing images and identify autism applying various machine learning techniques. Therefore, we collected eye-tracking data from the Figshare data repository. But, these scanpath images were almost similar for normal and autistic children. To obtain similar groups, k-means clustering method was used and generated four clusters. Further, several classifiers were applied into primary data and these clusters and evaluated the performance of them using various metrics. After the assessment of overall results, MLP shows the highest 87% accuracy in cluster 1. In addition, it shows the best area under curve, f-measure, g-mean, sensitivity, specificity, fall out and miss rate respectively. This predictive model could notably useful to forecast ASD status at early stages. © 2021 IEEE.

关键词： K-means clustering

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Automotive RADAR Human Classification Algorithm Through Simulation Analysis: Basics and Practical Challenges 2nd

Automotive RADAR Human Classification Algorithm Through Simu...

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2nd international conference on Computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2021

作者： James, Anto Jeyaprabu Jayavel, Kayalvizhi Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Information Technology School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789811698842

In the automotive domain, Advanced Driver-Assistance System (ADAS) sensors are game changers which bring science fiction movies into real life with the help of Automotive Radar and imaging sensors. This paper will summarize the types of radar, algorithms, and its features from the Advanced signal processing (ADSP) concepts. Especially in the urban roads, vehicle drivers need to make a quick decision on pedestrian classification behind other objects. Also, the pedestrians’ unique actions need to be predicted while crossing the road. There are multiple reasons where radars are unable to make a right decision, but behind the obstacles/objects scenario the Emergency Brake Assist (EBA) feature is more sophisticated to make a perfect decision better than humans (drivers). This paper will assist the researchers to visualize the open points behind the pedestrian nature and possible ways to improve the EBA feature. Also, this paper will help researchers to find the suitable algorithms and hardware from the automotive market. Hope this paper will also bridge the gap between academic researchers and industrial trends. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Research on Optimization System of machine learning Algorithm to Improve Wireless Power Transmission

Research on Optimization System of Machine Learning Algorith...

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Control, Electronics and Computer Technology (ICCECT), IEEE international conference on

作者： Taiqing Tang Xiwen Wei Peng Pang Cong Tian Heilongjiang University of Technology Jixi Heilongjiang China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

As wireless power transmission systems are widely used in modern communications, improving their performance and reliability has become an urgent task. However, frequent failures in the system pose challenges to communication quality and reliability. Traditional fault detection methods often rely on rules or manually defined features, which are difficult to cope with complex and changeable actual environments. To address this challenge, this study proposes a novel machine learning model named C-BiLSTM to improve the optimization of wireless power transmission systems. The C-BiLSTM model synergizes features from a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM). This sophisticated neural network effectively leverages the CNN's adept feature extraction and the BiLSTM's temporal modeling prowess for enhancing wireless power transmission. The transmission signals of the system are used for fault analysis and prediction. Specifically, the model accepts a one-dimensional transmission signal sequence as input, extracts local features through CNN, captures long-term dependencies through BiLSTM, and finally outputs fault classification or prediction results through a fully connected layer to provide a basis for subsequent fault processing. The C-BiLSTM model combines CNN and BiLSTM to accurately predict and analyze faults in wireless power transmission systems. The method of this study not only improves the accuracy of fault prediction, but also reduces the dependence on experts in related fields, which has higher practical significance.

关键词： Fault detection Bidirectional control Predictive models Feature extraction Electrical fault detection Wireless power transmission Convolutional neural networks

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Comparative Study of Deep learning Models for COVID-19 Diagnosis 2

Comparative Study of Deep Learning Models for COVID-19 Diagn...

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2nd international conference on Secure Cyber Computing and Communications, ICSCCC 2021

作者： Tomar, Muskan Arora, Tanishq Sabitha, A. Sai Hasteer, Nitasha Amity University Uttar Pradesh India

ISBN: (纸本)9781665444156

Corona Virus Disease 2019(COVID-19) spread far and wide in numerous nations in early 2020, causing the world to face an existential health crisis. This pandemic continues to have a devastating effect on the global population and by now it has infected more than a few million individuals around the world. One significant obstacle in controlling the spreading of this virus is that the initial system for addressing this infectious disease was not clear. A basic advancement in the struggle opposite the COVID-19 pandemic is early screening and dependable diagnosis utilizing computerized detection of lung infections. Computed Tomography (CT) scans and X-rays imagery offers great potential help to clinical specialists tackling COVID-19. An efficient Deep learning diagnosis application needs to be developed so that accurate and precise prediction can be done for the disease. This paper introduces dataset analysis and comparative evaluation of deep learning models for creating disease diagnosis application using image processing. Comparison is done using three main deep learning models-Convolutional Neural Network (CNN), Support Vector machine (SVM) Logistic Regression. Dataset analysis and model selection is a crucial phase for developing a predictive deep learning algorithm. This analysis is done for better results and is done using Orange data mining software. © 2021 IEEE.

关键词： Data mining

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Research on Transfer learning Technology in Natural Language processing 2nd

Research on Transfer Learning Technology in Natural Language...

引用

2nd international conference on Artificial Intelligence in China, ChinaAI 2020

作者： Shen, Ruilin Pan, Weimin School of Computer Science and Technology Xinjiang Normal University Urumqi830054 China

ISBN: (纸本)9789811585982

In the field of natural language processing, traditional machine learning methods rely on a large amount of labeled data to obtain better results and require training data and test data to satisfy the assumption of independent and identically distributed. Application of transfer learning technology provides a good solution to the problems above and promotes the research progress of natural language processing. This paper studied the application of transfer learning in natural language processing, made the definition of transfer learning, classified the transfer learning in natural language processing, and prospected the development of transfer learning in natural language processing. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Simulation Study of Financial Securities Market Volatility Model Based on BP Neural Network

Simulation Study of Financial Securities Market Volatility M...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Niu Xiaofang Beijing Junyu International Import and Export Trading Co. Limited Beijing China

With the continuous development and improvement of financial markets, the study of the nature and laws of the stock market has put forward higher requirements, of which predicting stock market volatility has become an important financial topic. The volatility of the stock market is its essential feature and attribute, but if the price volatility shows a sharp amplitude and too fast frequency, it may lead to the price distortion of the market, and then lead to the violent stock market shocks. Neural network is a powerful and widely used machine learning algorithm that is very good at discovering nonlinear relationships between data and is suitable for stock price prediction problems. The paper takes into account the factors influencing stock prices, extracts trading indicators and financial indicators, reduces the dimensionality through principal component analysis, and uses the reduced-dimensional principal components as the input variables of the network. Based on the actual stock trading market, this paper establishes a multilayer neural network classifier and uses four learning algorithms in BP neural network to fit the simulated data, and the results show that the algorithm has high accuracy for volatility pattern recognition. Under the modern fierce market competition, enterprises must strengthen the control of corporate financial risks and financial crisis prevention if they want to survive, develop and make profits.

关键词： Heuristic algorithms Sociology Neural networks signal processing algorithms Prediction algorithms Stock markets Statistics

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A Tree Based machine learning Approach for PTB Diagnostic Dataset 2

A Tree Based Machine Learning Approach for PTB Diagnostic Da...

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2nd international conference on Robotics, Intelligent Automation and Control Technologies, RIACT 2021

作者： Premanand, S. Narayanan, Sathiya School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

The primary objective of this particular paper is to classify the health-related data without feature extraction in machine learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based machine learning algorithms without extracting features like in Deep learning. This study performs better classification with Tree based machine learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of machine learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based machine learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Feature extraction

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Detecting Disease In Livestock and Recommending Medicines using machine learning

Detecting Disease In Livestock and Recommending Medicines us...

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international conference on Edge Computing and Applications (ICECAA)

作者： V. Lavanya A.Dinesh Kannan S. Kamaleshwaran R. Nishanth S.Muthu Kamatchi Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

Livestock is one of the critical socioeconomic assets in developing countries like India. However, the lack of a reliable and timely diagnosis system for identifying livestock diseases has led to significant losses in the livestock population, hindering efforts to achieve food security and reduce poverty in the country. To address this issue, a study proposed the integration of an expert system with machine learning and image processing. According to the 2019 Livestock Census, India has a total livestock population of around 535 million, which includes cattle, buffalo, sheep, goats, and pigs. The livestock sector is an important source of income for millions of households in India. As per the 2019 Livestock Census, there are around 145 million households involved in livestock farming and related activities in the country. The livestock sector contributes significantly to the Indian economy. In 2020, the total value of livestock output in India, excluding the value of horses, ponies, mules, donkeys, camels, and yak, was estimated to be around 9.37 trillion Indian rupees (around 125 billion US dollars), which accounts for around 4.2% of country's GDP. The dairy sector is a major contributor to the livestock economy in India. It accounts for around 70% of the total value of livestock output in the country. In addition to dairy, the livestock sector also provides meat, wool, leather, and other products. According to the latest available data from the Ministry of Statistics and Program Implementation, the contribution of the livestock sector to India's Gross Value Added (GVA) in 2020–21 was 5.04%. Since GVA is more accurate measure of the sector's contribution to the economy than GDP, it is concluded that livestock sector contributed around 5% to India's economy in 2020–21. According to a study by the National Dairy Development Board (ndDB) of India, there is an average of one veterinarian for every 5,000 cattle in India. This study improves the accuracy of livestock dis

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