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Performance Comparisson Activity recognition using Logistic Regression and Support Vector machine

Performance Comparisson Activity Recognition using Logistic ...

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international conference on Intelligent Autonomous Systems (ICoIAS)

作者： Agus Eko Minarno Wahyu Andhyka Kusuma Hardianto Wibowo Informatics Engineering Universitas Muhammadiyah Malang Malang Indonesia

ISBN: (数字)9781728160788

ISBN: (纸本)9781728160795

Daily activities has become leading problems in human physical analysis. Autonomous system as application in several area of human physical analysis was increase along with several machine learning methods. Fall detection, medical rehabilitation or other smart home application in physical analysis application has increase degree of life. Accelerometer and gyroscope was popular sensor for physical analysis. Several research was used these sensor with various position in human body part. Activities was separated in three class, static activity, transition activity, and dynamic activity. Basic activities has same pattern in each activity. From public HAR dataset, wich have three static activity (standing, sitting, and laying) each pattern has same shape and patterns. dataset were used in this paper have acquire from 30 volunters. Seven basic machine learning alghoritm Logistic Regression, Support Vector machine, Decission Tree, Random Forest, Gradien Boosted and K-Nearest Neighbor. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both method has same accuracy. Likewise for result where this main problems static activity was successful detected with logistic regression and SVM.

关键词： Support vector machines Accelerometers machine learning Gyroscopes Robot sensing systems Kernel Legged locomotion

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Prediction of Efficient Driving Mode for Vehicle using machine learning

Prediction of Efficient Driving Mode for Vehicle using Machi...

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Mysore Sub Section international conference (MysuruCon), IEEE

作者： Ramakrishna Hegde Sujan D Kripa Das Sriraksha M S Siri R Siri Gowri H Soumyasri S M Computer Science Engineering Vidyavardhaka College of Engg. Mysuru India Computer Science Engineering PES University Bengaluru India Master of Computer Applications The National Institute of Engineering Mysuru India

The field of autonomous driving cars is a rapidly growing area of research, machine learning and image recognition technology plays a pivotal role in ensuring their safe navigation on the roads. Different driving modes are offered by modern cars to accommodate various road conditions and driver preferences. However, manually selecting these modes can be cumbersome. Addressing the dynamic nature of mode switching in autonomous driving systems presents a multifaceted challenge. The system must continuously assess a multitude of variables such as traffic patterns, road conditions, weather conditions, and the wear and tear of vehicle components. Traffic conditions can change rapidly, requiring real-time analysis of traffic flow and congestion. Road conditions, including the presence of obstacles, construction zones, or slippery surfaces, demand instant adaptation for safe navigation. Weather conditions further complicate the scenario, as rain, snow, fog, or even intense sunlight can impact visibility and road grip. Addressing these challenges requires sophisticated algorithms capable of processing vast amounts of data from various sensors in real time. machine learning models must be trained to recognize patterns in diverse conditions, enabling the system to make informed decisions swiftly and accurately. Moreover, the system must possess the ability to prioritize these factors based on their immediate impact on driving safety and efficiency. This can be a future enhancement of the paper. By leveraging machine learning algorithms, the mode selection process can be automated. The system constantly monitors driving conditions, learns from real-time camera sensor data and selects the most optimal mode to adapt to current situation. Different models such as Convolutional Neural Network (CNN), CNN with MobileNetV2 as base and decision trees are analyzed.

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Co-Extracting Feature and Opinion Pairs From Customer Reviews Using Hybrid Approach

Co-Extracting Feature and Opinion Pairs From Customer Review...

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international conference for Convergence of Technology (I2CT)

作者： Sugandha C. Nandedkar Jayantrao B. Patil Deogiri Institute of Engg. and Mngt. Studies Aurangabad India R. C. Patel College of Engg. Shirpur India

The Internet and WWW have turned today's world into global village. Over the years technology has significantly changed the way people communicate. How they feel, what they like or dislike, people like to share it online. Proper mining and analysis of public feelings or opinions can bring a miracle to existing business profit. For the same purpose instead of exclusive public opinion polling, machine learning tools are preferred. Few researchers are trying to interpret it with the help of linguistic approach. But the reviews or opinion of the customers' also known as user-generated content has the characteristics such as incomplete, grammatically incorrect, weakly structured etc. This causes degradation in the performance of the opinion mining task. Whereas, few researchers are trying to apply statistical measures for the same. Both the methods have their own pros and cons. In this paper, we have proposed a hybrid approach by integrating both the linguistic as well as statistics approach into a single unified framework. The key objective of our task is to co-extract the feature and opinion pair from customers reviews. Let's view the unstructured corpus a combination of a set of grammatically correct and a set of grammatically incorrect sentences. For the grammatically correct sentence, use syntactic pattern-based rules to extract the features and opinion pairs. For the set of sentences not interpreted by the syntactic parser, apply word alignment technique to extract probable feature opinion pair. Further, we determine the aspect of each probable feature and calculate the opinion association among these words. Finally, we calculate the soundness of extracted feature opinion pair with the document. For experimentation purpose, we used customer review dataset of five different products. Through the analysis, a high accuracy is achieved by the hybrid approach.

关键词： Feature extraction Syntactics Linguistics Task analysis data mining Statistical analysis User-generated content

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Human Activity Tracking Using Mobile Sensor data and an Optimised LSTM 3rd

Human Activity Tracking Using Mobile Sensor Data and an Opti...

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3rd international conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Anandarao, Sarvani Mastani, Shaik Geeta, Tata Vamsi, Bolleddu Reddy, Puritipati Sai Santhosh Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819705726

Since many years smartphones are utilised for human activity recognition (HAR), important healthcare recommendations and telemedicine. Deep learning (DL) and machine learning techniques are commonly employed in studies of statistical models of human behaviour. However, the performance of present HAR platforms is constrained by complex physical activities. In this study, we developed the Ada-HAR human activity identification and real-time monitoring system, which is able to recognise more human motions in erratic situations. The chosen hierarchical clustering and classification methods are able automatically identify and categorise 12 activities (five dynamics, six statics, and a series of transitions). Finally, actual tests were run to verify the effectiveness and reliability of the suggested methods. The results demonstrate that the DL-based classifier achieves a higher identification rate (95.15% for waist and 92.20% for pocket) in comparison to the techniques discussed in the literature. Finally, the Ada-HAR system can track human behaviour in real-time regardless of how the smartphone is pointed. Here sensor activity dataset is considered which consists of user's activity log. By applying algorithms named LSTM, Adamax, Adagrad, SGD, indentifiesExi user’s activity based on the movement of the mobile. Existing Algorithms named LSTM, K-NN, DT, ANN, SVM, NB applied on different dataset produced better result and accuracy. RNN is an updated version of LSTM. RNN stores only the current activities and fails to store previous conclusions. Fortunately, LSTM stores existing and completed work in any dynamic situations. The LSTM works with random weights which led to local optimum and high time complexity. This paper has addressed the above issue by the usage of optimization algorithms in weight updation of LSTM. This increase the accuracy of the model. Here Adamax, Adagram, Stochastic gradient descent are used for weight updation. © The Author(s), under exclusive license

关键词： Smartphones

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Mobile Forensics in Digital World using Artificial Intelligence

Mobile Forensics in Digital World using Artificial Intellige...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Avani Dwivedi Ujjwal Kumar Singh Samta Kathuria Jayapal Lande Prafful Negi Law College Dehradun Uttaranchal University Dehradun India LCD Division of Research & Innovation Uttaranchal University Dehradun India School of Computer Science & Artificial Intelligence SR University Warangal India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

After the advent of industry 4.0, technologies are comprehensively unveiled to the public. Because of this, the proliferation in demand for technology in every field has begun to soar. Today, in the modern society, the pattern of crimes against women is increasing unprecedentedly. Cybercrimes are more frequent than traditional crimes. Therefore, Mobile Forensics (MF), a subset of digital forensics plays a crucial role in extracting the digital evidence. The technologies such as Artificial Intelligence (AI) is then incorporated to improve the efficiency and accuracy in mobile forensics and digital investigation. This study addresses the significance, challenges, and effects of integrating AI in Mobile Forensics and Digital Investigation (DI). In addition, this research study also emphasizes on the stages or directives of mobile forensics, which are considered as essential components in gathering the evidence. It fills the void by analyzing how various technologies and techniques such as machine learning, deep learning, and data mining can be used in mobile forensics. Eventually, this study is concluded by providing the suggestions for its advancement and avoidance while illustrating the challenges to negate the same in the future.

关键词： Deep learning Sentiment analysis Law enforcement Forensics Instruments Transforms Organizations

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Privacy issues involved in data mining and countermeasures

Privacy issues involved in data mining and countermeasures

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MEMAT 2022;2nd international conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology

作者： Jing Liu

data mining can extract valuable information from large amounts of data and has matured in recent years as an important resource for productive life. This technology is a decision support process. It is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, database, visualization technology, etc. It is highly automated to analyse a company's data, make inductive inferences and extract potential patterns from it to help decision makers adjust their market strategies, reduce risk and make the right decisions. Technology is neutral and not entirely good or bad. However, with the rapid development of the Internet, the increasing popularity of embedded and location-based technologies and the widespread use of location-based social networks, the amount of personal privacy information has increased dramatically and it is very easy to violate personal privacy if data mining tools are applied directly to the data for comprehensive analysis. Thus, in order to be able to carry out data mining with privacy protection, this article suggests some solutions, including: 1) Establish a perfect data privacy protection assessment mechanism and legal means.; 2) Develop a new theoretical framework for privacy protection; and 3) Efficient encryption algorithm.

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Motor Imagery EEG-based Control of Intelligent Wheelchair Using Deep Belief Network Coupled with OVO-CSP Algorithm

Motor Imagery EEG-based Control of Intelligent Wheelchair Us...

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international conference on data Science and Business Analytics (ICDSBA)

作者： Hongsen Zhou Guolong Zhang School of Computer Science Jiangxi University of Traditional Chinese Medicine Nanchang Jiangxi

ISBN: (数字)9781728146447

ISBN: (纸本)9781728146454

Aiming at the shortcomings of low recognition rate and the cumbersome feature extraction of traditional machine learning methods during the process of controlling wheelchair with electroencephalography(EEG) signals, this paper presents a scheme for controlling intelligent wheelchair with multi-class motor imagery (MI) EEG based on deep learning framework. The representative model of deep learning deep belief network (DBN) was used to classify the MI EEG. Firstly, the improved OVO-CSP algorithm is used to extract the features of multi-class EEG. Then, five kinds of MI EEG signals are trained and classified by DBN. Finally, the comparison with traditional machine classification methods such as SVM and BP Neural Network proves the effectiveness and validity of the proposed method. The results show that the deep belief network can better extract the essential characteristics of multi-class EEG and improve the classification accuracy.

关键词： Support vector machines Deep learning Wheelchairs Neural networks Feature extraction Electroencephalography Classification algorithms

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Empirical Evaluation on Stock Market Forecasting via Extreme learning machine

Empirical Evaluation on Stock Market Forecasting via Extreme...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Balakrishnan S Deepak Kumar Pathak Jeidy Panduro-Ramirez Dharam Buddhi B. Girimurugan Hemant Singh Pokhariya Department of Commerce Faculty of Science and Humanities SRM Institute of Science and Technology Chennai Tamil Nadu Faculty of Computer Applications Invertis University Bareilly Universidad Tecnológica del Perú Uttaranchal Institute of Technology Uttaranchal University KL Business School Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Stock market forecasting is a crucial element of financial systems, yet it may be tough to anticipate marketplace trends because of the loud & non-stationary nature of the data. Deep learning is known for extracting superior conceptual properties from massive amounts of actual data without relying upon previous information, which may be useful in predicting financial activities. This paper seeks to offer a deep learning system that automatically mines the statistic laws of material and directs stock market operations using bottom neural network models and empirical mode deconstruction. This basic pattern was discovered but then dissected using the data's typical timeframe. data from stock market transactions were examined, PSO-optimized, and forecasted. Prediction efficiency can be increased by merging non stationary and exponential financial time series. If a specific trust level is met, the deep learning prediction, which is based on the study of enormous amounts of financial trade data, can determine the future trend and financial markets pricing. The empirical findings demonstrate the superior predictive efficiency of the EMD-based deep learner *** aim of this paper is to conduct an empirical evaluation regarding stock market forecasting using extreme learning machine. A detailed research methodology has been discussed in this study to conduct the research efficiently. Moreover, a thorough practical analysis and discussion was also included along with statistical interpretation to get desired research outcomes.

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A comparative study of machine vision-based rail foreign object intrusion detection models

A comparative study of machine vision-based rail foreign obj...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Xinnan Cai Xuewen Ding Tianjin University of Technology and Education Tianjin China

Due to the lack of track foreign body intrusion dataset, classical target detection models are rarely used in the field of foreign body intrusion on railway tracks, and model comparison experiments are also insufficient. Aiming at these problems, this paper makes a comparative study on the application of yolov5 and fast RCNN in railway foreign object intrusion detection. First, the train and test dataset was established by image preprocessing, data cleaning and data labeling of UAV aerial images. Second, the canny edge detection algorithm combined with Hough transform was used to extract the track features for delineating the detection area. Finally, Yolov5 and fast RCNN, two widely used models, were used to train and test respectively based on our dataset for comparative studies. Experiment results show that YOLOv5 has better comprehensive performance in detection rate and detection speed, and Faster RCNN model cannot meet the requirements of real-time detection of track foreign objects intrusion.

关键词： Deep learning Target tracking Target recognition Image edge detection Intrusion detection Object detection Transforms

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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： K. Ananthi M. Balakrishnan M. Pandi N. Senthil Madasamy Artificial Intelligence &Data Science Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India Computer Science and Engineering Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India

In data mining and pattern recognition, the Support Vector machine is an extensively used classification technique. The tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. The problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. The experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library.

关键词： Measurement Fuzzy logic Weight measurement Training Computer bugs Support vector machine classification machine learning

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