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Application of machine learning in Crime Prediction 5

Application of Machine Learning in Crime Prediction

引用

5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Hussain, S K Saddam Reddy, T Udayabhanu Kiran, D Sai Samreen, S.K. Institute of Aeronautical Engineering Dept. of Information Technology Hyderabad India

ISBN: (纸本)9781665435246

the criminal cases in India are increasing at a faster pace. Due to this reason, the number of pending cases are piling up. Due to this increase in the number of pending cases it has become a difficult task to solve these cases. For the prevention of future crimes, the identification of the crime patterns of an area is very important. Different criminal solving agencies can be better at providing a good understanding of the pattern and pattern of criminal activity in a specific domain. machine learning can be used to find criminal activity patterns using different algorithms in a particular location. Criminal data are used to predict crimes in an area that helps our country to reduce crime rates. © 2021 IEEE.

关键词： Crime

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IoT-Enabled Intelligent System for Helmet Detection and Automated Penalty Enforcement using AI & ML

IoT-Enabled Intelligent System for Helmet Detection and Auto...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P. Vishnu Kumar Nandasree Shathanakota Saimanaswini Rachamadugu Uppara Layavardhini Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the increasing number of road accidents due to non-compliance with helmet regulations highlights the need for intelligent systems to enhance rider safety. this paper presents an IoT-enabled system that utilizes Artificial Intelligence (AI) and machine learning (ML) to detect riders without helmets and enforce penalties automatically. the system integrates a Raspberry Pi with a camera module to capture real-time images of motorcyclists at traffic signals. AI-powered object detection models, such as YOLOv5, determine helmet usage, while Optical Character recognition (OCR) techniques extract license plate details for violators. the system transmits violation data to a centralized server via IoT communication protocols, where an automated penalty system processes the information and notifies the rider through SMS or email. By automating helmet detection and penalty enforcement, this system enhances road safety, reduces manual intervention, and ensures adherence to traffic regulations.

关键词： Head Road accidents Manuals machine learning Road safety Regulation Real-time systems Safety Intelligent systems Monitoring

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Intelligent IoT System with AI and ML Integration for Smart Home Automation

Intelligent IoT System with AI and ML Integration for Smart ...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P. Vishnu Kumar Allam Prathyusha Nowly HimaBindu Palukuru Manaswini Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the integration of Artificial Intelligence (AI), machine learning (ML), and the Internet of things (IoT) is transforming smart home automation by enhancing security, energy efficiency, and user convenience. this paper presents an AI-powered IoT system that employs real-time monitoring and adaptive automation to optimize household operations. the system utilizes ultrasonic sensors for water level monitoring, motion sensors for automated lighting and fan control, and DHT11 sensors for climate regulation. AI-driven facial recognition ensures secure access control, while predictive energy management reduces electricity consumption by dynamically adjusting appliance usage. the system effectively detects hazards such as gas leaks and fires in real time, triggering immediate alerts and safety measures. With seamless device coordination and automated responses, this framework enhances home security, energy efficiency, and overall automation reliability. Despite challenges in data security and system integration, the findings highlight that AI-ML-driven IoT automation is a viable solution for creating self-sustaining and intelligent smart homes.

关键词： Automation Face recognition Smart homes machine learning Real-time systems Energy efficiency Sensor systems Hazards Internet of things Intelligent sensors

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An Efficient Techniques for Disease Prediction from Medical data Using data mining and machine learning 5

An Efficient Techniques for Disease Prediction from Medical ...

引用

5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Agrawal, Bharat Bhushan Abdulmughni, Husam H. Al-Bakhrani, Ali A. Nage, Pranoti Durgadas Jaiswal, Sushma Tripathi, Vikas IFTM University Department of Computer Science & Engineering Uttar Pradesh Moradabad India Sana'a University Faculty of Computer and IT Department of Information Technology Sana'a Yemen Sana'a Yemen Faculty of Administrative and Computer Sciences Albaydha University Technique Leaders College Department of Computer Science Yemen Mumbai India Bilaspur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Individuals are susceptible to numerous ailments in today's global setting, with people living highly automated lives under tremendous job pressure, both at home and at work. Such disorders have recently been on the rise at an alarming pace. As a result, the healthcare business must assume a prominent position soon, accountable for people's health, a better society, and a successful country at large. Healthcare prices are rising as the demand for health amenities grows. Healthcare facilities with improved detection, diagnostics, and treatment procedures are desperately needed. With growing digitization and computing techniques in place, a massive amount of data is being generated and used for diagnostic and detection approaches. this data might be used to obtain data for anticipating disease, commencing preventative measures, and improving treatment procedures long before the diseases progress. Powerful computing technologies must be used to build the finest intelligence of forecasting and decision-making abilities evaluated by an expert. the purpose of this paper is to examine the effectiveness of various classification techniques, such as Naive Bayes (NB), J48, REF Tree, Metaheuristic Optimization (SMO), and Linear regression (LR), on data sets pertaining to cardiovascular disease (CVD). Employing the WEKA tool, an analysis was designed to test the efficacy of several strategies using illness data sets obtained from UCI respiratory. Different categorization methods are used to compare the values of various metrics, such as the percentage of right classifications, the recall, the F-measure, and the time required. © 2022 IEEE.

关键词： Forecasting

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Automatic Optimization of Variational Quantum Algorithm-Based Classifiers 22

Automatic Optimization of Variational Quantum Algorithm-Base...

引用

Proceedings of the 2022 5th international conference on Artificial Intelligence and pattern recognition

作者： Ruihong Jia Guang Yang Min Nie Yuanhua Liu Meiling Zhang Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the advent of the era of big data, how to efficiently process data has become an urgent problem. Although classical machine learning algorithms are very mature in processing data, they require a large amount of data to complete the training process. Quantum machine learning uses the superposition, entanglement, parallelism and other characteristics of quantum computing to improve the performance of classical machine learning algorithms. At the same time, circuit optimization of the quantum classifier can reduce the computational complexity. this paper proposes an automatic optimization model of the classifier based on the variational quantum algorithm, which reduces the complexity of the parameter update process through the deep optimization of the core circuit. this model greatly speeds up the training process and has lower complexity than other quantum classifiers. In addition, a unique regularization method is used to avoid overfitting. the simulation results show that the classification of some typical data sets can achieve better classification results, and the training parameters and training speed are significantly better than other quantum classifiers. Finally, the robustness of the classifier is further verified in the presence of noise.

关键词： Automatic optimization machine learning Quantum classifiers Quantum computing

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Exploratory Analysis of machine learning Methods for Total Organic Carbon Prediction Using Well-Log data of Kolmani Field

Exploratory Analysis of Machine Learning Methods for Total O...

引用

pattern recognition Systems (ICPRS), international conference on

作者： Fodio S. Longman Habeeb Balogun Rasheed O. Ojulari Olaniyi J. Olatomiwa Husaini J. Balarabe Ifeanyichukwu S. Edeh Olabisi O. Joshua Dept. of Innovation Led Research (ILR) Nigerian National Petroleum Company (NNPC) Limited Abuja Nigeria School of Computer Science and Engineering University of Westminster London United Kingdom

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

machine learning methods have shown significance in automating the prediction of Total organic carbon (TOC) for determining source rock potential in oil and gas exploration. Higher TOC contents could indicate greater potential for oil and gas generation. Making accurate TOC predictions, therefore, is crucial in measuring hydrocarbon deposits for a prospective geological formation. Automating this procedure can save time and resources when compared to conventional geochemical methods. In this study, we explore machine learning methods on a Frontier-Basin well-log data for TOC Prediction. Firstly, we employ feature selection methods to ensure optimal feature usage in regression and classification tasks. Additionally, we compare several supervised machine learning methods including Logistic Regression, Decision Tree, K-Nearest Neighbor, Gradient Boosting, and Naive Bayes Methods to categorize the quality of TOC using its defined standard ranges on the well-log data of Kolmani River 2 and 3, respectively. Furthermore, Random Forest method was utilised for the regression task on both data. A 5 fold nested cross validation was utilised for both classification and regression task. We show an exploratory analysis and the prospect of using machine learning methods to effectively classify TOC distribution using continuous well log data. Results show that machine learning methods are efficient for TOC prediction for non-geochemical approaches. the regression analysis shows an acceptable R 2 value of 0.62 and 0.76 for the respective welllogs with an MSE score of 0.05 and 0.07, respectively. For the classification task, evaluation metrics including F1 score, Precision, Accuracy, and Recall were investigated for the different ML classification methods, and Naive Bayes’ was concluded to outperform others using standard metrics.

关键词： Accuracy Oils machine learning Nearest neighbor methods Rocks Rivers Naive Bayes methods

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A Deep learning Approach for Bangla Speech to Text Conversion 10

A Deep Learning Approach for Bangla Speech to Text Conversio...

引用

Joint 10th international conference on Informatics, Electronics and Vision, ICIEV 2021 and 2021 5th international conference on Imaging, Vision and pattern recognition, icIVPR 2021

作者： Adhikary, Raima Fatema, Kaniz Sifat-E-Jahan Yousuf, Mohammad Abu Institute of Information Technology Jahangirnagar University Bangladesh

ISBN: (纸本)9781665449212

One of the most remarkable advantages of speech recognition (SR) technology includes speech to text conversion (STT). this paper's focal point is to recognize Bangla words among various speakers and noisy environments and convert them into text. Mel frequency cepstral coefficient (MFCC) has been used for extracting words from the audio file. Using gated recurrent units (GRU) along with directional long short term memory (LSTM), the continuity of words and next word-level prediction in a sentence have been implemented. An existing dataset that is enriched by recorded speech has been used here. Keras-Tensorflow toolkit has been used as a software apparatus for training data. the highest train accuracy has been obtained from the GRU architecture in the acoustic model (AM) is 94.44% while the test accuracy rate is only 47% due to the small test dataset. On the other hand, test accuracy is maximum up to 45.81% with the LSTM model in the language model (LM). the proposed method signifies good training and testing accuracy rate based on applying GRU model to the AM, LSTM to the LM and the numbers of total recordings of each sentence. Contribution-A GRU model along with a LSTM model is designed for speech-to-text conversion in continuous Bangla speech. © 2021 IEEE

关键词： Long short-term memory

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A Review of Research Based on Generative Adversarial Networks 22

A Review of Research Based on Generative Adversarial Network...

引用

Proceedings of the 2022 5th international conference on Artificial Intelligence and pattern recognition

作者： Tianyi Zhu Huan Liu Xi'an University of Posts & Telecommunications China

ISBN: (纸本)9781450396899

In recent years, with the rapid development of deep learning, Generative Adversarial Network (GAN), as one of the unsupervised learning, has also gradually developed into one of the current research hotspots in the field of deep learning. GAN has more powerful feature learning and feature representation capabilities than traditional machine learning algorithms. GAN is currently showing great potential in deep learning, especially in the field of computer vision. In this paper, we discuss and analyse the basic principles of the basic GAN model and its advantages and disadvantages; introduce the derivative models of GAN and their limitations; summarise the application results of GAN in various fields such as image reconstruction, text description image generation, audio generation, migration learning, etc. Finally, this paper also presents some prospects for future research directions in this field.

关键词： data generation Generative Adversarial Networks, Review

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Multilingual Language Detection and Translation System with Multinomial Naive Bayes

Multilingual Language Detection and Translation System with ...

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： Sowjanya Vuddanti Dhathri Sri Lekha Ariveni Niharika Sri Sandhya Kethepalli Lokesh Manchimi Jaswanth Yajjavarapu Department of Artificial Intelligence and Data Science Lakireddy Bali Reddy College of Engineering Mylavaram Andhra Pradesh India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Smooth communication becomes essential as the world unites different linguistic groupings. the proposed system for language recognition and translation addresses this difficulty. the proposed system uses a Google Translate-powered “bridge” with a machine learning “detective” to properly identify text language and translate between any supported combination. the Multinomial Naive Bayes classifier, a machine learning approach, is used by the language detection component to determine the language of input text. As the “detective,” the Multinomial Naive Bayes classifier correctly determines the language of the input text with a remarkable accuracy rate of $97 \%$. the model can accurately classify language in a range of languages by preprocessing the text data and generating a representation of words in a bag. To translate text between languages, the text translation component uses the Google Translate API via the Google library. With the flexibility to choose the source and target languages provided by this component, users can translate text from any supported language to the language of their choice. Easy to use, it enables people to promote intercultural understanding and overcome language barriers in social media, business, education, and other domains, making the world more inclusive and connected.

关键词： Social networking (online) Image processing Education machine learning Linguistics Libraries data models

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Analysis of machine learning Model of Gynaecological Cancer Diagnosis Using Multilayer Perceptron Network

Analysis of Machine Learning Model of Gynaecological Cancer ...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Vivekanand Aelgani Dhanalaxmi Vadlakonda Department of Computer Science Osmania University Hyderabad India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Women's health is affected with ovarian cancer, a fatal gynaecological cancer. It has become more essential to comprehend the biological and neoplastic development of ovarian cancer so as to treat patients. the fifth most common form of cancer among women globally is cancer of the ovary, which must only be identified in its most advanced stages since it has no warning symptoms. the incidence of this disease rises due to age. the epithelial type of ovarian cancer is one of several classifications of ovarian cancer. the most common cancerous ovarian tumor can be brought on by it. Women of any age may be affected by this medical condition, however women over the age of 50 are more likely to develop it. the early identification of ovarian cancer can be done using a variety of classification strategies. the categorization of cancer diseases requires using data mining as a major proportion. By reviewing numerous data mining methods, several ways were suggested for developing a classification model in an explosive field like medical, having the goal of obtaining a model with the highest classification accuracy.

关键词： Accuracy Annotations Computational modeling Multilayer perceptrons data models Vectors data mining Random forests Ovarian cancer Tumors

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