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Positioning Method for Covered Vehicles Based on Neutrino detection and Reinforcement Learning

Positioning Method for Covered Vehicles Based on Neutrino De...

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Electronic Information Engineering and Computer technology (EIECT), international conference on

作者： Mingren Han Jiashan Gao Peng Zhou Yixin Zhang Beijing Institute of Control Engineering China Academy of Space Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

In complex environments like underwater or underground, traditional navigation technologies face limitations due to the restricted penetration of optical and radio signals. Neutrino signals, which have extremely strong penetration capabilities, offer a new possibility. This study explores the challenges of navigation and positioning of covered vehicles and proposes a neutrino-based navigation and positioning technology to overcome the limitations of traditional navigation technologies in complex environments. This research combines reinforcement learning methods and Kalman filters, using the Proximal Policy Optimization (PPO) algorithm to tune the parameters of the Kalman filter to improve navigation and positioning accuracy. In the study, a Kalman filter estimation system model for vehicles in navigation signal denied environment environments was first established, then a navigation and positioning method based on neutrino detection was designed, and finally, a parameter adaptive PPO Extended Kalman Filter (PPOEKF) state estimation method was proposed. Through mathematical simulation and comparative analysis, the research results show that using reinforcement learning methods to tune the parameters of the Kalman filter can improve the accuracy of navigation and positioning, especially in complex environments.

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Artificial intelligence Contribution to Forensic Science Crime Investigation

Artificial Intelligence Contribution to Forensic Science Cri...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Yashi Agarwal Poonam Rawat Samta Kathuria Rajesh Singh Kanegonda Ravi Chythanya Mansi Sahu Law College of Dehradun Uttaranchal University Dehradun India LCD Division of Research & Innovation Uttaranchal University Dehradun India UIT Division of Research & Innovation Uttaranchal University Dehradun India School of Computer Science & Artificial Intelligence SR University Warangal India School of Life and Applied Sciences Uttaranchal University Dehradun India

In this new digital era, now the world is of technology, and as time passes, we have seen tremendous growth or a blend of digitalization with technology. On the other hand, both traditional and modern crimes are exponentially growing worldwide. With a diverse variety of crimes in almost every domain, technological innovations come into the picture to identify, detect, analyse, and tackle the different types of crimes as per their nature. Artificial intelligence (AI) is one of the most renounced technologies in the digital forensics landscape. AI plays a vital role in providing high accuracy, effectiveness, and efficiency. It provides the most authentic and reliable source from where the information has formed. Digital Forensics is a separate vast branch in the domain of Forensic Sciences. It has been a recognized contributor since the concept came into light. The paper reviews the significance of AI in digital forensic investigation. The paper examines the contribution of the technology and the hike in the success rate it has brought to the legal system to effectively handle these investigations. The paper draws attention towards the challenges faced by Digital forensics when they together pave a path for an effective criminal justice system. The paper mentions the efficacy of the perfect blend of Digital Forensics in the criminal justice system aiming to provide the efficient disposal of matters. The study provides clarity with suggestions and recommendations for tackling the challenges that the combination of AI and Digital forensics faces when implemented in the criminal justice system to investigate various kinds of criminal investigations.

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Diabetes Prediction Using Nature-Inspired Optimization Algorithms

Diabetes Prediction Using Nature-Inspired Optimization Algor...

引用

international Symposium on Artificial intelligence and Signal Processing (AISP)

作者： J. Vijaya Divyanshu Tirkey Priyank Singh Aditya Goel Department of DSAI International Institute of Information Technology Naya Raipur chhattisgarh

Diabetes is a common genetic disease across the world. It is a chronic disease spurred by either production of insufficient production of insulin by the pancreas or less insulin use by the body. Insulin maintains the blood sugar level of the body. Uncontrolled diabetes can cause hypertension, also known as high blood glucose. Hypertension in the long run can be a cause of heart disease. Therefore, the prediction of diabetes is a major problem in the health sector. Various machine learning methods are already in use for the prediction of diabetes. Traditional methods were first used to address diabetes forecasting, but they take a while to deploy. Therefore, future research has adopted the use of various optimization strategies to solve the issue of diabetes forecasting as well as to boost system selectivity and operating speed. These methods have all proven to be successful in solving different optimization issues and also improve the model significantly. This work proposes a Logistic Regression model by the use of nature-inspired algorithms. We proposed a methodology which uses seven different optimization algorithms instead of gradient descent for linear regression. We implemented a linear regression algorithm from scratch and then used it with different optimization algorithms which improve the overall performance.

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Deep Fake detection: Unmasking the Illusion using CNN and LSTM

Deep Fake Detection: Unmasking the Illusion using CNN and LS...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： V Niranjani S Aishwarya T Devamitra B Jagapreetha Department of CSE Sri Eshwar College of Engineering Coimbatore India

Concerns regarding the veracity of digital media have arisen due to the development of deepfake technology. In order to effectively combat this new threat, this study offers a novel deepfake detection strategy that combines a number of techniques, including Photoplethysmography (PPG). For the detection and classification of deepfakes, our solution combines PPG with sophisticated deep learning techniques. PPG records physiological signals, enhancing analysis of images and sounds. Convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and audio fingerprinting are used to extract features from a large dataset that was used to train the system. A thorough study showing our multi-algorithm fusion approach’s improved performance in deepfake detection over single-algorithm approaches, including measures like accuracy and precision. This study also explores its resilience against adversarial attacks and consider ethical implications. This research work represents a significant advancement in deepfake detection, emphasizing the integration of various algorithms, including PPG, for improved accuracy and resilience. It has applications in media forensics, content verification, and online security, offering a comprehensive solution to the deepfake detection challenge while promoting responsible digital content authentication.

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Thermogram-based Regions With Convolutional Neural Network (RCNN) and Facial Biometrics for Safe Driving

Thermogram-based Regions With Convolutional Neural Network (...

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international conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Sukanta Sarkar Tammat Bin Khayer Nafish Haque Kisan Mohammad Nasir Uddin Dept. of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh

A significant number of wrongful death cases involve motor vehicle accidents. In most car accidents, the driver is somehow at fault. This can be due to a lack of visibility, slow decision-making, or bad weather, among other things. The proposed system aims to create a safe driving assist technology consisting of thermal camera-based object detection and intelligent vehicle anti-theft measures to assist in safe driving and provide vehicle security on top of the existing system. This proposed system not only can easily detect objects in low visibility under unsuitable weather conditions, with an average accuracy of 97% but also provides vehicle safety by using facial-biometrics-based vehicle authentication where the accuracy is 95%. This also has a 36 facial data saving capacity in the database at a time. The authorized user doesn't always require internet support to access the vehicle, whereas the unregistered user needs app-based permission from the user to access the car.

关键词： Road accidents Roads Vehicle safety Object detection Signal processing Automobiles Convolutional neural networks

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Fault detection Methodology for PV Array

Fault Detection Methodology for PV Array

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Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), international conference on

作者： Anshul Shekhar M Senthil Kumar Department of Electrical Engineering National Institute of Technology Patna India

The photovoltaic (PV) array is one of the most important renewable energy resources, as the power generation from PV contributes significantly to the system load among renewable power generation. Further, the energy generation is free of pollution and cost, as it generates power from abundant sunlight using the photovoltaic effect. Though there are various factors, such as variations in sunlight irradiance, sunlight hours, atmosphere temperature, etc., that affect the energy generation in PV, the fault in the PV array is one of the crucial factors that affects it severely. Each fault in the PV array has its own distinct characteristics. These characteristics may be detected using the various methods proposed. This paper presents a comprehensive analysis of protection methods implemented for detecting PV array faults. It discusses the features and limitations of each protection methodology elaborately. It also describes the causes of each fault and its characteristics in detail.

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Higgs Boson detection from Monte Carlo Simulation Tensors using Wide and Deep Networks

Higgs Boson Detection from Monte Carlo Simulation Tensors us...

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international Symposium on Artificial intelligence and Signal Processing (AISP)

作者： Subhrangshu Adhikary Chandrachur Mukherjee Saikat Banerjee Department of Computer Science and Engineering National Institute of Technology Durgapur India Department of Physics Wingbotics West Bengal India Department of Remote Sensing Wingbotics West Bengal India

Higgs Bosons are the fundamental particles with 0 spin that couple to mass and are produced by quantum excitation of the Higgs Field. They have even parity and have no electric or colour charge. It is very unstable in nature and quickly decays into other particles. Monte Carlo simulation is a probabilistic technique to estimate different outcomes in a process that are difficult to predict because of random variables. The Monte Carlo simulation method can be used to generate a probabilistic dataset containing 211ow-level features and 7 high-level features to detect Higgs Boson. Deep learning, on the other hand, is very useful to create a decision model based on a large amount of data with fast prediction capabilities. But training a deep learning model comes with requiring large computational resources. Other related works which have been conducted on this domain take a large number (50 to several thousand) of iterations to converge to a stable loss. The presented paper aims to reduce the number of iterations and resource consumption to train the dataset by using a Wide and Deep Network based learning model. The presented model converges to an AUC of 0.89 within 12 iterations hitting an early stopping condition. This shows that the presented model is more efficient than other available models.

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Improved and Accurate Face Mask detection Using Machine Learning in the Crowded Places

Improved and Accurate Face Mask Detection Using Machine Lear...

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

作者： M. Nagaraju Naik Amandeep Kaur Nikhitha Yathiraju Saptorshi Das Kumud Pant Department of ECE CMR College of Engineering & Technology Department of Computer Science & Engineering Chandigarh Engineering College Jhanjeri India Information Technology University of Cumberlands USA KIIT School of Management KIIT Deemed to be University India Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand

Face detection while wearing a mask is used to see whether there are any faces in a photo or video. Face recognition is a technique for recognizing or validating an individual's identification based on their facial features. Face recognition algorithms come in a variety of forms, but their accuracy varies. This page explains how we use deep learning to do facial recognition. For example, nowadays the majority of individuals wear masks. It is quite difficult to recognize somebody wearing a *** the difficult period of Covid-19, there was a sudden increase in criminal activity, and most of the time the victim was unable to portray the criminal's facial structure. As a result, security cameras installed throughout the area that work on the principle of face detection can easily identify an individual whether or not they are wearing a mask. This system's testing results show that this technique can identify the face mask using a multi-feature combination effectively and precisely. As a result, identifying the person behind the mask is simple with the aid of this programme.

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ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning

ConvoWaste: An Automatic Waste Segregation Machine Using Dee...

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international conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Md. Shahariar Nafiz Shuvra Smaran Das Md. Kishor Morol Abdullah Al Juabir Dip Nandi Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Department of Computer Science American International University-Bangladesh (AIUB)

Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model.

关键词： Deep learning Waste management Training Image processing Signal processing algorithms Signal processing Turning

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Machine Learning Approaches for Security detection in Cloud Web Applications

Machine Learning Approaches for Security Detection in Cloud ...

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

作者： Piyush Vyas G. Lakshmi Bhavani Nidhi Gairola Durgunala Ranjith Waleed Khalid Ibrahim Malik Bader Alazzam Computer Information Systems Texas A&M University-Central Texas Killeen Texas USA Electronics and Communication Engineering KG Reddy College of Engineering & Technology Uttaranchal Institute of Technology Uttaranchal University School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Medical Technical College Al-Farahidi University Baghdad Iraq Information Technology College Ajloun National University Jordan

A technology architecture known as "cloud computing" offers end users on-demand, scalable, & measurable resources. Today's businesses rely heavily on computational innovation for a variety of reasons, including cost savings, architecture, developmental platforms, data preparation, data insights, etc. The end users have accessed the cloud service providers' (CSP) services from any location at any time using a webpage. The protection of the cloud platform is of the utmost importance, and many studies using a variety of technologies have been conducted to develop more effective defences against cloud threats. In upcoming years, machine learning technology has shown to be more effective in security the cloud environment. In contrast to other technologies, machine learning algorithms are educated on a variety of real-world information to develop complex that can streamline the task of identifying cloud assaults. This article examines some of the most recent studies that have used machine learning as a defence versus cloud threats.

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