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Suspicious Object Tracking With Yolov3 with Python Using Open-CV

Suspicious Object Tracking With Yolov3 with Python Using Ope...

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

作者： Deepanshu Gautam Harsh Gupta Himanshu Shekhar Manish Kumar Sarmad Jaafar Nasser Laith Fouad Dept. of Electrical and Electronics Engineering Galgotias College of Engineering and Technology Greater Noida India Collage of Engineering National University of Science and Technology Dhi Qar Iraq Medical Laboratory Techniques Department Al-Farahidi University Baghd Iraq

Object tracking system is essential for the surveillance of the suspicious abandoned objects. Tracking of the suspicious abandoned object is widely used in many areas such as airports, railway station and on its track, parking lots and public transportations to prevent terrorism and avoid the incident related to it. Precise recognition of the abandoned objects in an image arena can detect many applications using different vision algorithms. In this research paper, we present a multi-object tracking model along with left over baggage detection in a real time environment. In this article, we present the analysis of track-by-track methods, including the YOLO track and the SORT algorithm track. This article contains details about a procedure dataset of image edify with YOLO for 6 specific category, which is cumulated in videos for detection by the SORT algorithm. Identifying vehicles or pedestrians in continuous videos is useful for congestion survey. The purpose of this article is to analyze and understand the province.

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Epileptic Seizure detection Using Single-Channel EEG and Artificial intelligence Techniques

Epileptic Seizure Detection Using Single-Channel EEG and Art...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Alla Zwawi Mariam Krkara Mohamed Samir Elbuni Department of Computer Engineering University of Tripoli Tripoli Libya

Epilepsy is a chronic neurological disorder characterized by recurrent involuntary seizures which can lead to serious injuries and death. Clinicians diagnose epilepsy by examining the electroencephalograms (EEG) of patients for seizures. However, early-stage diagnosis of epilepsy is difficult due to the labor-intensive nature of the procedure, lack of trained professionals, and inherent variability in the EEG of different patients. In this paper, we propose an automatic seizure detection system with a patient-independent approach in which the system analyzes and detects seizures in single-channel EEG from multiple patients. The system classifies an EEG instance as seizure or non-seizure based on learned features from the training set. For this task, we combine a comprehensive set of features extracted from the time, frequency, and time-frequency domains to capture information about EEG signals' amplitude, frequency, and morphology. A significant amount of experimental work has been conducted to establish the best algorithms and parameters for preprocessing, feature selection, and classification. Which achieved satisfactory results where the highest accuracy and specificity exceeded 99% through a random forest classifier and the highest sensitivity of 98.9% was achieved through the RBF-SVM classifier. We also demonstrate through comparative results that feature sets selected by the minimum redundancy maximum relevance (MRMR) algorithm outperform the ones obtained through principal component analysis (PCA). Our results indicate that an accurate, rapid, and robust automatic detection of epileptic seizures is possible; which contributes significantly to the efficiency of the epilepsy diagnosis process and the care of epileptic patients.

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Comparison and Analysis of Four Signal detection Algorithms in Different MIMO-VLC systems

Comparison and Analysis of Four Signal Detection Algorithms ...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Chong Li Yufeng Shao Anrong Wang Peng Chen Yanlin Li Renjie Zuo Shuanfan Liu Jie Yuan College of Electronic and Information Engineering Chongqing Three Gorges University Chongqing China

In indoor high-speed visible light communication (VLC) systems, the detection sensitivities of several access signals are often affected due to the mutual interference of different light-emitting diodes (LED) emission signals and the multipath effect from different transmission channels, which hinders the wide application of multiple-input multiple-output-VLC(MIMO-VLC) technology. In this work, high speed VLC signal using 16 quadrature amplitude modulation (16QAM) modulation format is selected, and four signal detection algorithms are compared and analyzed in different MIMO-VLC systems. The results show that the bit error rate (BER) performance while suing zero forcing-successive interference cancellation (SIC-ZF) is significantly better than that of ZF and minimum mean square error (MMSE) signal detection algorithms in complex indoor environments. At the SNR $\mathbf{\leqslant 10dB}$ case, the value of BER in 4×6 MIMO system can reach 10 −5 using SIC-ZF ignoring impacts of non-line-of-sight (NLOS) links.

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Artificial intelligence driven security model for Internet of Medical Things (IoMT)

Artificial Intelligence driven security model for Internet o...

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： Cuddapah Anitha Komala C R Chettiyar Vani Vivekanand S D Lalitha Sampath Boopathi Revathi. R Department of Computer Science and Systems Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Information Science and Engineering HKBK College of Engineering Bangalore Karnataka India Department of Electronics and Communication Engineering RMK College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Computer Science and Engineering R.M. K. Engineering College Kavaraipettai Tamil Nadu India Department of Mechanical Engineering Muthayammal Engineering College Namakkal Tamil Nadu India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India

The Internet of Medical Things (IoMT) has been applied to provide health care facilities for elders and parents. Remote health care is essential for providing scarce resources and facilities to coronavirus patients. Ongoing IoMT communication is susceptible to potential security attacks. In this research, an artificial intelligence-driven security model of the IoMT is also proposed to simulate and analyses the results. Under the proposed plan, only authorized users will be able to access private and sensitive patient information, and unauthorized users will be unable to access a secure healthcare network. The various phases for implementing artificial intelligence (AI) techniques in the IoMT system have been discussed. AI-driven IoMT is implemented using decision trees, logistic regression, support vector machines (SVM), and k-nearest neighbours (KNN) techniques. The KNN learning models are recommended for IoMT applications due to their low consumption time with high accuracy and effective prediction.

关键词： Support vector machines COVID-19 Analytical models Computational modeling Internet of Medical Things Predictive models Security

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Smart Watch to Avoid Face Touching During Pandemic Condition

Smart Watch to Avoid Face Touching During Pandemic Condition

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： S. Vimala Ramakrishnan Raman Gowrishankkar V N. Bharathiraja I. Thamarai Ashok Kumar Department of Electrical and Electronics Panimalar Engineering College Chennai India Symbiosis Institute of Business Management Pune & Symbiosis International Pune India School of Management Sri Krishna College of Technology Chennai India Department of Biomedical Engineering VelTech MultiTech Chennai India Department of Computer Science Panimalar Engineering College Chennai India Department of Computer Science BanasthaliVidyapith Rajasthan India

The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces.

关键词： COVID-19 Temperature sensors Temperature measurement Pulse oximeter Pandemics Nose Watches

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Implementation of a smart parking system using LoRa, STM32 Board & IoT

Implementation of a smart parking system using LoRa, STM32 B...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Praveen Kumar Malik Arun Malik G Sunil Arshi Naim Ram Kumar Sharma Samta Kathuria School of Electronics and Electrical Engineering Lovely Professional University Jalandhar Punjab India School of Computer Science and Engineering Lovely Professional University Jalandhar Punjab India School of Computer Science & Artificial Intelligence SR University Warangal Department of Business Management AlSamer University campus King Khalid University Abha Aseer KSA Raj Kumar Goel Institute of Technology Ghaziabad Uttar Pradesh Law College of Dehradun Uttaranchal University Dehradun India

A parking system with technology is an automated system that makes better use of cutting-edge technology to manage parking spaces. These systems typically monitor the occupancy of parking spaces and advise vehicles in real-time about the availability of parking spaces by combining sensors, wireless communication technologies, and software algorithms. In this project, the use of an STM32 board and Long Range (LoRa) communication technology to automate a parking system over the Internet is suggested. Smart parking systems can communicate over great distances with less power usage thanks to the use of LoRa technology. The system is made to be economical and effective, offering a workable answer to the issue of parking in congested region. The project shows that a smart parking system can be implemented using LoRa and an STM32 board and may be further improved by adding features like automated payment methods and parking space reservations. The technology is built to give consumers access to real-time information regarding parking spot availability via a mobile app. The research shows that a cost-effective and practical smart parking system may be implemented utilizing LoRa and an STM32 board.

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A Robust CNN-based Approach for Skin Lesion detection and Classification

A Robust CNN-based Approach for Skin Lesion Detection and Cl...

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Intelligent Technologies (CONIT), international conference on

作者： Surbhi Dhir Astha Sinha Charvi Bansal Ritu Rani Nidhi Goel Pawan Bhutani Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Center of Excellence - Artificial Intelligence Indira Gandhi Delhi Technical University for Women Delhi India Electronics and Communication Engineering Ajay Kumar Garg Engineering College Ghaziabad India

The abnormal cell growth that most frequently develops on skin that has in some way been exposed to the sun is called skin cancer. Yet, areas of our skin that are not commonly exposed to sunlight might potentially acquire this sort of cancer. The three main kinds of skin cancer are Melanoma, Basal cell carcinoma and squamous cell carcinoma. On an estimate around 9,500 people are affected daily by skin cancer in the United States. Early screening and detection of warning signs can help to detect and cure cancer before it becomes serious. Therefore, deep diving in the world of Deep Learning aims to build an easily accessible technology that can detect skin cancer based on the type of dermatoscopic lesions. In this paper, we have explored a similar venture, where we have built a classification model on the type of skin lesions using CNN. We have trained our model on MNIST HAM-10000 dataset. The efficiency of our model has been assessed using accuracy, recall, precision and f1-score. We have achieved maximum accuracy of 99.13% for classification of seven types of skin lesions.

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Managing Techniques, Strategy, Validation, and Dataset for Harmful Threats detection in IoT Networks: A Critical Review

Managing Techniques, Strategy, Validation, and Dataset for H...

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

作者： Shashidhar Sonnad Ramakrishnan Raman Devendra Singh Kumud Pant Babita Rawat G. Udaya Sri Department of Electronics & Communication Engineering Sharnbasva University Kalaburagi Karnataka India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Management Uttaranchal University Department of ECE St. Martin's Engineering College Secunderabad Telangana

The Internet of Things (IoT) is fast growing to have a bigger influence on everything from ordinary living to massive commercial systems. Tragically, it's caught hackers' notice, who have turned the Internet of Things into an opportunity for unlawful activity, perhaps paving the way for an assault on end nodes. Numerous IoT intrusion detection systems (IDS) are currently developed to achieve this aim, with thorough classification of recognition methodologies, evaluation procedures, and distribution tactics. This review paper offers a thorough assessment of contemporary IoT IDS, a summary of the process, the implementation strategy, the testing method, and data sets that frequently get employed for IDS innovation. Malware, internet of things, fault diagnosis, intrusion detection system, deep learning, internet of things, assaults, and IoT security are some of the terms used in this article.

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A Novel Framework for detection of Digital Face Video Manipulation using Deep Learning

A Novel Framework for Detection of Digital Face Video Manipu...

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Computing and Information technology (ICCIT-1441), 2020 international conference on

作者： Sanskriti Chandra Shivam Saxena Santosh Kumar Mithilesh Kumar Chaube Srinivas K G Saeed Hamood Alsamhi Edward Curry Abdu Saif Department of Data Science and Artificial Intelligence IIIT-Naya Raipur Chhattisgarh India Department of Computer Science and Engineering IIIT-Naya Raipur Chhattisgarh India Department of Mathematical Sciences IIIT-Naya Raipur Chhattisgarh India Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security.

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Analyzing The Image Augmentation to Find the Defect in Apple Leaf

Analyzing The Image Augmentation to Find the Defect in Apple...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： K. Renuka U. Veeresh T. Varun Subba Rao Polamuri V. Lingamaiah Department of CSE CMR Institute of Technology Hyderabad India Department of CSE (Artificial Intelligence & Data Science) Bonam Venkata Chalamayya Engineering College (Autonomous) Odalarevu India Department of CSE Anurag University Hyderabad India

Automatic plant disease detection is vital because it eliminates human intervention in monitoring big farms and allows for early illness diagnosis to prevent further plant decline. When plant health declines, a nation's economy follows suit. Reduced output in this circumstance might have an impact. Current illness detection methods rely on vast farms; an expert is too slow and inefficient. To classify apple tree leaves into healthy, apple scab, apple cedar rust, and various disease categories, present a model that is an ensemble of pre-trained DenseNet121, EfficientNetB7, and Efficient Net Noisy Student. This study uses several Image Augmentation strategies to bolster the size of the dataset and, by extension, the reliability of the resulting model. The validation dataset shows the proposed model has a 96.25% accuracy rate. The proposed approach can identify many leaf attributes at a 90% confidence level, including diseases. The proposed model works quite well across a range of metrics. In agriculture, it might be used to assess the state of a plant in a short amount of time with high accuracy.

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