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Innovation in Technology (ASIANCON), Asian Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Deepshikha Saxena Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer India Department of Computer Applications Govt. Engineering College Bikaner India

The proposed method involves pre-processing of the dataset, which includes normalization and resizing of the images to a standard size. Local binary pattern is then used to extract features from the pre-processed images. These features are used to train a machine learning model, specifically a support vector machine, to recognize faces in the images. The effectiveness of the proposed approach was evaluated using a publicly available dataset, and the results indicate a high level of accuracy in face detection, with a recognition rate of over 90%. The approach was also tested on images with varying contrast and rotation angles, and the results demonstrate its ability to accurately identify faces under such conditions. The proposed method has practical applications in various areas such as surveillance, access control, and forensics. It has the potential to be integrated into security systems for real-time face recognition and identification, improving the accuracy and efficiency of such systems. Furthermore, the method can be extended to include more complex features for more accurate and robust face detection. Overall, this research contributes to the growing body of knowledge on face detection techniques and their practical applications.

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Adaptive Clustering Algorithm for Big Health Data Classification

Adaptive Clustering Algorithm for Big Health Data Classifica...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Meenakshi Jha Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer India Department of Computer Applications Govt. Engineering College Bikaner India

This paper presents an analysis of tobacco and alcohol consumption among different age groups, using data collected by the World Health Organization (WHO). The analysis quantifies alcohol and tobacco consumption in grams per day, aggregating the total amount consumed by each age group. Consumption of alcohol is considered not only through wine, but also through other mediums such as medicine, inhalers, etc. while tobacco is consumed through smoking, dry tobacco, etc. Our analysis also takes into account whether the cases of consumption were controlled or uncontrolled. The majority of cases analyzed in our study were found to be controlled. Finally, we present graphical representations of the analysis results, highlighting the dependencies between different variables.

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Enhancing Facial Recognition and Tracking for Security Applications using Haar Cascade Classifier

Enhancing Facial Recognition and Tracking for Security Appli...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Meenakshi Jha Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India MDS University Ajmer Department of Computer Applications Govt. Engineering College Bikaner India

Facial recognition is used in biometric technology. Facial recognition is the process of using the face to recognize or verify a person's identity. It captures, analyses, and compares patterns based on a person's facial details. The task of authenticating or identifying human faces from multimedia photos is accomplished using facial recognition technology. Viola and Jones present a method capable of accurately and quickly detecting faces in images. This technology can be used to accurately detect the facial features. Face tracking technology provides better opportunities for security and surveillance. This increases the level of protection. For example, facial tracking software helps improve surveillance strategies and serves as a basis for identifying terrorists and criminals. National borders, casinos, museums, banks or prisons are other examples where this system can be used for security. This paper proposes a face detection and eye and smile recognition system with real time images, using the Haar Cascade classifier for face, eye and smile recognition. This trace is based on the jpeg/jpg file format and can be acquired from any type of camera. It works with almost all types of image formats. Its classifier will detect facial features. In this research paper a comparison is also analyzed between face and facial detection by a camera captured image and saved images. Analysis of both types of code is also being made in this research work. The document also gives the percentage of accuracy by comparing the results. Therefore, this work aims at face processing with eye and smile detection. In this article it first detects the face using a web camera and then the captured image is processed and, on the image, it detects the face, eyes and smile.

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Model-Based Testing of Access Control Requirement in Multi-tenant Application: An Extensive Life Cycle 1

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3rd International Conference on computer Science, Electrical and Electronic engineering, ICCEE 2021

作者： Duraisamy, Gunavathi Ghani, Abdul Azim Abd Zulzalil, Hazura Abdullah, Azizol Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Selangor Serdang Malaysia Faculty of Computer Science and Information Technology Department of Communication Technology and Networking Universiti Putra Malaysia Selangor Serdang Malaysia Lee Kong Chian Faculty of Engineering and Science Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Sungai Long Campus Selangor Kajang Malaysia

ISBN: (数字)9789811697814

ISBN: (纸本)9789811697807

Model-based testing is a substantial approach that is based on and involving models. It is well known for achieving test coverage and for generating and executing test cases automatically. The main and core activity of model-based testing is the modelling activity. Models are used to explain and represent the behaviour of the product. The requirement will be translated into models and those models will be used in generating the test cases automatically in model-based testing. The modelling activity clarifies test requirement and contribute to automatic generation and execution of large test suites with tool support. Model-based testing provides numerous advantages in testing;mainly on achieving appropriate test coverage and reduce manual effort and time spent since it can execute the test cases automatically. Despite all the benefit it can provide, there are some drawbacks too. However, the challenges faced can overcome by implementing proper test modelling activity and by adopting a well-versed test management activity. Thus, in this paper, we have studied the existing testing process, the taxonomy of model-based testing and model-based testing steps and proposed an extensive lifecycle for model-based testing with detail activities and steps to be carried out in order to test and validate access control requirement for multi-tenant application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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Performance Evaluation Customer Access Network Using Difference Modulation Technique 1st

Performance Evaluation Customer Access Network Using Differe...

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1st International Symposium on systems Modelling and Simulation, SMS 2024

作者： Mugunthan, Sivaguru Abdul Rahman, Juwairiyyah Abu Hassan, Hasliza Muslim, Norliana Ab Rahman, Mohammad Syuhaimi Department of Engineering Faculty of Engineering and Life Sciences Universiti Selangor Selangor Bestari Jaya45600 Malaysia Department of Computer and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar31900 Malaysia Department of Electrical Electronic and System Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (纸本)9789819646128

Modeling Fiber To The Home (FTTH) networks involves several technical aspects and design considerations to enable effective and high-quality service delivery. This research seeks to boost the received signal quality for end-users inside a FTTH network. In order to create suitable and high-quality FTTH networks, this study will simulate FTTH networks utilizing network feasibility criteria, with an emphasis on BER and Q-factor. The signal distribution inside access networks is improved using Wavelength Division Multiplexing (WDM) and 10G-Passive Optical Network (PON) technology is used since it can deliver a fourfold improvement in downstream data transfer rates per user. Simulation result using Optisystem 21 software, by setting transmitter wavelengths of 1490 nm and 1550 nm with a loss of 0 dB for a maximum of 2 users for total access based on the Q-factor calculation, which must be more than 6, while the BER must be less than 1 × e−9. system performance parameters that demonstrate Q-factor and Bit Error Rate (BER) give good quality and good performance is also shown by eye pattern in optical simulation. For 1490 nm, the highest Q-factor for 2 users is 35.8226, with a minimum BER of 2.4360 × 10−281. For 1550 nm, the highest Q-factor is 15.3668 (in phase) and 14.8087 (in quadrature), with minimal BER values of 1.3674 × e−53 (in phase) and 6.4291 × e−50 (in quadrature). Good performance is also shown by eye pattern in optical simulation that displays amplitude signals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Q factor measurement

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Design of Machine Learning Based Smart Irrigation system for Precision Agriculture

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computers, Materials & Continua 2022年第7期72卷 109-124页

作者： Khalil Ibrahim Mohammad Abuzanouneh Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Mesfer Al Duhayyim M.Al-Shabi Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani K.Muthulakshmi Department of Information Technology College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a61101Yemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Management Information System College of Business and AdministrativeTaibah UniversityMedina42353Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electronics and Communication Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable *** use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision *** achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be *** this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision *** proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for *** proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and ***,the sensed data are transmitted to the cloud server for processing and decision ***,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for ***,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly *** performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

关键词： Automatic irrigation precision agriculture smart farming machine learning cloud computing decision making internet of things

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An Investigation and Development into the Use of AI-Based Analytical Methods for Forecasting the Stock Market 4

An Investigation and Development into the Use of AI-Based An...

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4th International Conference on Smart Electronics and communication, ICOSEC 2023

作者： Sheeba, M. Kumar, D. Ranjith Kirubakaran, S. Dixit, Ila Rani, D. Leela Yadav, Ajay Singh Department of B. Com Information System Management Tamil Nadu Chennai600042 India Department of B. Com Corporate Secretaryship Tamil Nadu Chennai600042 India CMR College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad501401 India Maharaja Agarsen International College Department of Management Chhattisgarh Raipur492001 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India Srm Institute of Science and Technology Department of Mathematics Utter Pradesh Ghaziabad India

ISBN: (纸本)9798350300888

Forecasting the stock market is difficult due to the fact that a number numerous factors, including the current situation of economy, current politics, and market sentiment. Traditional analytical methods have limitations in accurately predicting stock prices, leading to increased curiosity about using algorithms based on artificial intelligence (AI) and machine learning (ML) stock market forecasting. This paper investigates the use of AI-based analytical methods for forecasting the stock market and presents a detailed analysis of various algorithms used in AI-based stock market forecasting. The use of AI-based analytical methods for forecasting the stock market has several benefits. Firstly, AI-based models can analyse vast amounts of financial data much faster than traditional methods, allowing for more accurate and timely predictions. Secondly, AI-based models can handle complex relationships between different factors affecting stock prices, such as market trends, news events, and economic indicators, which can be challenging to capture using traditional methods. This study also discusses about the challenges and limitations of using AI-based models for stock market forecasting research in the future research. Here, this study uses a novel theory of recurrent neural networks and a random forest model to enable prediction with more accuracy. One of the commonly used algorithms in AI-based stock market forecasting is the recurrent neural network (RNN), which model that uses deep learning. RNNs are very helpful for modelling time-series information, like stock prices, just because they may ensnare the temporal data dependencies. In addition, RNNs can be used to generate forecasts by predicting the next value in a set of timestamps based on previous values. Another popular algorithm for stock market forecasting is the Random Forest (RF) ensemble model. RF models are a type of decision tree-based algorithm that can generate accurate predictions by combining the output

关键词： Financial markets

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Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder

Machine Learning-based Evaluation of Heart Rate Variability ...

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Artificial Intelligence and Smart systems (ICAIS), International Conference on

作者： Vazeer Ali Mohammed Mehmood Ali Mohammed Murtuza Ali Mohammed J. Logeshwaran Nasmin Jiwani Department of Computer and Information System Lewis University Illinois USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India

At present, various electronic devices are used to monitor human heart rates. However, its functions are to avoid predicting the problems caused by heart rate variability in advance and analyzing its implications. It makes it difficult to diagnose problems caused by heart rate variability. A human should have an average heart rate of 72. At the same time, the newborn's heart should beat between 120 and 160 beats per minute. A baby born with autism spectrum disorder may have a lower-than-average heart rate. Complete blockage of the heart at birth is rare. Abnormal heart rate leads to heart block. So, there is a high chance of the child's death due to permanent heart blockage at any time. Most heart diseases in children with Autism Spectrum Disorder (ASD) are present at birth. A significant congenital disability is a hole in the heart. Many people do not realize that having holes in the heart is a common occurrence. Before the baby is born, tiny holes form in the muscular wall that divides the heart into the right and left halves. This paper proposed Machine Learning-Based Evaluation to identify the Heart Rate Variability Response in Children with Autism Spectrum Disorder with Autism Spectrum Disorder. The reasons for this are yet to be identified. However, 70 per cent of perforations resolve spontaneously before or after birth. Exceptionally, Children with Autism Spectrum Disorder with perforations that do not close properly may require surgery or a perforator brace, depending on the location and size of the perforation.

关键词： Autism Pediatrics Pain Autonomic nervous system Surgery Human factors Muscles

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Parameters for Non-Solitons in Fiber and Their Impact on Transmission Performance over Long Distances 6

Parameters for Non-Solitons in Fiber and Their Impact on Tra...

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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Salih, Mustafa Muhanad M. Alrubaee, Saif H. Dheyab, Abdullah Abdulateef, Ihsan Amjad Abdulsattar, Nejood Faisal Abbas, Fatima Hashim AL Mansour University College Department of Computer Science and Information System Iraq Al-Farahidi University Department of Communication Technical Engineering Baghdad Iraq Al-Anbar Health Directorate Ministry of Health Iraq Altinbas University Department of Electrical and Computer Engineering Turkey College of Information Technology Imam ja'Afar AI-Sadiq University Department of Computer Technical Engineering Al-Muthanna66002 Iraq Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah51001 Iraq

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

One area of study that has gained popularity recently is communication technology. Due to the exponential growth in data transfer rates, optical fibre communication (OFC) is the only viable solution. The OFC technology provides extremely high bandwidth and rates of up to several gigabits per second. Optical fibre can carry a lot of data while being quite lightweight. Unfortunately, attenuation, dispersion, and nonlinearity are all phenomena of optical fibre transmission systems that might increase fibre signals. Rather of relying on dispersive effects, an improved approach lies in the use of optical solitons. Different from non-soliton communication systems, solitons do not grow in size as the distance between nodes increases. This research compares and contrasts the efficiency of solitons and non-solitons in fibres. By comparing the eye diagrams and bit error rates, we may deduce that soli-ton-based fibre systems are the most distortion-free, whereas non-soliton-based fibre systems exhibit some level of background noise. © 2022 IEEE.

关键词： Solitons

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Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali Language Comment on Facebook

Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali L...

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Integrated Circuits and communication systems (ICICACS), IEEE International Conference on

作者： SM Nuruzzaman Nobel S M Masfequier Rahman Swapno Ramachandra A C Hasibul Hossain Shajeeb Md Babul Islam Rezaul Haque Dept of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Dept of CSE East West University Dhaka Bangladesh

This study addresses the challenge of sentiment analysis in Bengali Facebook comments, where users express their opinions on various topics. Existing sentiment analysis techniques have difficulty achieving high accuracy in this situation, which has inspired the creation of a fresh methodology. To improve sentiment analysis of Bengali Facebook comments, we suggest a model that combines a hybrid convolutional neural network (CNN) and a long short-term memory network (LSTM). Our research adds to the expanding area of sentiment analysis, especially for languages with few resources, such as Bengali. The success of the Hybrid CNN LSTM model opens the door for more precise and context-aware sentiment analysis on social media platforms, allowing for a better understanding of user sentiments and boosting user experiences. Our study focused on dividing comments into five groups according to their emotional content: troll, not bully, sexual, religious, and threat. We carefully generated the handmade dataset and ensured the quality and relevance of a dataset of 50,000 Bengali comments before using it to train and test our algorithm. The Hybrid CNN LSTM model outperforms conventional approaches and achieves an outstanding accuracy rate of 92.09%, demonstrating amazing efficiency in sentiment recognition. This dramatic increase in precision indicates that machine learning techniques are successful in sentiment analysis of Bengali Facebook comments.

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