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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Dheerender Kumar Thakur Krishna Prasath Sivaraj Monali Gulhane Jaswanth Alahari Rakesh Jena Punit Goel Masters in Computer Science Osmania University India The University of Toledo Toledo OH United States Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India University of Illinois Springfield One University Plaza Springfield IL USA Biju Patnaik University of Technology Rourkela Odisha India Maharaja Agrasen Himalayan Garhwal University Uttarakhand India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Efficient Network Anomaly Detection and Mitigation Strategies for Large-scale Networks As the number of connected devices has increased so has the potential for malicious activities and network malfunctions. This study focuses on creating efficient mechanisms for real-time and accurate detection and avoidance of outliers in big Abstract-Efficient Strategies to Detect and Mitigate Network Anomalies in Large-scale Networks As more and more devices get connected to the internet, the chances of malicious activities and network failures only increase. To address this problem, this study develops efficient mechanisms for detecting and avoiding outliers in big data networks in real time and with high accuracy. The approaches proposed will utilize techniques as machine learning, data mining and network traffic analytics. The next step is to build out anomaly detection models that can identify and classify abnormal network behavior using these techniques. In addition, the methods will employ advanced data processing tools to detect vast quantities of network data in real-time to minimize false positive rates." Active learning theory: efficient and scalable, deployable to large-scale networks, anomaly detection and remediation methods. L7-QoS takes even the network one step ahead and thus increases the network quality by significantly decreasing the ratio of the hazardous operation or the error of the system. This work has important implications to any sector or organization where large-scale networks are important--including telecommunications, banking, and healthcare.

关键词： Accuracy Prevention and mitigation Telecommunication traffic Organizations Data processing Real-time systems Telecommunications Anomaly detection Stress Stress measurement

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Advancing Data science Using AI-Driven Processes

Advancing Data Science Using AI-Driven Processes

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： M Jahir Pasha Kumar Gaurav Amit Kumar Bhanja Mohamed Dawood Shamout Chinna Babu Mupparaju Mahesh Manohar Bhanushali Computer Science and Engineering G.Pullaiah College of Engineering and Technology Kurnool English School of Liberal and Creative Arts(Social Sciences and Languages) Lovely Professional University Phagwara Department of Management School of Management Sciences Varanasi Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Masters in Computer Science University of central Missouri warrensburg USA Management Studies University of Mumbai VPM's Dr.V.N.Bedekar Institute of Management Studies Thane

Advances in AI-pushed facts technological know-how can be seen in an expansion of different regions. Examples of those consist of herbal Language Processing (NLP), machine learning (ML), Deep gaining knowledge of (DL), pc vision (CV), computerized data evaluation, and plenty *** technology is used to technique and extract significant records from textual content-based totally datasets. This era uses semantic algorithms, natural language processing tools, and rule-based totally era to apprehend and interpret textual content information. machine studying (ML) is now being used to automate positive obligations such as predicting monetary trends, figuring out consumer styles, and greater. ML technology can be used to identify relationships among facts points that have been previously no longer seen. Deep gaining knowledge of (DL) is a sub field of the device getting to know that applies layers of neural networks to identify patterns inside facts units. This generation gives an exceptionally powerful manner of analyzing records, allowing machines to stumble on complicated relationships that may have previously been too hard to come *** imaginative and prescient (CV) is another region in which advances in AI-pushed information science are being visible. CV generation lets in for computerized evaluation of snap shots and movies, making it viable to become aware of gadgets, come across tendencies, and discover anomalies. computerized facts analysis is one of the maximum powerful approaches.

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Splicing Image Forgery Detection by Deploying Deep Learning Model 1

Splicing Image Forgery Detection by Deploying Deep Learning ...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Krishnamoorthy, N. Amuthadevi, C. Geedtha, M.K. Reddy, Poli Lokeshwara Anitha Rani, K.S. Gopinathan, R. Department of Masters in Computer Application SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai600089 India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Anurag University Telangana Hyderabad500088 India School of CSA REVA University Karnataka Bangalore560064 India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore641008 India

ISBN: (纸本)9781665460842

The use of digital images has become more important in numerous industries, including journalism, medical diagnosis, law enforcement, and forensics. Images could be easily modified due to the availability of photo-altering tools and software, which can distort the original information. The availability of low-cost and simple image-editing software has also drastically decreased the time and money needed to engage in image tampering. This destroys the purity of the image, making it vulnerable to misuse by anyone. Copy-move, splicing, and enhancement falsification are prominent methods used to create fake images. Using a combination of two or more images, a new image is created called splicing, which may be shared across many online channels and used to spread information and impact target audiences. The discovery could have beneficial as well as negative results. Consequently, it is important to provide a method that can identify the splicing forgery in an image. The study used a Deep Learning (DL) model named MobileNet to distinguish between authentic and manipulated images. Both the training and testing of the DL model make use of the CASIA data. Resizing and enhancing methods are used to process the collected data. Accuracy, F1 score, and recall are employed to test the reliability of the created DL model. © 2022 IEEE

关键词： Diagnosis

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Stock watcher application using google web toolkit

TechRxiv

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TechRxiv 2020年

作者： Kondoju, Akhilesh Kumar Kontham, Revanth Reddy Fiaidhi, Jinan A. Masters of Applied Computer Science Lakehead University Thunder Bay ON Canada

This article describes simple stock watcher based on Google Web Toolkit (GWT) as the new technology in the creation of rich AJAX applications using only Java as the programming language, which is later on compiled into pure JavaScript and deployed as a regular web site. The user is able to know and manage stock watcher to be updated with the latest changes in stock. We used random data as input as it is a prototype model. © 2020, CC BY.

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Investigating Neural Network-Based Deep Learning Strategies for Real-Time Data Analysis in Machine Learning

Investigating Neural Network-Based Deep Learning Strategies ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Suchita Walke Mounika Nalluri Lavanya R Aruna Sri Rongali Hrishikesh Bhanudas Vanjari Ritu Computer Engineering pillai collage of Engg and Technology Mumbai University India Masters in Computer Information Systems Murray State University USA ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US E &TC Engineering Dept BVCOE Lavale SPPU Pune Maharashtra India Management Maharishi Markandeshwar(deemed to be university) Ambala Haryana

there are numerous distinct strategies and techniques that fall under the huge class of neural network-primarily based deep gaining knowledge of in device getting to know. those strategies revolve around developing and schooling synthetic neural networks, that are computational models that mimic the capabilities and structure of the human brain, to analyze huge datasets and make predictions or decisions based totally on the *** of the key challenges in actual-time information analysis is being capable of method incoming information fast and efficiently, as well as adapt to converting data styles or sudden occasions. traditional device learning algorithms are frequently restricted in their ability to deal with these demanding situations, that is in which deep gaining knowledge of techniques are available *** getting to know techniques contain education neural networks with a couple of layers, every of which strategies one-of-a-kind aspects of the data. This lets in for greater complex and accurate evaluation of the facts, in addition to the potential to handle larger and more numerous datasets. One famous method in deep studying is known as convolutional neural networks (CNNs). Those networks are specifically properly-suitable for studying visual information, including snap shots, by means of breaking down the data into smaller parts and studying them at one of a kind ranges of abstraction. This permits for the identification of patterns and features in the data which could then be used for category or prediction responsibilities.

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Impact of Structural Properties on Network Structure for Online Social Networks

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Procedia computer science 2020年 167卷 1200-1209页

作者： Subhayan Bhattacharya Sankhamita Sinha Sarbani Roy School of Mobile Computing and Communication Jadavpur University Kolkata Department of Masters of Computer Applications Meghnad Saha Institute of Technology Kolkata Department of Computer Science and Engineering Jadavpur University Kolkata

Online Social Networks (OSNs) are largely popular. People interact daily on networks such as Facebook, Twitter, YouTube, Email, Messenger, WhatsApp, Google+, Quora, LiveJournal etc. and the types of interaction on these networks are different. The types of content that people share on these networks are also different. Each of the OSNs serves a different purpose, such as sharing multimedia, microblogging, serving as a Question and Answer forum etc. Moreover, the community structure of these networks is also different. However, currently, all OSNs are considered as scale-free network based on the power-law degree distribution nature of these networks. In this paper, the effect of network properties such as density, diameter, degree distribution, global clustering coefficient, local clustering coefficient, homophily, assortativity and other centrality measures such as power-law exponent, eigenvector centrality and closeness centrality on seven online social networks, namely, Facebook, Twitter, YouTube, Email, Google+, Epinions and Gowalla networks are studied. The differences and similarities in the empirical results are analyzed, and the OSNs are divided categories based on the observed differences and similarities in network properties and centrality measures.

关键词： Online Social Networks Categorisation Centrality Measures Assortativity Homophily Clustering Coefficient

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Optimization of Multi-Channel Quartz Crystal Microbalance for Gas Sensing Application

Optimization of Multi-Channel Quartz Crystal Microbalance fo...

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Ahmad Anwar Zainuddin Soumik Bhattacharjee Aliza Aini Md Ralib Nurain Sufi Sabreena Mohd Sukri Saidatul Izyanie Binti Kamarudin Krishnan Subramaniam Dini Handayani M. Reyasudin Basir Khan Nik Nor Muhammad Saifudin Computer Science Kuliyyah of Information Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Digital Media Masters Universität Bremen Germany Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Kolej Pengajian Pengkomputeran Informatik dan Matematik (KPPIM) Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mechanical Engineering Mila University Nilai Negeri Sembilan Malaysia Tun Razak Graduate School (TRGS) Universiti Tun Abdul Razak (UNIRAZAK) Kuala Lumpur Malaysia

Mass detection sensor Quartz Crystal Microbalance (QCM) measures mass sensitivity by piezoelectric, high frequency shaving on nanograms. These sensors detect with high precise geometry, low cost and ease of production. Conventional QCM sensors are limited to one electrode structure that minimizes the range of sensing. On a single quartz crystal substrate, three new QCM Multi-Channel sensors are developed and optimized that include 2-channel, 3-channel and 4-channel QCM sensors. The COMSOL Multiphysics software is used to design and simulate the multi-channel sensors to enhance the device structure for applications with less interference frequency. From the results, it shows that the least interferences exhibit frequency values of 9.58MHz as both QCMs for the 5mm centre-to-centre (c2c). However, due to small c2c distances that decrease accuracy and operating disadvantages, this design was not selected. For 3 channel sensors, however, three resonant peaks show a similar value of 9.58MHz when c2c is maintained at 7mm. With the highest c2c distance this design shows less interference. Finally, for 4-channel sensors, a distance of 6 mm c2c also has a minimum interference with a 9.58 MHz resonance frequency. This shows that 4 channel sensors with the highest c2c can provide greater operational convenience and less interference at frequencies and thus maximize measurement accuracy. Multi-sensor arrays are advantageous when detecting a large number of analytes at the same time or when only selective coatings (coatings that respond to more than one analyte at the same time) are available. These sensor arrays, which use only one sensor and combine it with enhanced signal evaluation methods or prior sample separation to achieve the results of multi-sensor arrays, are becoming increasingly popular.

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iTCLab PID Control Tuning Using Deep Learning

iTCLab PID Control Tuning Using Deep Learning

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Information Technology International Seminar (ITIS)

作者： Basuki Rahmat Minto Waluyo Tuhu Agung Rachmanto Agussalim Alfan Rizaldy Pratama Muljono Muljono I Gede Susrama Mas Diyasa Made Hanindia Prami Swari Novirina Hendrasarie Information Technology Masters Department Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia Industrial Engineering Department Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia Environmental Engineering Department Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia Center for Research and Innovation on Advanced Transportation Electrification - Politeknik Elektronika Negeri Surabaya Indonesia Computer Science Department Universitas Dian Nuswantoro Semarang Indonesia Informatics Department Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia

This paper presents a novel approach for tuning Proportional-Integral-Derivative (PID) controllers using deep learning techniques, specifically applied to the iTCLab, an advanced industrial control laboratory system. PID controllers are widely used in industrial processes for their simplicity and effectiveness. However, their performance heavily relies on proper tuning, which can be a complex and time-consuming task. In this study, we leverage the capabilities of deep learning to automate and optimize the PID tuning process for the iTCLab setup. We propose a data-driven methodology that combines system identification, Deep Learning networks, and optimization algorithms to achieve superior PID controller tuning.

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Splicing Image Forgery Detection by Deploying Deep Learning Model

Splicing Image Forgery Detection by Deploying Deep Learning ...

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

作者： N Krishnamoorthy C. Amuthadevi M. K. Geedtha Poli Lokeshwara Reddy Anitha Rani K S R. Gopinathan Department of Masters in Computer Application Ramapuram Campus SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Anurag University Hyderabad Telangana India School of CSA REVA University Bangalore Karnataka India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (纸本)9781665460859

关键词： Deep learning Training Renewable energy sources Splicing Computational modeling Forgery Data models

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Prospective integration between Environmental Intelligence (AMI), Data Analytics (DA), and Internet of Things (IoT)

Prospective integration between Environmental Intelligence (...

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Congreso Internacional de InnovaciÃ³n y Tendencias en IngenierÃa (CONIITI)

作者： Andrés Armando Sánchez Martín Enrique Gonzalez Guerrero Luis Eduardo Barreto Santamaría Masters in Systems Engineering and Computer Science Pontificia Universidad Javeriana Bogotà Colombìa

This article presents the results of an investigation and analysis of information on emerging technologies to achieve two purposes. The first purpose is to create three reference models: IOT reference model, AMI reference model and DA reference model, applicable for the implementation of solutions to different real-life problems. The second purpose is to present the proposal of a single integrated reference model, called Quysqua, which synergistically combines the three models mentioned above.

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