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12th EAI International Conference on Ad Hoc Networks, AdHocNets 2020

作者： Elgammal, Ali Safwat, Mena Badawy, Wael AbdAllah, Eslam G. Azer, Marianne A. Huang, Changcheng Valeo Giza Egypt School of Information Technology and Computer Science Nile University Giza Egypt Master of Information Systems Security Management Concordia University of Edmonton EdmontonAB Canada National Telecommunication Institute Cairo Egypt Badr University Cairo Egypt Department of Systems and Computer Engineering Carleton University OttawaON Canada

ISBN: (纸本)9783030673680

The Internet Protocol (IP) architecture could not fully satisfy Vehicular Ad-hoc Networks (VANETs) needed efficiency due to their dynamic topology and high mobility. This paper presents a technique to update the software of Electronic Control Units (ECUs) in vehicles using Information Centric Network (ICN) architecture. The proposed technique replaces Flashing Over The Air (FOTA) using IP with FOTA using ICN. The importance of FOTA is illustrated as well as the impact of applying the ICN architecture on VANETs. Through our experiments, we compare between the known FOTA over IP and the newly introduced FOTA technique over ICN. © 2021, ICST institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicular ad hoc networks

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Real-Time Energy Management in Buildings Using Reinforcement Learning Techniques

Real-Time Energy Management in Buildings Using Reinforcement...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： T. Aruna Sri Balasubbareddy Mallala S. Bathrinath I. Elsie Diana Velmurugan V H. Mickle Aancy Department of Computer Science and Engineering (AIML&IOT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Department of Electrical and Electronic Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of English PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunlatha R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

While simultaneously maintaining the level of comfort and reducing the costs associated with functionality, the primary objective is to reduce the quantity of energy that is spent as much as possible. During the investigation, a framework that is anchored on RL is developed. By learning from literal and real-time data, reacting to varied environmental circumstances, and taking into account the actions of inhabitants, this system can make substantial alterations to the amount of energy that is currently being used. To anticipate and adjust to the requirements for energy, the system that has been shown makes use of complex algorithms. This ultimately leads to an increase in efficiency being achieved by the system. The results of the simulation indicate that there have been significant improvements in terms of both the amount of energy that has been saved and the functional performance that has been achieved in contrast to more conventional control strategies. Furthermore, the findings shed light on the probable role that RL will play in the accomplishment of sustainable energy operations in smart structures. This is in addition to the fact that they contribute to the broader pretensions of energy efficiency and environmental sustainability.

关键词： Costs Smart buildings Scalability Operating systems Reinforcement learning Real-time systems Energy efficiency Internet of Things Sustainable development Optimization

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Investigating the Effectiveness of Image Restoration Algorithms in Digital Image Processing

Investigating the Effectiveness of Image Restoration Algorit...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Amritpal Sidhu Ganesh Kumar Kantak S. Sathiya Naveena Gitanjali R. Shinde Vaishali Singh Taskeen Zaidi Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Mechanical Engineering Vivekananda Global University Jaipur India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This paper examines the effectiveness of image healing algorithms for virtual photo processing. The research makes a specialty of different forms of picture recovery algorithms, inclusive of side-retaining methods and regression-based totally methods and their outcomes on photo nice. It proposes a quantitative assessment framework based on a hard and fast of goal metrics for the overall performance of these algorithms. The assessment framework includes simulations to acquire sensible dimension consequences. Outcomes have shown that the regression-based techniques outperformed the brink-keeping techniques in phrases of picture constancy and sharpness. The paper concludes that the proposed quantitative evaluation framework may be used to assess the performance of different picture restoration algorithms in digital picture processing.

关键词： Measurement Digital images Image restoration Information technology

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Quantum K-Nearest Neighbors for Emotion Recognition Using Visual Art Based on Color and Texture Analysis

Quantum K-Nearest Neighbors for Emotion Recognition Using Vi...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Ammar H. Shnain P. Jaya Prakash Varanasi Srinivas S. Thanalakshmi S. Senthil Kumar Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Information Technology Sri Venkateswara College of Engineering Tirupathi India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Master of Business Administration Gojan School of Business and Technology Chennai India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The emotion recognition is the process of recognizing a person emotion in various ways that also expressed in arts such as paintings, drawings are the visual art. In traditional automated detection methods performs based on fixed rules and manual steps which affect to recognize the arts. To overcome this problem, Quantum K-Nearest Neighbors (QKNN) for used classify the emotions using visual art. The WikiArt and AffectNet are collection of images, artworks based on visual art that provide as input to the proposed method QKNN. The gray scale conversion and data augmentation is used for pre-processing for improve the quality of images. The Histogram of Oriented Gradients (HOG) is used for extract features of pre-processed data. The KNN is used for classify the emotions and improved QKNN is utilized for enhance the accuracy of the classification. The performance metrices are used to evaluate the overall results of metrics such as accuracy of 98.46% and 99.12% for WikiArt and AffectNet are compared with existing works of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) respectively.

关键词： Support vector machines Visualization Emotion recognition Art Accuracy Nearest neighbor methods Feature extraction Data mining Convolutional neural networks Painting

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A Novel Method to Identify and Recover the Fault Nodes over 5G Wireless Sensor Network Environment

A Novel Method to Identify and Recover the Fault Nodes over ...

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Innovation in technology (APCIT), Asia Pacific Conference on

作者： Vijaya Vardan Reddy S P A. Hency Juliet R. Jayadurga S. Sethu Pavithra K N V. Samuthira Pandi Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamilnadu India Department of Science and Humanities R. M. D. Engineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

The significance of communication networks is growing in tandem with the proliferation of communication technologies. Present methods for maintaining 5G Wireless Sensor Networks are still restricted to routine maintenance and post-maintenance tasks. They lack a comprehensive function for monitoring the network's status, are unable to assess the network's health, and are difficult to maintain before the 5G-based WSNs seriously degrade. 5G Wireless Sensor Network faults can only be resolved by highly trained technicians due to low maintenance efficiency. As a result, errors cannot be detected or located promptly or accurately, leading to forced repairs that incur the expense of new network cables. First, the article lays forth the basics of network fault analysis. Then, it uses deep learning to simulate communication network problem diagnosis. Lastly, the experimental section compares various methodologies and analyses the findings of fault location. The results of the simulation demonstrate that the suggested approach mitigates the created model's flaws to a certain degree while simultaneously enhancing the network fault detection model's accuracy, universality, and robustness. A novel approach to autonomous placement, the Fault Node Recovery Protocol is described in this study. It is implemented in 5G mobile communications to detect faulty data according to wireless sensor network standards, and it is made to fix the problems with conventional techniques, such poor positioning precision and lengthy running time. The automated localization model for 5G mobile communication fault data, constructed using the suggested FNRP approach, is presented in this work. By comparing it to the standard Adhoc On-Demand Distance Vector Routing protocol, we can see how well the suggested system performs.

关键词： Wireless communication Location awareness Wireless sensor networks Accuracy 5G mobile communication Vectors Routing protocols

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Digitizing Farmers' Land Data Collection Systems in Indonesia with the Use of Tani Millenial Apps

Digitizing Farmers' Land Data Collection Systems in Indonesi...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mohammad Prasanto Bimantio Dian Pratama Putra Teddy Suparyanto Amallia Ferhat Nanda Satya Nugraha Alam Ahmad Hidayat Bens Pardamean Department of Food and Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Agricultural Socio-Economics Institute of Agriculture STIPER Yogyakarta Indonesia Department of Forestry Institute of Agriculture STIPER Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods.

关键词： Decision making Education Government Transforms Data collection Agriculture Recording

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White blood cell subtype detection and classification

arXiv

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arXiv 2021年

作者： Praveen, Nalla Punn, Narinder Singh Sonbhadra, Sanjay Kumar Agarwal, Sonali Syafrullah, M. Adiyarta, Krisna Dept. of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India Program of Master of Computer Science Universitas Budi Luhur Indonesia

—Machine learning has endless applications in the health care industry. White blood cell classification is one of the interesting and promising area of research. The classification of the white blood cells plays an important part in the medical diagnosis. In practise white blood cell classification is performed by the haematologist by taking a small smear of blood and careful examination under the microscope. The current procedures to identify the white blood cell subtype is more time taking and error-prone. The computer aided detection and diagnosis of the white blood cells tend to avoid the human error and reduce the time taken to classify the white blood cells. In the recent years several deep learning approaches have been developed in the context of classification of the white blood cells that are able to identify but are unable to localize the positions of white blood cells in the blood cell image. Following this, the present research proposes to utilize YOLOv3 object detection technique to localize and classify the white blood cells with bounding boxes. With exhaustive experimental analysis, the proposed work is found to detect the white blood cell with 99.2% accuracy and classify with 90% accuracy. © 2021, CC BY.

关键词： Image classification

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Analyzing the Benefits of Applying Machine Learning to Real-Time Data Analysis

Analyzing the Benefits of Applying Machine Learning to Real-...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Rakesh Kumar Yadav TR Mahesh Ajay Kumar R. Jayadurga A Vishal Gourav Sood Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Centre of Interdisciplinary Research in Bus iness and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The application of device learning to actual-time data analysis has the potential to revolutionize the way businesses make decisions. With the capacity to procedure big volumes of data fast and correctly, system learning can provide insights that tell strategic decisions at the same time as promoting information-pushed choice making. This evaluation can assist agencies increase efficiency, lower expenses, and create a more aggressive area. By means of leveraging system learning to higher recognize patron behavior, operational operations, and traits in the market, organizations can create proactive procedures to assume customer wishes and capitalize on opportunities. This paper will discover the capacity advantages of making use of device studying to actual-time records analysis and talk how businesses can satisfactory leverage it. The paper can even speak exceptional varieties of device gaining knowledge of strategies and do not forget some problems businesses need to do not forget before integrating system gaining knowledge of into their records evaluation systems.

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An intelligent IoT Enabled Traffic queue handling System Using Machine Learning Algorithm

An intelligent IoT Enabled Traffic queue handling System Usi...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Shamitha, C. Radhika, S. Malathy, K. Ranjith, S. Sasirekha, N. MAHE MIT Department of Electronics and Communication Engineering Bengaluru560064 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Engineering Chennai602105 India S.A. Engineering College Thiruverkadu Department of Master of Computer Applications Chennai600077 India Jeppiaar Engineering College Department of Electronics and Communication Engineering Semmenchery Raghiv Gandhi Salai OMR Chennai600119 India Sona College of Technology Department of Electronics and Communication Engineering Salem636005 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Overcrowding, pollutants, and logistical delays have been exacerbated by the fast expansion in the number of automobiles. The Internet of Things (IoT) is a new breakthrough that is bringing the world closer to fully automated processes and systems with sophisticated control. The growth of a nation on the other side of the world leads to an increase in personal automobiles. As a result, metropolitan cities have seen an increase in traffic. A improved traffic management system is needed, therefore. Good and dependable traffic control and bottleneck management may save a lot of money and time. IoT-based ITM sensors are installed in automated cars and smart gadgets to detect, acquire, and send data.. The use of machine learning (ML) may also help to enhance the transportation system. Because of the many issues with current transportation management systems, there is a high prevalence of traffic congestion, delays, and fatalities. Adaptive traffic control based on machine learning and the internet of things is the subject of this study. For the purposes of this proposal, vehicles, infrastructure, and events all play a key role in its design. The design uses a variety of situations to address all of the transportation system's challenges. A machine-learning-based DBSCAN clustering algorithm is included into the system's design to catch any unintentional anomalies. The suggested algorithm continually adjusts traffic light timings based on traffic flow and predicted movements from neighboring intersections. In addition to reducing travel time, it also reduces traffic congestion by creating a smoother transition. For smart-city-based transportation systems, the suggested traffic-management technique greatly outperforms the traditional approach, according to the results of an experiment. Decreases traffic jams, decrease traffic fatalities and enhances the overall route experience via the use of a suggested solution. © 2022 IEEE.

关键词： Traffic congestion

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An Effective Security Protocol Design for IRIS based Credential Evaluation using Intensive Deep Learning Scheme

An Effective Security Protocol Design for IRIS based Credent...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Ramachandran. R Hema Priya. K K. Malathy G. Sivagamidevi C. Venkata Sudhakar V. Indhumathi Department of Biomedical Engineering Rajalakshmi Engineering College Chennai Department of Computer Science and Engineering Easwari Engineering College Chennai Department of Master of Computer Applications S.A. Engineering College Chennai Dept of AI & DS Dhanalakshmi College of Engineering Chennai Department of ECE SreeVidyanikethan Engineering College Tirupati Andhra Pradesh Department of IT Panimalar Institute of Technology Chennai

The component of a system charged with ensuring the security of its users is among its most crucial parts. It has been demonstrated that using a password or login that is too basic makes you vulnerable to hackers and does not ensure a high level of security. Authentication using biometric methods can be achieved in several ways. Some of the most advanced and reliable are facial recognition technology and iris recognition. Because it relies heavily on detection of patterns, it is able to reliably identify the rightful owner of an Iris scan. Accuracy as well as effectiveness have both been greatly enhanced in the resultant recognition system. Security breaches and other identification scams are on the rise, making it all the more crucial to implement a robust biometric system. The option that has gained a lot of attention is biometric identification. The iris's potential as a biometric has gained traction in recent years. This quantifiable quality ensures great productivity and precision, which is what triggered the phenomenon. We present a complete ResNet50-based deep learning system for iris identification in this study. Utilizing only a small number of training photos from every class, we train our algorithm on a popular identification of iris dataset, achieving significant gains over prior methods. In addition, we introduce a visualization method that may identify key features of iris pictures that have a significant bearing on the precision of recognition. We anticipate that this approach will be utilized extensively in the future to improve the scalability and precision of various biometrics identification jobs

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