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Discernment and Diminution of Black Hole Attack in Mobile Ad-Hoc Network using Artificial Intelligence

Journal of Physics: Conference Series 2021年第1期2040卷

作者： K Srinivas V Harsha Shastri Vinay Kumar Nassa Gudapati Syam Prasad Prathipati Ratna Kumar Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad - 500075. Assistant Professor Department of Computer Science and EngineeringLoyola Academy Secunderabad-500010 Telangana. Principal/Professor Department of Computer Science & Engineering South Point Group of Institutions Sonepat-131001(Hr.) Professor & HOD Department of IT Narasaraopeta Engineering College Narasaraopeta Guntur District. Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad – 500075.

The MANET is an interesting research topic that has movable nodes. The mobile ad hoc system is an important research area. The nodes are connected by wireless connections. Thus it is vital to have a stable network because information and interaction are crucial in diverse fields such as security and catastrophe emergency response. Because of the decentralized system's vibrant existence, these channels are vulnerable to various threats, including BHA, GHA, SHA. One of the well-known security issues in MANET is the black hole assault. Each node has a routing table containing information from the target node. The number of assaults in MANET is usually protocol networking assaults. The study focused on black hole attacks for discernment and decrease. A black hole attack node wrongly picks up all packet and collects them without passing them to an endpoint by taking a fresh path to the destination nodes. This paper presents BHA defence from assaults with the principle of Artificial Neural Network and calculates its efficiency according to the set of elements, such as efficiency, PDR and delay and energy utilization. This paper aims to devise an artificial neural network method for black hole discernment and reduction in MANET.

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An evaluation of deep learning approaches for detection of voice disorders.

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IOP Conference Series: Materials science and engineering 2021年第1期1085卷

作者： M S Chinchu B. Kirubagari Assistant Professor at SJCET Palai & Research Scholar Computer science and Engineering Department India AssociateProfessor& Research Guide Computer science and Engineering Department India.

The human voice manufacturing system is a complicated natural device capable of modulating pitch and loudness Human sound frequency particularly. The part in which the folded is the primary source underlying internal and/or external factors often destroys vocal folds justification. Some changes consequences are reflected in functioning emotional state soul. Therefore it essential to identify variations at an early stage gives the patient an endangerment overcome any impact modernize their quality of life will-less detection disorders using depth study methods plays an important role as has been proven to facilitate the process. Many researchers have explored technologies for streamlined that can help clinics diagnose noise paper we present the conducted research activities.

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Detecting Bone Fracture in Medical Images

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Journal of Physics: Conference Series 2021年第1期1770卷

作者： N Satya Sriveni K Hema Latha A Viji Amutha Mary Mercy Paul Selvan UG Student Department of Computer Science and Engineering Sathyabama Insititute of Science and Technology Chennai India Associate Professor Department of Computer Science and Engineering Sathyabama Instititute of Science and Technology Chennai India Assistant Professor Department of Computer Science and Engineering Sathyabama Insititute of Science and Technology Chennai India

Bone fracture is one of the most ordinary problems in mortals because of accidents or other causes. Breaking bones can occur in our body, such as the wrist, heel, ankle, hip, rib, leg, chest, etc. Fractures cannot be seen with the naked eye, so X-ray/CT images are used to detect them. But sometimes these images lack sufficient detail for diagnosis. Today image processing plays an important role in detecting bone fractures. Image processing is important for the archiving and transmission of modern data, in particular for image transmission, video encoding, digital libraries, image databases, and remote sensing. This research paper presents detailed research on image processing methods for detecting bone fractures. The following research will help in studying various techniques that can be used to detect fractures in boned through the process of image processing and also includes updating of some new techniques as an improvement.

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Detection of Online Fake Reviews Based on Text Emotion

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： S Kiruthika V Vishnu Priyan Assistant professor Department of Computer Science Engineering Sri Krishna College of Technology Coimbatore Student Department of Computer Science Engineering Sri Krishna College of Technology Coimbatore

online groups offer clients with ways to overpower a few data hurdles and limitations, like the challenge to get self-governing data about movies and for the co-occurrence of positive and negative reactions within reviews. False reviews will disturb such choices because of misleading data, causing commercial disadvantages for the customers. Recognition of false opinions has thus expected very large concentration now days. But, many websites have only concentrated on handling the problematical comments and reviews. Our work tends to categorize people opinions into groups of true orfalse polarization by employing text feature analysis. In our work, our team analyze people opinions by implementing Sentiment Analysis techniques to recognize false opinions. Sentiment Analysis and text feature categorization techniques were used to a database containing people opinions. Furthermore, the estimation reviews acquired from reviewers could be categorized into good or bad opinions, that could be utilized by a customer to choose a movie. Further the proposed technique will graph based on the classification of true and fake reviews as the analysis of good and bad reviews for a product (movie). It will help us to predict the ratio of fake reviews to true reviews easily. To estimate the accomplishment of SA technique, this work has employed accurateness, exactness, recollection and F-degree as a performance rate.

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Detecting Cardiac Arrest Based on Image Processing

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Journal of Physics: Conference Series 2021年第1期1770卷

作者： Veeranna Chowdary Muthamreddy A Viji Amutha Mary Mercy Paul Selvan Jancy UG Student Department of Computer Science and Engineering Sathyabama Insititute of Science and Technology Chennai India Associate Professor Department of Computer Science and Engineering Sathyabama Instititute of Science and Technology Chennai India Assistant Professor Department of Computer Science and Engineering Sathyabama Insititute of Science and Technology Chennai India

In the world, about 21 percent of death occurs in the age group of 28-72 years because of a cardiac arrest. Many people among us lose their life because of heart-attack. After the occurrence of heart-attack only, the patient can be monitored. To help our society from heart-attack, I am developing a system that helps to decrease the death rate and early detection of a heart attack. Thus, this system is used to save the life of many people. We detect the prior occurrence of heart-attack of a patient using their monitored lungs datasets using image processing along with IoT.

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Smart Health Care Pathology

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Journal of Physics: Conference Series 2021年第1期1770卷

作者： Nama Saikumar N. T. V. Nitesh A. Viji Amutha Mary Mercy Paul Selvan UG Student Department of Computer Science and Engineering Sathyabama Insititute of Science and Technology Chennai India Associate Professor Department of Computer Science and Engineering Sathyabama Insititute of Science and Technology Chennai India Assistant Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Wireless sensor networks have wide use in different fields such as medical, military defense. This paper discusses the primary hidden protection framework for data privacy. The hidden key, remote node, and base station are used to connect and hack through secrets. We used fiber labeling technologies to secure data transmission Between the base station and the far node. We combined those techniques with Data slice, homomorphism, data privacy and data security defense encryption technology. The only way to obtain more than a sufficient number of different secrets That can decode data from base station to remote node. To order to protect data privacy, the homomorphism encryption technology Sets up a safe and powerful network of wireless sensors.

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Efficient Feature Extraction from Multispectral Images for Face Recognition Applications: A Deep Learning Approach

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： R Sudharsanan PV Gopirajan K Suresh Kumar Assistant Professor Department of Computer Science and Engineering Karpaga Vinayaga College of Engineering and Technology GST road Padalam Chengalpattu Tamil Nadu - 603308 India Assistant Professor (SG) Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Associate Professor Department of Information Technology Saveetha Engineering College Tamil Nadu India

In recent years many face recognition algorithms were used for the identification and authentication of a person to a system. However, still, feature extraction from multispectral images was considered to be a challenging task. Feature extraction, including highlight location and portrayal, assumes a significant job in real-time security-based applications. In this paper, a novel Geometric Algebra-based Multivariate Regression Feature Extraction (GA-MVRFE) algorithm was proposed to extract features from a huge dataset stored in the cloud efficiently. This proposed algorithm works with the supreme expedient deep learning approach - Convolutional Neural Network (CNN) for image classification. CNN will automatically detect significant features from the multispectral images without any human intrusion from a huge database. Real-time images were captured with three different cameras and applied filters over the images and were created as a dataset. To show the competence of the proposed algorithm, an exclusively created dataset with a set of 14,400 image data was applied in the proposed and other existing algorithms, and their efficiency and robustness were noted. Providentially, GA-MVRFE produced better accuracy in 'Face Recognition' with a less time fraction compared with former algorithms. Obtained accuracy % for Geometric Algebra Oriented fast and Rotated Brief (GA-ORB), Geometric Algebra Fast Retina key-point Extraction Algorithm (GA-FREAK), Trilateral Smooth Filtering (TRSF), Cross Regression Multiple View Features extraction (CRMVF) and GA-MVRFE was 87.81, 83.23, 90.72, 91.67 and 97.57 respectively.

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Skin Disease Prediction and Provision of Medical Advice Using Deep Learning

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Journal of Physics: Conference Series 2021年第1期1724卷

作者： S. Hemavathi K. Jayasakthi Velmurugan Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai-44 Tamil Nadu India Associate Professor Department of Computer Science and Engineering Jeppiaar Engineering College Chennai-119 Tamil Nadu India

Skin diseases are hazardous and often contagious, especially melanoma, eczema, and impetigo. These skin diseases can be cured if detected early. The fundamental problem with it is, only an expert dermatologist is able to detect and classify such disease. Sometimes, the doctors also fail to correctly classify the disease and hence provide inappropriate medications to the patient. Our project proposes a skin disease detection method based on Image Processing and Deep Learning Techniques. Our system is Personal computer based so can be used even in remote areas. The patient needs to provide the image of the infected area and it is given as an input to the application. Image Processing and Deep Learning techniques process it and deliver the accurate output. The output is used to get the best medical cure for the disease with nearest hospital details. In this project, we present a comparison of two different approaches for real-time skin disease detection algorithm based on accuracy.

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A Comparative Analysis of using Various Machine learning Techniques based on Drowsy Driver Detection

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IOP Conference Series: Materials science and engineering 2021年第1期1119卷

作者： A Bano A Saxena G K Das Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India Professor Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India Assistant Professor Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India

In image processing or computer vision, image segmentation is a vital issue for applications such as scene understanding, medical image evaluation, robotic perception, video surveillance, increased reality or compression, etc. Every year in road accidents caused because of human mistake, the numbers of dead and injured are rising. Drowsiness and driving are particularly risky and difficult to recognize. The second leading cause of road crashes in drowsiness after alcohol. Detecting driver drowsiness is a technology of safety for vehicles that helps placed an end to driver injuries that are dozy. One of the main causes of road accidents is driver drowsiness. It is a very serious issue for road safety. We have presented various methods for detecting the drowsiness of the driverin this paper and the comparisons among such methods are extremely challenging. For this purpose, we have compared machine learning methods based on facial expression, especially on eye state. Apart from eye detection, it performed experiments on mouth detection and face detection as well. This paper explores several methods for machine learning, like SVM, CNN, or HMM. From the analysis, we have found that the HMM model achieved more accurate results in comparisonto others.

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LRFM model for customer purchase behaviour using K-Means algorithm

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C Jamunadevi S Tamil Selvan M Govindarajan C Saravanan B R Janaki Raman Assistant Professor Department of CT-PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India Associate Professor Department of Computer Applications Velalar College of Engineering and Technology Erode India Assistant Professor Department of Information Technology Velalar College of Engineering and Technology Erode India UG Scholar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India

The COVID-19 pandemics have a major collision on every aspect of life, including how people shop for their requirements. As the pandemic has reshaped life as we know, it's also initiated many trends – but the biggest of these trends may be online shopping. The shift toward online shopping was happening before the pandemic, but according to new statistics from IBM, the COIVD-19 has accelerated consumers shift toward online shopping by 5 years. The chief idea of the article is to inspect if the situation is approaching people to purchase things online and the continuation of shopping things online even after the end of pandemic. The information for the article has been gathered by circulating the survey on social networks. The questionnaire is comprised of 12 different questions, and 615 people responded to it. This work is based on LRFM (Length, Recency, Frequency, and Monetary) replica and separation of data based on the questionnaire using K-Means algorithm. Silhouette analysis helps to decide the extent of division among clusters. The results of the survey has a termination that people are fond of purchasing products online through the lockdown and people too agreed that the rate of online shopping will increase in the future when this pandemic is over.

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