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An ECC-based authentication protocol for fog-IoT enabled smart home environment

International Journal of Intelligent Systems Technologies and Applications 2021年第1期20卷 25-46页

作者： Mohanta, Bhabendu Kumar Jena, Debasish Patnaik, Srikanta Department of Computer Science and Engineering International Institute of Information Technology BhubaneswarOdisha751003 India Department of Computer Science and Engineering SOA University BhubaneswarOdisha751030 India

Internet of things (IoT)-enabled smart home means all the home appliances are connected to the internet. All these devices are monitored and controlled using a mobile device, iPhone, or iPad regardless of location or time constraint. In an IoT-based smart home network, associated users and devices need to be recognised otherwise unauthorised access reduce the security and privacy of the smart environment. In this paper, contributions are first proposed as an elliptical curve cryptography-based authentication protocol for fog nodes and users in the IoT-based smart home environment. Secondly, the simulation for security analysis of the proposed authentication protocol using AVISPA tools has been done. Thirdly, BAN logic is used to verify that the devices in the smart home environment have achieved the security goals. Finally, a comparison with some existing works has been done and it has been seen that proposed protocol achieved better security goals in a smart home environment. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Authentication

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Insight into Diverse Keyphrase Extraction Techniques from Text Documents 1st

Insight into Diverse Keyphrase Extraction Techniques from Te...

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1st International Conference on Intelligent and Cloud Computing, ICICC 2019

作者： Parida, Upasana Nayak, Mamata Nayak, Ajit Ku. Computer Science and Engineering SOA Deemed to be University Bhubaneswar India Computer Science and Information Technology SOA Deemed to be University Bhubaneswar India

ISBN: (纸本)9789811559709

Keyphrase refers to a set of terms which best present the document content in a brief way. As the world is already approached toward digital documents, it is more crucial to find out a particular document accurately and efficiently. This can be achieved by keyphrase extraction as it describes the core information of documents which will be helpful to find target documents for the users. The keyphrase extraction is one of the challenging research areas in the natural language processing field because of the involvement of the voluminous unstructured and unorganized data. This paper surveys various keyphrase extraction technologies and their respective families. A systematic review of this paper provides an overview of the existing technologies with their pros and cons, and explores the direction for future development and research in this field. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Extraction

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Tomato Leaf Disease Detection using Neural Networks 1

Tomato Leaf Disease Detection using Neural Networks

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Chaitanya, Vemakoti Santosh Krishna Rakesh, Datti Dash, Sachikanta Sahoo, Bidush Kumar Padhy, Sasmita Nayak, Mamata Giet University Computer Science and Engineering Odisha Gunupur India Gmr Institute Institute of Technology Jntu Computer Science and Engineering Andhra Pradesh Kakinada India School of Computing Science and Engineering Vit Bhopal University MP Bhopal India Soa University Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9781665454933

Plants are a very important source of food worldwide. The diseases among them generate production and economic losses leading to a decrease in agricultural product quality and amount. Disease watching by hand is long and susceptible to mistakes. One of such plants is the Tomato plant, the foremost wide consumed vegetable in the Asian nation. Early illness detection is to scale back future losses. It is so crucial that a Convolutional Neural Network (CNN) with two convolutions and two max-pooling layers, together flattening layer followed by two totally connected layers is employed during this study in order to diagnose herbaceous plant diseases. Pictures of tomato leaves with 9 diseases and healthy samples were accustomed to ensure the findings. The images area unit metameric mistreatment image scaling, color threshold and flood filling techniques are used for findings. With our classification drawback mistreatment the notion of transfer learning. Within the tomato crop, nine of the foremost common leaf diseases and one healthy leaves were chosen for categorization. The projected model has a mean accuracy of 91.2 % for the 9 disorders and one healthy category. © 2022 IEEE.

关键词： Convolution

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Stacked Layer Based Deep Learning Approach for Fake News Classification

Stacked Layer Based Deep Learning Approach for Fake News Cla...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Pradip Dhal Deb Biswas Pragya Jayshree Patra Manima Asish Mishra Prakash Kumar Jha Department of Computer Science and Engineering ITER SOA University Bhubaneswar India

The growth of the global population”s social., economic., and emotional spheres is impacted by fake news and misleading information. Several classification systems are available to help identify fake news., but they needed to be quicker., more secure., and as trustworthy as was required at the time. Since manually confirming the integrity of news is difficult and expensive., researchers have shown much interest in this area. Different techniques for spotting fake news., including sentiment-based., image-based., content-based., social context-based., and hybrid context-based categorization., were examined. In this paper., we have developed a lightweight Deep Learning (DL) network for Fake News Classification (FNC)., a sentiment-based classification. The deep network consists of a stacked layer based feature extraction approach. Here stack layer first extracts the global features by the network of Bidirectional-Long Short Term Memory (Bi-LSTM) and Bidirectional-Gated Recurrent Unit (Bi-GRU). The results of the global features we have extracted the local features by the Convolutional Neural Network (CNN). The experiment findings show that the suggested lightweight DL network is more successful in producing superior results than the other methods.

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Unmanned Integrated Autonomous Vehicle: Swayambhu 2

Unmanned Integrated Autonomous Vehicle: Swayambhu

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2nd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2022

作者： Anand, Ankit Cherukuri, Murthy Samanta, Indu Sekhar Swain, Kunjabihari Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India Iter SOA University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9781665478397

Every human life is valuable. Soldiers are in charge of security at the country's borders. Many soldiers were killed as a result of terror strikes on the frontiers. With advancements in science and technology, Security systems have improved significantly. Robotics is one of the technologies that are now receiving significant attention. The development of autonomous decision-making robots' potential to kill terrorists is one of the most remarkable breakthroughs. These robots can operate at locations where humans cannot reach them. These bots can also execute self-exploiting actions without endangering the soldiers' lives. This autonomous vehicle is intelligent enough to detect buried landmines, bombs, and weaponry, pick bombs with its 6-axis hand and throw them away. The term 'autonomous' refers to a system that can control itself and is controlled by the authorized deputed user. This autonomous vehicle has an embedded camera that sends video streams to the headquarters and can also detect suspicious behavior and respond appropriately. This article presents the design and working of an unmanned autonomous vehicle named 'Swayambhu.' © 2022 IEEE.

关键词： Autonomous vehicles

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Generating Higher Order Mutants using PSO with Levy Flight(LFPSO) Algorithm 1

Generating Higher Order Mutants using PSO with Levy Flight(L...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Mohanty, Subhasish Mishra, Jyotirmaya Mohapatra, Sudhir Kumar Dash, Sachikanta Padhy, Sasmita Das, Sameer Kumar Computer Science and Engineering Giet University Odisha Gunupur India Sri Sri University Faculty of Emerging Technologies Odisha India School of Computing Science and Engineering Vit Bhopal University MP Bhopal India Computer Science and Engineering Soa University Odisha Bhubaneswar India

ISBN: (纸本)9781665454933

Nowadays, test data generation via a search based become popular, and mutation testing plays a very essential role by improving the efficiency of test data creation. In this research, LFPSO with Mutation Testing, a high-order mutation test data generating method, was presented, which was then implemented on large programs from the Software-artifact Infrastructure Repository (SIR). The suggested algorithm LFPSO is then compared against GA, as well as the most extensively used method for developing test data, CRO. For the majority of the benchmark programs, the results show that LFPSO outperforms GA and CRO in terms of computing efficiency. © 2022 IEEE.

关键词： Software testing

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Image Colorization Using Auto Encoders

Image Colorization Using Auto Encoders

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Susmita Panda Mohit Ranjan Panda Abhigyan Prakash Dept. of Computer Science Engineering ITER SOA University Bhubaneswar Odisha India School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Image colorization is a useful technique in various fields such as photography, art, and design. It can be used to restore old photos or add color to artistic renditions of real-world scenes and objects to make them appear more natural. It is a challenging task because it requires the model to understand the context and semantics of the image to predict accurate colors. The code uses a convolutional neural network (CNN) to predict the color of each pixel in an image based on its surrounding pixels. These techniques can be used for the restoration of old black and white photos. By colorizing these photos, they can be brought to life and preserved for future generations. This paper can benefit various domains such as art, history, and education. For instance, museums and historians can use this proposed method to enrich their collections and documents by adding color.

关键词： Training Photography Power engineering Accuracy Art Image color analysis Semantics Predictive models Image restoration Convolutional neural networks

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BXrayBLR: Experimental Analysis & Evaluation of the Performance for LR Model on BC Prediction

BXrayBLR: Experimental Analysis & Evaluation of the Performa...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Sashikanta Prusty Sushree Gayatri Priyadarsini Prusty Ghanashyam Sahoo Jyotirmayee Rautaray Sasanka Sekhar Dalai Manjog Padhy Department of Computer Science & Engineering SOA (deemed to be University) Bhubaneswar India Department of Computer Science & Engineering GITA Autonomous College Bhubaneswar Odisha India Department of Computer Science & Engineering OUTR Bhubaneswar Odisha India

In the recent two decades, cancer has been the leading cause of death around the globe. Breast Cancer (BC) is the second-highest, causing a maximum number of deaths globally these days. This, comprises pharmaceuticals, medical and surgical supplies, medical gadgets and equipment, and other products required to assist doctors, nurses, and other healthcare individuals. This work aims to address the requirement for BC specimen collection within healthcare organizations, as well as to predict BC before it happens. Firstly, we have carried out a methodology to determine the necessary steps for predicting BC. Secondly, breast screening and testing using X-ray techniques. Thirdly, a model called logistic regression (LR) to implement on breast mammograms to predict the BC disease. In the end, performance for this model has been measured by using precision, recall, f1score, and accuracy. Finally, it was found that the LR model achieves an accuracy of 97.36 % while detecting BC from breast mammograms. However, an ROC (Receiver Operator Characteristic) curve has been plotted, representing the false positive rate (FPR) and true positive rate (TPR) on the x and y-axis respectively. Thus, defining the LR model predicts the BC closer to ‘1’, which might be a greater advantage for the doctors.

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Approach for Preprocessing in offline Optical Character Recognition (OCR)

Approach for Preprocessing in offline Optical Character Reco...

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2022 International Conference on Interdisciplinary Research in Technology and Management, IRTM 2022

作者： Dey, Raghunath Balabantaray, Rakesh Chandra Mohanty, Surajit Singh, Debabrata Karuppiah, Marimuthu Samanta, Debabrata Iiit Bhubaneswar Department Of Cse Bhubaneswar India Driems Department Of Computer Science And Engineering Cuttack India Soa University Department Of Ca Bhubaneswar India Srm Institute Of Science And Technology Department Of Computer Science And Engineering Ghaziabad India Department Of Computer Science Bangalore India

ISBN: (纸本)9781665478861

offline optical character recognition (offline OCR) is one of the important applications of pattern recognition. To achieve a better recognition result, the input character images must have good quality. That is why the preprocessing step be-comes essential for any image identification task. Lots of research has been performed in numerous jobs towards this preprocessing in the literature. Here, an attempt has been made to summarize different procedures and aspects of preprocessing adopted in implementing these preprocessing techniques. This is done in the hope that this may help the research community towards the gaining of knowledge of different preprocessing techniques used in offline OCR. offline OCR has several applications, such as old manuscript digitization, signature authentication, bank cheque automatic clearance and postal letter sorting, etc. Finally, an overall summary in a concise way has been presented based on different preprocessing techniques used in offline OCR. © 2022 IEEE.

关键词： Image enhancement

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DCTSPA: A Proposed Model for Controlling Parking Spots

DCTSPA: A Proposed Model for Controlling Parking Spots

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2021 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2021

作者： Prusty, Sashikanta Patnaik, Srikanta Dash, Sujit Kumar SOA University Department of Computer Science and Engineering Bhubaneswar751030 India Engineering SOA University Department of Electrical and Electronics Bhubaneswar751030 India

ISBN: (纸本)9781665425063

Urban cities in India are becoming smartness when it has properly synchronized with parking lots. In recent years urban cities in India are facing a lot of challenges during the availability of parking areas for their citizens as it has not well organized like assign parking lot at people immediately when it is free, squeeze the parking space according to size of vehicles and many more. In this paper this proposed a model called DCTSPA (Data;Collect;Transmit;Storage;Process;Application) that would be undertaken for the city planner, government and citizen for providing smart parking system in urban cities. This model describes about how data are collected, transmitted, storage, processing and providing good services to their citizen in terms of quality and availability. While providing smart parking system, first of all adding Internet-Of- Things (IoT)-enabled sensors in parking areas that collect the data for processing with help of deep learning algorithms and assigns parking areas as per people's requirement. Smart IoT devices with Deep learning technology delivers powerful performances in smart cities like as able to recognize hazards from many single pictures to give better accuracy. This proposed work mainly focus on two latest technology called as i) Picterra, an Artificial Intelligence (AI) based software designed in such a way that can identify, map objects through satellite and also able to get actionable insights faster and the ii) second one is the CivicSmart, that has smart parking meters programmed with deep learning algorithm which might fix in the residents, malls and any other parking spots to reduce the vehicle waiting time, traffics and many more in Smart City framework. © 2021 IEEE.

关键词： Internet of things

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