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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Chola, Abhinav Awasthi, Lalit Kumar Singh, Samayveer Department of Computer Science and Engineering Dr B R Ambedkar National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar India

ISBN: (纸本)9789811998751

Data privacy has been a significant concern since the beginning of the Internet, and in the current scenario, the need and importance of data privacy are increasing exponentially. Today, a multitude of media formats including text, images, videos, Graphics Interchange Format (GIFs), etc., are available for exchanging data, and techniques of cryptography and steganography play a key role in maintaining data secrecy during such transmissions. This paper elaborates on the GIF-based techniques of steganography. It provides a detailed comparison of the working, advantages and limitations of the major approaches developed in this field in the last three decades. It provides a clear and chronological understanding regarding the evolution of GIF-based steganography methods and discusses future directions to enhance the existing techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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Application of Machine Learning and Cloud Computing for Observing Health Status of Patients Remotely in Healthcare System

Application of Machine Learning and Cloud Computing for Obse...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Manivasagam Kumar, Anshu Murshleen, Mohd. Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350344387

Strategies that may gain insight from both labeled and unlabeled data as well as from their environment are being developed through the use of machine learning. Machine learning is applied in many domains, but it is most commonly used in the healthcare sector, where it offers substantial benefits through suitable decision- and prediction-making techniques. A online system which can continuously monitor the individual's condition and generate an alert when needed is introduced by the proposed work. This suggested methodology uses cloud computing and various machine learning algorithms to store data continuously. To identify risks and provide the right treatment and diagnosis, physicians analyze medical data using machine learning techniques and tools. The range of remote healthcare services includes monitoring elderly patients, premature children, patients with chronic illnesses, and victims of accidents. The current study investigates how machine learning technologies can be used to monitor patients who are located remotely and provide real-time alerts about their condition via a remote system. © 2023 IEEE.

关键词： cloud computing machine learning remote healthcare Remote patient monitoring

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Investigating the Impact of Public-Key Infrastructure in Information Security Models for Networked Applications 5th

Investigating the Impact of Public-Key Infrastructure in Inf...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Shree, Ritu Saxena, Kumud Mehta, Deepak Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Public-key infrastructure (PKI) is poised to play a crucial function in the use of secure data technology systems. With the increasing complexity of these dais’s networks and packages, PKI gives a reliable way to electronically affirm and securely exchange critical records. This paper investigates the effect of PKI on modern-day facts security models for networked applications. An assessment of the literature is conducted to discover the limitations of various PKI‐enabled architectures and packages. The research seems at the usage of cryptography and digital certificates inside the implementation of PKI, and evaluates the different varieties of authentication, authorization, and encryption strategies in shielding networked structures. Furthermore, the paper discusses the advantages and demanding situations associated with the deployment of PKI for distinctive use instances. The findings exhibit that during order for PKI to efficiently defend networked structures, a sound authentication system need to be blended with sturdy and consumer-friendly encryption strategies. Moreover, the paper shows strategies for overcoming the demanding situations related to enforcing PKI in a relaxed and efficient way. Public-key infrastructure (PKI) is an important device in data security fashions for networked packages. PKI is a security architecture that entails the use of public and private interchangeable cryptographic keys to ensure authenticated and comfortable verbal exchange among entities. Through developing a hierarchical accept as true with device, virtual certificate are used to cozy communications and the sender and authenticity and integrity of records. This architecture gives a at ease foundation for at ease dispensed computing across multiple sites located in numerous geographical places, as it enhances confidentiality, integrity, and authentication of exchanged information. In addition, PKI can facilitate cozy statistics sharing, alleviate potential privateness issues,

关键词： Authentication

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Security Model for IoT-Based Smart Water Meter Billing System

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IEEE Internet of Things Magazine 2022年第2期5卷 59-62页

作者： Bhola, Biswaranjan Kumar, Raghvendra Sharma, Rohit Mahapatra, Rajendra Prasad Department of Computer Science and Engineering Giet University India Department of Electronics and Communication Engineering Srm Institute of Science and Technology Delhi Ncr Campus Ghaziabad India Srm Institute of Science and Technology Ncr Campus Computer Science and Engineering Department and Dean of Admissions India

The water industry faces new challenges in the sustainable management of urban water systems. There are many external factors, including the effects of climate change, thirst, and urban population growth, leading to greater responsibility for dealing more sustainably with the water sector. Coverage of costs, audit of non-revenue water (NRW), and customer demand experience for internal equity revenue are some of the key challenges for water management. In these types of problematic scenarios, IoT-based smart meters may be a suitable option to handle them. Using a smart meter, we can monitor the use of water of each household in an automated environment. Hence, as per the use of water, it can be predicted by the number of people present in the house. By getting all the above information, it can also be predicted whether a family member is absent or present. Using the reverse engineering process, an intruder may also block the whole water meter network communication system. Considering this fact, information security is badly needed for the IoT smart water meter. When designing the security model for IoT devices, the security system should be lightweight due to the low computational speed and low compact battery. The proposed work explains a lightweight security model for a smart water meter to avoid the logical or physical loss of any person by the intruder. © 2018 IEEE.

关键词： Urban growth

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Twin eye Authentication Gateway Architecture Resilient to DDoS attacks in 6LoWPAN IoT Network Using Machine Learning Techniques

Twin eye Authentication Gateway Architecture Resilient to DD...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Manjula, L. Raju, G.T. Ramaiah Institute of Technology Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi India Sjc Institute of Technology Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350300826

IoT technology establishes a platform for automating services by connecting diverse objects through the Internet backbone. However, the integration of IoT networks also introduces security challenges, rendering IoT infrastructure susceptible to cyber-attacks. Notably, Distributed Denial of Service (DDoS) attacks breach the authorization conditions and these attacks have the potential to disrupt the physical functioning of the IoT infrastructure, leading to significant financial losses and even endangering human lives. Yet, maintaining availability even when networking elements malfunction has not received much attention. This research paper introduces a novel Twin eye Architecture, which includes dual gateway connecting every IoT access network to provide reliability even with the failure or inaccessibility of connected gateway. It includes the module called DDoS Manager that is molded into the gateway to recognize the dangling of the gateway. The effectiveness of the proposed model is evaluated using dataset simulated in NS3 environment. The results highlight the outstanding performance of the proposed model, achieving high accuracy rates. These findings demonstrate the proposed network architecture continues to provide critical authentication services even upon the failure of assigned gateway. © 2023 IEEE.

关键词： Denial-of-service attack

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Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

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computer Systems science & engineering 2024年第2期48卷 341-362页

作者： Arpit Jain Nageswara Rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq Department of Computer Science and Engineering KoneruLakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the *** Health Organization(WHO)and many others advised controlling Corona Virus Disease in *** limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to *** all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)*** countries made masks mandatory to prevent *** such enforcement,automatic and effective face detection systems are *** study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without *** contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or *** paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection *** experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced *** proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing *** outstanding work also can be used in video surveillance applications.

关键词： Transfer learning depth analysis convolutional neural networks(CNN) COVID-19

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Deep Learning for the Investigation of Lung and Pancreatic Tumors 7

Deep Learning for the Investigation of Lung and Pancreatic T...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Mahadevi, M. Marimuthu, M. Poornima, I. Gethzi Ahila Vijayalakshmi, K. Vidyabharathi, D. Priya, G Sakthi Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women Department of Computer Science Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Ramco Institute of Technology Department of Cse Rajapalayam India Sona College of Technology Department of Computer Science and Engineering Salem India Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India

ISBN: (纸本)9798350341485

Among the worst malignancies in the world are lung and pancreatic, and effective treatment depends on early identification and precise diagnosis. A powerful technique for analyzing medical images, deep learning, a category of machine learning, has the potential to enhance the identification and characterization of lung and tumors of the pancreas. In this research, this study examines current developments in classification of images, segmentation, and treatment outcome prediction methods for studying lung and pancreatic cancers. Gaussian Mixture model and EM techniques are employed during preprocessing. The model for classification is then used to determine the disease prediction after segmenting the pancreatic and lung tumors. In order to help radiologists discover malignancies early, traditional machine learning methods have demonstrated encouraging results in properly recognizing lung tumors from CT images. This entails defining tumor edges from medical pictures while segmenting pancreatic tumors. Finally, deep learning offers useful methods for early identification, cancer segmentation and outcome prediction, holding significant potential for research into lung and pancreatic malignancies. To guarantee the dependability, accessibility, and generality. of models developed using deep learning in clinical practice, there are some issues that must be resolved. © 2023 IEEE.

关键词： Tumors

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Wandering Strategy-Sand Cat Swarm Optimization-Based Energy Efficient Underwater Wireless Sensor Network 2

Wandering Strategy-Sand Cat Swarm Optimization-Based Energy ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Habelalmateen, M.I. Sravankumar, B. Keirolona Safana Seles, J. Phadke, Rekha Indupalli, Tabitha The Islamic University Najaf Iraq Vaagdevi College of Engineering Warangal India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India Department of Computer Science and Engineering Griet Telangana Hyderabad India

ISBN: (纸本)9798350382952

Underwater Wireless Sensor Network (UWSN) includes a numerous number of sensors positioned in oceans and rivers to observe in underwater environment. Though, underwater sensors are limited for energy and that is tedious for replacing a battery, resulted in energy efficient is main problem. For clustering and routing, in this research proposed a Wandering Strategy - Sand Cat Swarm optimization (WS-SCSO) algorithm which enhanced network lifetime. The WS-SCSO algorithm selects the appropriate Cluster Head (CH) for network and then generates a cluster. Then, by using WS-SCSO algorithm identifies a suitable route for nodes that transmits a data. The proposed algorithm enhances a network lifetime with less energy consumption. The evaluation metrics used to evaluate proposed algorithm are alive nodes, dead nodes, total energy consumption and packet delivery ratio. The proposed algorithm attained less energy consumption of 3% at 200 rounds, 7% at 400 rounds, 18% at 600, 31% at 800 rounds and 55% at 1000 rounds which is effective than previous optimization algorithms like Improved Metaheuristics based Clustering with Multihop Routing protocol (IMCMR-UWSN). © 2024 IEEE.

关键词： Energy utilization

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Depth-Integrated CNN Approach for Effective Face Spoof Detection 1

Depth-Integrated CNN Approach for Effective Face Spoof Detec...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Singh, Ravi Pratap Singh, Siddhartha Shekhar Dash, Ratnakar Mishra, Vinita Debayani Gochhayat, Sudeep Kumar Muduli, Debendra National Institute of Technology Rourkela Department of Computer Science and Engineering Rourkela India NIIS Institute of Business Administration Department of MCA Bhubaneswar India CV Raman Global University Bhubaneswar Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

Facial recognition systems present certain benefits, notably their relative convenience, contactless nature, and nonintrusiveness compared to alternative biometric methods like voice recognition and fingerprint technology. These characteristics enhance their applicability, popularity, and overall adoption. However, the susceptibility of facial recognition to spoofing attacks poses a significant challenge. This vulnerability arises from the ease with which photos and videos of registered users can be obtained online or captured without their knowledge or consent. This article introduces a method based on convolutional neural networks (CNN) to enhance the security of facial recognition systems against presentation attacks (PA). By employing both RGB and monocular depth data, the method distinguishes between authentic and fake facial images. The initial step involves preprocessing the images to minimize background interference and improve feature extraction. Following this, the CNN analyzes the preprocessed images to extract color features from both RGB and depth data. This approach not only lowers the expenses associated with depth cameras but also improves the accuracy of classification. The efficiency of this new model has been confirmed through its training and testing on the NUAA and MSU-MFSD datasets. Quantization is implemented for model optimization to make it memory-efficient and faster to execute. © 2024 IEEE.

关键词： Convolutional neural networks

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Internet of Things Based Agribot for Smart Farming and farm Monitoring

Internet of Things Based Agribot for Smart Farming and farm ...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Srivastava, Vivek Singh, Gulbir Aggarwal, Ritu Kumar, Suneet Haryana Ambala India Graphic Era Hill University Haldwani Campus Department of Computer Science and Engineering Uttarakhand Nanital India Seth Jai Parkash Mukand Lal Institute of Engineering and Technology Department of Computer Science and Engineering Haryana Yamunanagar India Mullana Department of Computer Science and Engineering Haryana Ambala India

ISBN: (纸本)9798350375237

The agricultural business of today is data-centered, precise, and wiser than it has ever been before. This is in contrast to the impression that some people may have about the agricultural process. Almost every sector, including 'smart farming,' was rebuilt as a result of the fast rise of technology built on the Internet of Things (IoT), which shifted the industry away from statistical techniques and towards quantitative ones. These revolutionary developments are shaking up the many agricultural practices that are now in use and bringing forth new possibilities of Agribots along with a variety of obstacles. Within the context of agriculture, this article present the promise of IoT Agribot, as well as the obstacles that are expected to be encountered when integrating it with the conventional agricultural operations. This paper conduct an in-depth analysis of IoT Agribot methods related with wireless sensors that are used in smart farming and farm monitoring. The explanation of how Agribot technology assists producers throughout the many phases of agricultural production, beginning with planting and continuing through harvesting, packaging, shipping and monitoring. In addition, this paper takes into consideration the use of Unmanned Aerial Vehicles (UAV) for the purpose of farm monitoring as well as other advantageous applications such as the crop yield optimization. On the basis of this comprehensive assessment, it conclude by identifying present and future trends of the IoT Agribot and highlighting prospective research problems. © 2024 IEEE.

关键词： Internet of things

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