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

作者： R. Raja Kumar R. Pandian T. Prem Jacob A. Pravin P. Indumathi Professor Mathematics Department Sathyabama Institute of Science and Technology Sholinganallur Chennai India Associate Professor Department of Electronics and Instrumentation Engineering Sathyabama Institute of Science and Technology Sholinganallur Chennai India Associate Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Sholinganallur Chennai India Professor Department of Electronics Engineering MIT Campus of Anna University Chennai India

Seemingly random, chaotic dynamic systems have state variables that move about in a non periodic, bounded fashion. The sensitivity to initial conditions of chaotic signals also holds an interesting pattern. A seemingly tiny change in the values of initial condition, can greatly affect the values of output. This means that the cross-correlation of two chaotic functions of same source is very low. This pattern of chaotic functions makes their use in the field of cryptography more interesting. A more complex version of chaos is the multiple-chaos, where two or more chaotic functions are used, enhances the security of the procedure. Additionally the dynamically varying look-up table, which is being transmitted along with the encrypted message, ensures high reliability in transmitting the data. Overall, this method ensures high security and high reliability in transmitting the data.

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Comprehensive Analysis of Lightweight Cryptographic Algorithms for Battery-Limited Internet of Things Devices

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International Journal of Distributed Sensor Networks 2025年第1期2025卷

作者： Nahom Gebeyehu Zinabu Yihenew Wondie Marye Kula Kekeba Tune Samuel Asferaw Demilew Department of Software Engineering College of Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia aastu.edu.et Associate Professor in School of Electrical and Computer Engineering (SECE) Addis Ababa Institute of Technology (AAiT) Addis Ababa University (AAU) Addis Ababa Ethiopia aau.edu.et Department of Information Technology College of Computing Debre Berhan University Debre Berhan Ethiopia dbu.edu.et

The growth of billions of devices functioning in resource-constrained situations in the Internet of Things (IoT) era poses serious security issues. As a result of their limited processing power efficiency, memory, and battery life, traditional cryptographic algorithms like elliptic curve cryptography (ECC), Rivest–Shamir–Adleman (RSA), and Advanced Encryption Standard (AES) are sometimes too resource-intensive for these devices. Lightweight cryptography has become a crucial field as a remedy, created especially to maintain security while maximizing energy efficiency and reducing resource usage. With an emphasis on contrasting well-known lightweight block ciphers and pointing out areas for future research, this paper examines the most recent developments in lightweight cryptographic methods. In order to synthesize insights into present issues and future goals, it explores cutting-edge methods that improve performance, security, and energy efficiency. Among the main trends that were covered were the trade-offs between resource limitations and security strength, hardware–software co-optimization, block and stream cipher optimization, and hybrid encryption techniques. The study’s conclusion emphasizes how urgently more research is needed to create cryptographic solutions that can sufficiently safeguard IoT devices.

关键词： battery-limited devices block cipher algorithm embedded system security energy-efficient cryptography IoT security lightweight cryptography low-power cryptographic algorithms and performance analysis

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An efficient machine learning approach for activity recognition

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

作者： K Jashwant R Jasvant S Siamala Devi UG Student Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamilnadu. Associate Professor Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamilnadu.

The population of older people in western nations are growing drastically. Independent lifestyle is their preference and this leads to fall instances often. Falls of this kind results in severe health issues or sometimes causes deadly damages to the elderly people. Considering this problem, it is highly important to come up with fall discovery systems. A machine learning framework has been proposed in this regard which covers both fall discovery and day to day movement recognition. Utilizing acceleration and speed inputs from dual unrestricted repositories are highly helpful in diagnosing to the maximum of seven movements. Acceleration and angular velocity parameters can be used to extract the attributes that are relevant to time and frequency domain. These attributes are then offered to a classification algorithm. An attempt has been made to check the outcomes of four different procedures for categorizing manual movements. The above-mentioned procedures are the Artificial Neural Network (ANN), K-Nearest Neighbours (KNN), Quadratic Support Vector Machine (QSVM), and FP growth. Power spectral density of the acceleration is used to maximize the outcomes of the classifier. Acceleration data alone is considered for activity discovery in the initial step. Experimental results show that the KNN, ANN, QSVM, and FP growth procedures can attain correctness of 81.2%, 87.8%, 93.2%, and 94.1%. The accuracy of fall detection touches 97.2% and 99.1% with no false positive values for the QSVM and FP growth procedures. As a next phase, the attributes can be taken autocorrelation function and the power spectral density of the acceleration and the angular velocity data that obviously maximizes the classification correctness. Projected attributes are adopted to achieve notable improvement in accuracy of 85.8%, 91.8%, 96.1%, and 97.7% for the KNN, ANN, QSVM, and FP growth algorithm. The accurateness of fall discovery touches 100% for both the QSVM and FP growth procedures in the absenc

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Analysis of image inpainting and object removal methodologies

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

作者： Shiney Thomas Juby Mathew Research Scholar Department of Computer Science and Engineering Amal Jyothi College of Engineering Kanjirappally Kottayam Kerala India Associate Professor Department of Computer Science and Engineering Amal Jyothi College of Engineering Kanjirappally Kottayam Kerala India

Image inpainting is a process that tries to fill in missing parts of a degraded image or remove objects from an image, but maintain the realistic content in the image. Various methods can be employed to perform inpainting. Here three benchmark methods of which two are conventional and other GAN based are evaluated to check the effectives of the inpainting process, for different class of images. The visual quality evaluation as well as quantification parameter namely the SSIM score was computed. Based on the test score it was seen that the GAN based method was capable of providing visually good restored images with an average SSIM score of 0.9516, which is an indicator on the quality of the image. The GAN based methods can be further enhanced to provide more realistic images by incorporating functions to extract both local and global contextual content.

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Multiple Watermarking Scheme for Video & Image for Authentication & Copyright Protection

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IOP Conference Series: Materials science and engineering 2021年第1期1131卷

作者： Neha Janu Ankit Kumar Pankaj Dadheech Gajanand Sharma Ashutosh Kumar Linesh Raja Associate Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Science & Engineering JECRC University Jaipur Rajasthan India Assistant Professor Department of Computer Applications Manipal University Jaipur Rajasthan India

Watermarking has been used frequently to authenticate the accuracy and security of the image and video files. In the world of computer technology, several watermarking strategies have developed during the past 20 years. Integrate a picture of identity that is not always concealed, such that no detail is not possible to delete. A monitoring code can also be used to deter unauthorized recording equipment. Another application is the watermark copyright control, which works at stopping the creator of the image from stealing photos unlawfully. A watermark is a promising option for the copyright transcendence of multi-media files, as embedded messages are still included. Due to the limits of fidelity, a watermark may be implemented in a small multimedia data space. There is no proof that Automated Watermarking technologies will fulfil the ultimate purpose of the cleaners of all sorts of copyright security operations to gather knowledge from the data they obtain. Relevant situations may be deemed more fairly expected with the usage of automated copyright marking technologies. A perfect device will not be able to add a digital watermark without the limit, which does not supply the whole object with details. In this work, a modern technique for watermarking includes injecting two or more messages or photographs into a single picture for protection purposes and repeating a similar procedure for N-frames for authentication in the film.

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Investigation of applying various filters for traffic sign board detection using convolution neural network

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AIP Conference Proceedings 2021年第1期2387卷

作者： K. Kousalya R. S. Mohana E. Kavitha T. Kavya S. Keerthana 1Professor Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu 638060 India 2Associate Professor Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu 638060 India 3UG Student Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu 638060 India

Autonomous cars must take real-time decisions about surroundings to reduce death rates during traffic accidents. Traffic related information's are available in the Traffic sign. It assists to drive better and safer. In the traditional method, the traffic sign is detected by manually or computer vision methods. Those are time consuming ***-one will be aware of all traffic signs, so it will let everyone know and learn the signs easily. Traffic sign recognition is just one of the problems that computer vision and deep learning can solve Convolution Neural Network (CNN) ***-to identify traffic signs from the German Traffic Signal Recognition Benchmark (GTSRB) dataset that contains forty-three classes. The proposed system has three working stages: image pre-processing, detection, and recognition. Initially, the traffic sign image is pre-processed, and the detailed information present in the traffic sign image is detected by using the histogram equalization method, which improves the contrast of the traffic sign image. After preprocessing, the features of the images are extracted by using CNN architecture with three non-linear activation functions such as Re-Lu, Leaky-Re-Lu and sigmoid. The experimental results compare the results of the above three non-linear activation *** activation function Re-Lu and Leaky Re-Lu achieved accuracy above 95%.After feature extraction, the output layer is used to predict the traffic sign images.

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Daily stress classification using functional near infrared spectroscopy

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

作者： S Siamala Devi Elamparithi V Gowtham B Julian Sharon Associate Professor Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamilnadu India. UG Student Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamilnadu India.

Any mental or physical pressure can be termed as a stress. Stress usually originates from any action or feelings that make a person to feel irritated or panic. An external inducement, action or an atmosphere that pressurizes an individual person is known as a stressor. Several studies related to mental stress have pointed on the insight of psychological state in the presence or absence of investigational stressor. Anyhow, considering psychological state without investigational stressor will not precisely signify the no stress state as people naturally experience extensive pressure in their day-to-day life. It is high time to improvise the stress detection in an exact manner. In this paper, functional near-infrared spectroscopy (fNIRS) was evaluated in 41 healthy members to enumerate their prefrontal cortical oxygenation while executing a reasoning task as an investigational stressor, by considering distinct strain level. The primary six attributes are mined which includes slope, mean, standard deviation, peak, skewness, and kurtosis of the oxygenated hemoglobin concentration. With the help of Support Vector machines (SVM), by considering the several attribute combinations and its time frame, the daily stress and mental stress were properly classified. Our discoveries show that the higher the use of web-based media, the higher the danger of sadness, with young ladies being exposed to the most elevated danger. An early misery locator is proposed to track and control this danger factor of web-based media usage. We similarly misuse multi-source learning in SNMDD and suggest another SNMD-based Tensor Model (STM) to advance the exactness. To extend the flexibility of STM and to additionally improve the capability with execution guarantee. Our structure is assessed through a customer concentrate with 3126 online casual network customers. A segment examination is applied, and besides applies SNMDD for huge extension datasets and analyses the features of the three SNMD types

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AutoAssociative Memory for Face Recognition using High-Dimensional Octonion Data

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IOP Conference Series: Materials science and engineering 2021年第1期1042卷

作者： Shoba Rani Salvadi D. Nagendra Rao S Vatsal Associate Professor Department of Computer Science and Engineering Vardhaman College of Engineering Shamshabad Telangana India. Principal & Professor Electronics and Communication Engineering JBREC Moinabad Telangana India. Professor Electrical and Electronics Engineering Institute of Aeronautical Engineering Telangana India

Uniqueness or individuality of an individual face is the representation of one's identity. Recent research in color image processing focusing on octonion algebra, which is the generalization of complex and quaternion algebra. In this paper, the face of an individual is used for identification making automatically. For this, an autoassociative memory is used for the recognition of images or videos with 8-dimensions. Octonion algebra is not commutative and not associative but it is an alternate algebra for representing the phase and angle of images effectively. The prospective experiments are conducted on color images for image reconstruction, denoising and efficiently retrieving blurred images in true color images.

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An empirical analysis of machine learning frameworks for digital pathology in medical science

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

作者： S.K.B. Sangeetha R Dhaya Dhruv T Shah R Dharanidharan K. Praneeth Sai Reddy Assistant Professor(Sr.G) Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai-600026 India Associate Professor Department of Computer Science and EngineeringKing Khalid UniversityKingdom of Saudi Arabia UG Student Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai-600026 India

Digital pathology is a technology that allows pathological information created from a digital slide to be accessed, handled, and interpreted. Using optical pathology scanners, glass slides are collected and transformed to digitized glass slides that can be viewed on your computer monitor. Relevant support for education and the practice of human anatomy is offered by digital pathology. With the recent developments in digital pathology led to computer-aided diagnosis using machine learning approaches. So, machine learning frameworks assist physicians in diagnosing critical cases such as cancer, tumors, etc and improve patient management. With an ever growing number of choices, it can be hard to pick a better machine learning method for pathological data. Big potential attempts are made in this paper to research the full context of digital pathology with the specifics of how artificial intelligence has contributed to digital pathology. This review also analyzes various machine learning frameworks by providing as much information as possible and quantifying what the tradeoffs will be. This paper ultimately provides the improvements in the frameworks available that will be required in the near future applications.

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Attendance Management System Using Face Recognition Method

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Journal of Physics: Conference Series 2021年第1期2089卷

作者： Syed Mansoora Giribabu Sadineni Shaik Heena Kauser P.G Scholar Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India Associate Professor Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India Assistant Professor Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India

When it comes to classroom management, the attendance check is a critical component. Time-consuming, particularly when it comes to open meetings, is checking attendance by calling names or by handing around a sign-in sheet to make it easier to commit fraud. An implementation of a real-time attendance check is described in this article in great detail facial recognition system and its outcomes. The system must be able to identify a student's face in order for it to work first snap a photograph of the pupil and save it in a database as a reference for future use. During the event, there were students may be identified by using the webcam, which captures photos of their faces auto-detects faces and selects students with names that are most likely to match, and lastly, depending on the facial recognition findings, an excel file will be updated to reflect attendance. To identify faces in webcam footage, the system uses a pre-trained Haar Cascade model. As a result, a 128-bit FaceNet has been generated by training it to minimise the triplet loss. The dimensions of the facial picture. When two facial pictures have similar encodings If the two facial pictures are from the same student or different. Use of the system as part of a class, and the outcomes have been extremely positive. There has been a poll done to find out more about There are both advantages and disadvantages to using a college attendance system.

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