Reviewerly/exHarmony huggingface dataset:
Removal of Salt and Pepper Noise Using Hybrid Adaptive Switching Median Filter with Ant Colony Optimization Technique in Nano Electronic Applications

Background: Restoration of noisy images from the salt and pepper noise is an interesting area in the field of image processing. The restoration process can be done using various filtering algorithms. The restoration process should not affect the pixels of the original image. The problem of the existing work persists as the increase in the error rate while the dimensions as well as the image format changes. The proposed work consists of Hybrid Adaptive Switching median filtering (HASMF). The hybrid technique corrupted images’ high-density salt and pepper noise removal using Ant colony Optimization technique. This hybrid technique would remove the high-density salt and pepper noise from the corrupted images. The noisy pixel value from the corrupted images is identified and selected using the Ant Colony Optimization technique (ACO). The identified corrupted value can be replaced using the Adaptive Switching Median Filter. The switching process is carried out using the pixel by pixel with the normalized median values. The noisy pixels are identified and selected using Ant colony Optimization. The optimized values are subjected to the filtering process. The proposed method decreases the salt and pepper noise within the original image. The hybrid design approach was used in the proposed study, which used 45 nm technology combined with a Verilog-A model-based circuit that was implemented using Spintronic. It was discovered that the suggested changed task had less latency, used less space, and dissipated less power than the original. Furthermore, it was discovered that designed memory arrays were both energy and space-efficient. It does not affect the normal pixels within the original image. The comparison process has been made with the various existing algorithms such as Median Filter (MF), Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The proposed method has overcome the various performance metrics such as Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM). The results obtained have shown the significant results in terms of object measures as well as visual perception of the denoised image.
Measuring Objective Information

Abstract In the context of the sextuple model, this chapter presents a metrics system with 11 indicators to measure objective information. Under the measurement, potential, and features of the set, each indicator is mathematical explored from the methodological foundations, specific definitions, and related propositions for quantitative information analysis and information systems applications.
Automatic Text Classification for Web-Based Malayalam Documents

Text documents are important sources of information that offer insights to administrators, planners, and researchers on what the public likes and does not. They are available in the form of plan documents, government reports, journal write-ups on sports, politics, science, and technology, etc., at web portals. Since its quantity is huge, searching for them on the web is time-consuming. This calls for a real-time tool that would be quicker and effective with categorizations. Comparisons with legacy documents that provide public opinions are constant or changing over a period of time. This study examines existing processes involved in article categorization and puts forth new algorithmic techniques for the automatic classification of Malayalam web-based articles. The performance of new classifiers is evaluated by using precision, recall, accuracy, and F1 score values.
A Comparative Study of Spam SMS Detection Techniques for English Content Using Supervised Machine Learning Algorithms

In recent years, with technical improvements and growth in content-based advertising, individuals have started utilizing SMS (Short Message Service), which has resulted in a significant increase in spam SMS. African continent experiences the most spam SMS in the globe, with an average of 119 spam SMS received per month by a person. These spam SMS are unsolicited messages to users, which are disturbing and may sometimes lead to the loss of important data. There exist many spam SMS detection methods which are impacted by the inclusion of well-known words, phrases, abbreviations, and idioms. The proposed work is using supervised machine learning techniques such as Multinomial Naïve Bayes and Support Vector Machine to identify SMS as spam or ham and compare their results based on specific evaluation parameters to find the most effective technique for filtering messages. The technique is evaluated on a real-world SMS dataset including over 5572 messages. According to the findings, the Support Vector Machine algorithm is the best at classifying SMS as spam or ham with an accuracy of 98.83%.
DNA and Improved Sine Map Based Video Encryption

The security of digitally shared data like images, audio and video incurs many security and privacy-related challenges such as unauthorized access. In this work, a video encryption technique using improved sine map and DNA sequencing has been proposed. The proposed video encryption process mainly consists of three phases. In the first phase, key is generated using SHA-256. The second phase comprises of permutation of the video frame using SHA-256 key and improved sine map. In the final phase, diffusion is performed of the permutated frame by using SHA-256 and DNA sequencing, to obtain encrypted video. The experimental outcomes unveil that the obtained entropy of the encrypted video frames is approximately 8 that reflects high randomness in the encrypted frame, and it has low correlation coefficients among neighbouring pixels in all three horizontal, vertical and diagonal directions. The presented scheme of video encryption is resistant to threats like brute force attack, plaintext attack and statistical attack.
Abstractive Text Summarization of Hindi Corpus Using Transformer Encoder-Decoder Model

Text Summarization based on Abstraction is the task of generating a concise summary that captures the principal ideas of the source text. It potentially contains new phrases that do not appear in the original text. Although it is widely studied for languages like English and French, owing to the scarcity of data on regional vernacular languages like Hindi, the research in this area is still in the primitive stages. We propose a novel approach for building an Abstractive Text Summarizer for Hindi corpus using the Transformer encoder-decoder architecture. Firstly, efficient pre-trained word representations are generated using Facebook’s fastText model. Next, the Transformer model is employed to extract contextual dependencies and yield better semantic representations for a morphologically rich language like Hindi, engendering an abstractive summary. On performing an experimental evaluation on the Hindi news dataset to generate news article headlines, we achieve a ROUGE-1 precision and recall score of 0.682 and 0.598, respectively, which outperforms the state-of-the-art techniques.
Segmentation Approach for Nucleus Cytoplasm of Ewing Sarcoma

Malicious disease cancer is predominant and spreading like any other epidemic disease. Awareness on preventive and post-preventive measures is inevitable to combat this syndrome. Presently, bone cancer exceeds its limit and the gravity of attention must be focused on digital remedy. This research work is framed with major bone cancer namely Ewing sarcoma. In this direction, the basic features of Ewing sarcoma are studied, and the algorithm for automated diagnosis is developed. The algorithms (1) model-based clustering and (2) gradient-based watershed segmentation are developed to extract the nucleus and cytoplasm. To ensure quality segmentation, H &E components are separated by the deconvolution method. Parallel application of segmentation algorithms is used on the H &E component for better time factor. The features are defined to construct a classification model by using a Support Vector Machine. There are 300 images of different features selected to train the SVM classifier and 120 images are used for testing and the accuracy obtained is 91.6%.
Gradual Search and Fixed Grouping Scheme Based Variant of Genetic Algorithm for Large Scale Global Optimization

Large Scale Global Optimization (LSGO) is an identified problem in the literature and almost all Bio-inspired metaheuristic search-based optimization algorithms, including Genetic Algorithm (GA), face this problem. The article first identifies schema deception, domino convergence with genetic drift and nonseparability among the variables as the reasons for LSGO. Towards LSGO solution, the article progresses the cooperative coevolution approach, with some novel concepts in solution representation, subcomponent selection, search mechanism and static adaptation of algorithm parameters. The concepts are demonstrated on GA as it is having a strong theoretical and mathematical background. Many GA variants are derived based on the above novel concepts those better balance the exploration and exploitation abilities of an algorithm, specifically at large scale. The proposal is justified using a performance comparison with other algorithms on some of the LSGO test bench functions with 4–20 dimensions.
Comparative Analysis of Various Standards for Medical Image Compression

For efficient storage and transmitting of medical images over the internet, an adequate compression method is necessary. Modern healthcare services that rely on cloud computing use such compression methods, as often we are limited by internet bandwidth and memory to store these medical images on cloud servers. The Digital Imaging and Communications in Medicine (DICOM), which is the standard currently used for transmission and handling communication of medical data uses the JPEG-2000 standard for compression. Recent impressive advancement in computer hardware and software leads to new compression methods to be developed and used widely for many applications. High-Efficiency Video Coding (HEVC/H.265/x265) is a video coding standard that has intra-frame coding for still medical images such as X-rays, MRIs, CT Scans, etc and as an advanced video coding standard, it supports compression of medical videos such as ultrasound and sonography. HEVC can be a single format that can support the compression of still Medical images, images series, and videos as well as modern medical imaging such as 3-D X-rays and 3-D Ultrasound. This study illustrates the utilization of HEVC for the compression of Medical images and a comparison of various compression techniques that have been used here for medical image compression. The Lossless compression performance of HEVC is compared to JPEG -2000 for grayscale and RGB images. Experimental results show, that in lossless mode, HEVC performs up to $${\sim }15\%$$ better for grayscale and $${\sim }17\%$$ better for RGB images. For lossy mode, HEVC accomplishes better results than JPEG, JPEG-2000 and AVC.
Encoder–Decoder Network with Guided Transmission Map: Robustness and Applicability

The robustness and applicability of the Encoder–Decoder Network with Guided Transmission Map (EDN-GTM) proposed for efficient single image dehazing purpose are examined in this paper. The EDN-GTM utilizes the transmission map extracted by dark channel prior approach as an additional input channel of a novel U-Net-based generative network to achieve an improved dehazing performance. The EDN-GTM has shown a very favorable performance compared with most recently proposed dehazing schemes including both traditional and deep learning-based ones in terms of PSNR and SSIM metrics. To further validate the robustness and applicability of the EDN-GTM scheme, extensive experiments and quantitative evaluations on various benchmark datasets are conducted in this paper. In terms of robustness, experimental results on different benchmark dehazing datasets such as Dense-HAZE, NH-HAZE, and D-HAZY show that the EDN-GTM scheme consistently outperforms most modern dehazing approaches on both synthetic and realistic hazy data regardless of scene locations: indoor or outdoor. On the other hand, experiments on WAYMO and Foggy Driving datasets imply that the EDN-GTM can be effectively applied as an image pre-processing tool to object detection tasks in autonomous driving systems.
Liver Tracking for Intraoperative Augmented Reality Navigation System

Liver cancer is one of the main causes of cancer-related deaths in the world. To treat this deadly disease, open liver surgery is still the preferred method. Due to the complexity of the liver structure, the addition of augmented reality (AR) navigation would provide the surgeon with a necessary 3D anatomy of the patient’s liver. In this study, we propose an AR navigation system where the main focus is on providing automatic 3D model registration during the operative process. The difficulty in the process of registration, comes from the fact that the liver is not a static organ due to the deformation and movement of the soft tissue. We propose a method that finds liver movement by constantly tracking a patient’s organ during operation, using liver features detected by Shi-Tomasi Feature Detector. To be able to track only liver movement and deformation, it was important first to segment only the organ itself. Liver segmentation was performed using the image segmentation method based on HSV color space.
Digital Image Encryption using Composition of RaMSH-1 Map Transposition and Logistic Map Keystream Substitution

Digital communication of multimedia data (text, signal/audio, image, and video) through the internet network has an important role in the era of industrial revolution 4.0 and society 5.0.However, the easiness of exchanging personal and confidential digital information/data has a high risk of being hijacked by irresponsible people.The development of reliable and robust data encryption methods is a solution to this risk.This paper suggests combining modified Henon Map transposition encryption and Logistic Map keystream substitution encryption to create a novel data encryption method (called RaMSH-1).The proposed algorithm simultaneously transposes data positions and substitute data values randomly, as well as having encryption key combination or key space of 1.05 × 10 670 .A few images with various sizes and variations of color features, object shapes, and textures have been tested.Based on the results of the analysis of randomness, key sensitivity, and visual, it is evident that the proposed encryption algorithm is resistant to differential attack, entropy attack and brute force attack.
Decentralized Blockchain Technology for the Development of IoT-Based Smart City Applications

Most people nowadays are shifting to smart cities for better education, opportunity, and facilities. A smart city is a city that uses modern technology and that improves the facility or quality of the area by using modern technology, devices, or services. Researchers are using blockchain technology for the upgradation of quality of life and services. To improve the services in smart cities, people are using blockchain in various sectors of society. Blockchain is one of the technologies that help in securing sensitive data by encryption. Blockchain technology has the capability to remedy the problems of the health sector with the aid of using bringing more efficiency, privacy, and security to patient health records. The potential of blockchain can be possible because of decentralization, disturbed ledger, and inherited encryption. This paper proverbs information about the contribution of blockchain in smart cities and the use of blockchain in the healthcare sector, enhancing the data securities and also how it can secure the online transaction between users. How services based on blockchain technology can be used in the upgradation and development of smart cities to improve their services and quality of life. It also increases the privacy and security of patients’ medical data. Further, it gives knowledge about the blockchain and its implementation in improving people's quality of life. This paper also discusses the plenty of applications in healthcare in blockchain and the working of blockchain in various sectors.
Educational Development in the Postdigital Era

The recent COVID-19 pandemic has highlighted a pressing need to better theorize the role of digital technologies in education, given the widespread “mainstreaming” of software platforms and online teaching. Digital technologies are now, more than ever, structural elements of the educational landscape, rather than merely peripheral devices that augment existing practices. Drawing on the concept of the “postdigital,” recent educational research has argued for just such a shift in perspective, away from viewing technologies simply as “tools” for enhancement, and toward greater recognition of the ways digital systems and infrastructures are already profoundly interrelated with the everyday activities of teaching and learning. In particular, such perspectives emphasize the role of the digital in the economic, political, and material dimensions of education. This chapter will utilize the concept of the postdigital in order to examine the ways in which education in the Asia Pacific region appears to be increasingly embroiled in the wider sociotechnical systems and political economies of digital technology, as well as growing environmental concerns related to technology. The chapter will focus on artificial intelligence (AI) as a prominent example of current digital technology being utilized for educational development, as well as a key illustration of the “postdigital” ways technologies are interrelated with economic, political, and material dimensions of education. The specific national context for this examination will be China, which has produced one of the most ambitious strategies for the technological development of education in the Asia Pacific region.
Machine and Deep Learning Studies for Cyberbullying Detection

The internet revolution in society has various effects on our daily life such as the use of social media. While social media is ubiquitous and great in some aspects, it brings a new issue that appears more and more in today’s world. This new issue, Cyberbullying, involves harming someone by posting or sharing content that causes feelings of embarrassment, guilt, or humiliation. Easily creating fake social media accounts with fake identity further increase cyberbullying incidents and encourages cyberbullies. Cyberbullying can affect people both mentally and physically and can lead to permanent problems. However, studies in this area show that cyberbullying can be prevented. In this study, we review machine learning techniques to detect and prevent cyberbullying, evaluate the performances of the machine and deep learning models, and examine factors that affect the performance of the models. We also discuss the importance of data preprocessing, feature extraction and selection, and classification processes in cyberbullying detection problems.
CONVERGENCE OF BLOCK CHAIN AND ARTIFICIAL INTELLIGENCE

Artificial Intelligence (AI) and blockchain have recently been two of the most popular topics. Artificial intelligence (AI) and blockchain are two of the most trending innovations that would radically alter how we live, function, and communicate. The author attempts to understand the fundamentals of block chain and artificial intelligence, as well as why these technologies are so common, and what technical revolutions these two technologies will bring together. Like for every coin, there are two sides and this technology has certain drawbacks, flaws, and security concerns, which the author has discussed briefly.
INTELLECTUAL PROPERTY RIGHTS AND SOCIAL MEDIA

In general, intellectual property rights relate to a collection of intangible assets held and legally protected by an individual or corporation from outside use or implementation without approved approval, such as invention, creativity, and contributions to the relevant area of expertise. The economic process, financial incentive, and motivation for advanced innovations engrained in the appropriate legal protection of Intellectual Property Rights demand expert, guided, and constantly updated assistance in the field of Intellectual Property Rights. With the tremendous rate of technical, scientific, and medical innovation that we are experiencing today, intellectual property has become increasingly crucial. Furthermore, developments in the global economic climate have had an impact on the creation of business models in which intellectual property is a key component in building value and prospective growth. Several new intellectual property legislations have been passed in India in order to fulfil international commitments under the WTO Agreement on Trade-Related Aspects of Intellectual Property Rights. As a result, intellectual property has become one of the world's largest and fastest-growing disciplines of law, necessitating the need for specialists to deal with IP. Copyright is a broad sphere that encompasses innovations, creations, intellectual products in print, audio-visual, sign &amp; symbol, and digital formats. Copyright is a tremendous stimulant for the social media industry, which is full of invention and innovation. Media platforms, including social media, employ creative ideas, pictures, sounds, scripts, and a variety of other tools and ways of communication for professional, commercial, and individual aspirations. All of these forms, which are generated by individuals and businesses with rigorous efforts in design, research, and development, need a significant expenditure. These projects were built through the dedication of one’s time and money has yielded a profit to the creators and it has become a source of encouragement for everybody.
Gambaran Implementasi Alur Prosedur Pelayanan &amp; Penyelenggaraan Rekam Medis di RS Muhammadiyah Selogiri, Wonogiri

Background : Medical records are managed based on a standard structure, provided that the implementation system is evaluated to produce information and has a confidentiality standard that must be maintained. The medical record document is in principle stored properly in the hospital so that it is easy to retrieve every service information need for patients.&#x0D; Objective : To find out how the implementation of service procedures and management functions as well as the implementation of medical records at the Selogiri Muhammadiyan Hospital.&#x0D; Methods : The type of research used in this study is descriptive with a qualitative approach. While the research design used is phenomenology.&#x0D; Results : The management system of medical record data at Muhammadiyah Selogiri Hospital which includes assembling, coding and indexing, analizing and reporting, filing. The medical record system in the Wonogiri Selogiri Muhammadiyah Hospital includes: The Naming System uses the patient's own name in accordance with valid identification such as KTP, birth certificate, SIM, etc. The numbering system uses the Unit Numbering System (UNS). The system uses the Digit Filing (TDF) and Straight Numerical Filing (SNF) terminals.&#x0D; Conclusion : It is necessary to incorporate the SNF alignment system into the TDF alignment system as a whole to facilitate and accelerate the retrieval of medical record documents. Filling officers need to increase accuracy in storing medical record documents so that missfile does not occur frequently.&#x0D; &#x0D; Keywords : Procedure Flow, Procedure of Service and Management Functions, Medical Record Implementation