Reviewerly/exHarmony huggingface dataset:
Security of Image Using Watermarking Techniques and Visual Cryptography

Due to rapid growth in technology there is a need to secure multimedia data like audio, video, text etc. available on the internet. In the proposed work, visual cryptography based watermarking techniques are proposed to secure image data. The watermark is inserted followed by visual cryptography where ‘n’ numbers of shares are created. DCT and IDCT are implemented to insert the watermark. The k shares out of n are required to get the original image where watermark is authenticated. PSNR is used for comparing the original image with images obtained after decryption.
Speech Recognition of Vedic Sanskrit using Deep Learning Algorithm

In this study, we examined the use of computational techniques for accurately processing acoustic signals of human speech using digital media. Specifically, we focused on the Sanskrit language and applied a language modeling approach to improve recognition by machines. Our implementation of this approach for Sanskrit speech represents a novel approach in the field and has the potential to extract valuable information through automated processing. The ability to accurately process speech is crucial for effective human communication, and our research contributes to the development of more efficient and effective methods for achieving this goal..
An Insight into Machine Learning Techniques to Detect Anomalous Users

Anomalous user profile detection is a challenging problem in machine learning. Fake user accounts can be used for malicious activities and can cause extensive damage. This paper reviews the existing literature on user profile detection, categorizing the methods into supervised and unsupervised approaches. It also discusses the challenges and weaknesses of existing approaches and suggests possible areas for improvement. The paper provides a comprehensive overview and proposes a model to classify profiles as real or fake. Social media has made it easier for attackers to steal information and spread malicious content, which makes it important to detect and stop malicious profiles.
Acknowledgement Verification of Stored Data in Shared Cloud Resource Pool

Even if CU jealously envies the public data auditing task of stored data to a trustworthy third-party auditor to prevent communication costs as well as computing costs after data outsourcing. Practically speaking, neither TPA nor CSP can be trusted. Data may be deleted by CSPs in order to clear up storage space for financial gain or in order to protect their own reputation among cloud consumers by fabricating data loss instances. Since they do not have physical control over the outsourced data, cloud users are unable to identify the person claiming responsibility for unlawful data treatment. Internal security risks and risks to shared cloud storage still exist. On the other hand, TPA errs on occasion and acts deliberately or is the victim of other illegal activities. As a result, either the audit message might be spoofed or TPA could create a misleading audit message without performing any data verification work, in which case CSP would be up against it. In this study, we provide an innovative acknowledgment verification scheme for cloud storage services to resolve these challenging issues.
Risk Minimization Approach for Image Restoration Using L<sub>2</sub> Penalty in EM Framework

This paper presents a modified expectation maximization (EM) technique for the restoring the image from its degraded model. The proposed methodology comprises the concept of minimization of the risk associated with the L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> penalty in the maximization step (M-step) of the algorithm. This method optimizes the worst case error, and at the same time also ensures the uniform shrinkage of the wavelet coefficients by using proper regularization parameter. The proposed methodology achieves 8.77 dB which is larger than the ISNR achieved by the state-of-the art methods.
Human Pose Estimation using Artificial Intelligence with Virtual Gym Tracker

Artificial intelligence has become essential in a wide range of industries, including the fitness industry. Human pose estimation is becoming increasingly popular. Human pose assessment can be established based on Artificial Intelligence or Machine Learning techniques, where sample data is employed in system with the help of trained models, after that place the joints of human body by video or picture. After that joints of that person’s body have been confined and further utilize in a variety of purposes, such as determining a person’s gait cycle or their subsequent motions of a specialized athlete in order to study about the physical methods and approaches for acquiring his or her achievement. One major application of Human pose valuation could be in the area of gym trainer tracker which helps in struggling gymnasts in order to accomplish their goals. Machine learning technology can aid in counting repetitions of any exercise during weightlifting or CrossFit events. Pose estimation is used to identify key points, and the angle between key points (elbow and shoulder) is measured. In this research paper, we can estimate the up and down stages in the Gym tracker based on the angle’s threshold following that, the tracker predicts all 33 position key points.
Designing A Real-Time Bring your Own Device Security Awareness Model for Mobile Device Users within Namibian Enterprises

In the modern world, people rely on mobile devices for personal and business use. Bring Your Own Device (BYOD) is a modern concept that brings vast benefits to organisations and employees, where employers allow employees to use their personal devices to execute official duties. Such organisations experience reduced IT costs, increased productivity, and efficiency. Despite the benefits, BYOD cyber threats are also alarming. Spamming, online harassment, cyber stalking, and passing on viruses are common types of cyber threats. This paper mains to present the proposed real-time BYOD security awareness model for Namibian enterprises. This paper outlines the outcomes of a BYOD security awareness study which was conducted within an enterprises in Namibia. The findings revealed that the BYOD concept has been implemented within the enterprise thus there is a need for increasing mobile user awareness to preserve the organisational data, hence the findings were used to design the proposed BYOD Security Awareness Model (BYOD – SAM) using Design Science Research method.
Balance Search Particle Swarm Optimization

Particle swarm optimization approach is a swarm based procedure, used to search global optimum solution in a search domain. The Basic PSO algorithm show premature convergence to find global optimum solution (best fitness value of particle). The fitness function value may be maximum or minimum value depends on application. Relative search behaviour of PSO particles are to be guided according to the fitness value of gbest particle. This paper proposed an approach which increases global exploration and local exploitation ability of basic PSO algorithm using angular relation between solutions. The proposed approach named as Balance Search Particle Swarm optimization (BS-PSO) model search space as a concentric circular search environment. The BS-PSO algorithm maintains optimum search behavior, from inception of the search procedure to wider search space. Initially BS-PSO approach provide a controlled and directed procedure to find optimal solution using search exploitation and later on it increase exploration ability of the search procedure to capture global optimum solution. In this manner BS-PSO approach maintain a perfect diversity among solution and prevent PSO algorithm to trap in local optima and premature convergence situation.
Activity Recognition System via Unification of CNN and SVM in Complex Domain

Human automatic activity recognition is an essential part of a little interactive application which involves human-being. The main disadvantage is the person’s diverse activities. These techniques provide high accuracy and pattern recognition. Deep learning methods succeed in human activity recognition (HAR); hence our goal is to combine Convolutional Neural Networks (CNN), which work with image data and extract features from images with Support Vector Machines (SVM), a shallow architecture that maps low-dimensional space to high-dimensional space. This paper uses an intelligent system algorithm to detect human activity and recognize its pattern. Cooking, cleaning, dancing, steering, and discussion activities are to be tested. Each class has 7480 images. Applying all preprocessing techniques, experimental results shows that the cleaning class gives the highest training and testing accuracy i.e., 98.78% and 97.51%. This method achieves the highest recognition accuracy with the lowest computational cost.
A Comprehensive Study of Image Inpainting Techniques with Algorithmic approach

The process of image inpainting is indeed a method that allows you to fill in particular regions of an image using novel content by estimating from surrounding pixels, from other images, or from various sources. The term “image inpainting” refers to a wide range of techniques used to repair visual media, from restoring old photographs and films to erasing undesirable text or objects. When only individual components of a picture are missing, traditional methods can produce high-quality solutions, but they are unable to detect new elements that were not included in the picture. Improvements in the quality of image inpainting have been achieved by the use of Deep Learning techniques, which have made it possible to generate appropriate hole filling and separate objects that aren’t included in a real image. However, there is a great deal of room for development in this regard, especially in the areas of adapting to different image sizes, using free-form masks, making high-resolution textures, using fewer computational resources, and shortening the training process. The comprehensive study carried on various image inpainting techniques and their applications will be useful to the new researchers to explore the possibility of developing new efficient algorithms for further improved image inpainting techniques truer to life.
An Architectural Framework for Word level Language Identification in Mixed Script Text

As social media networks have grown in prominence in recent years, we have seen a transformation in how we live our lives. People in multilingual societies are increasingly using social media platforms. Research communities have recently begun using code-mixed data to accomplish NLP tasks involving multiple languages. This paper analyzes text representation by the code-mixed and code-switching of two different languages of separate scripts like English Roman script and Hindi Devanagari through the mixed script. Word-level language Identification. Quantifying the pattern of sentence/Text written in which language and which word is ambiguous same spelling two to three meanings in a mixed script, and spelling variation normalization of Hindi Roman one has the different spelling used for daily communications by users using word embedding techniques (word2vec, TF-IDF, skip-gram, Continuous bag of words(CBOW)). An approach that uses character-based embedding to process ambiguous words in a code-mixed text has been proposed and shows promising results in terms of spelling variation and language identification.
Estimation of Recall Values and Accuracy of Gender Identification for the Different Age Groups Based on Voice Signals

Speech is an crucial channel for exchanging ideas between human beings. The coordinated movement of the muscular part of vocal tract is main reason for generating the voice signals. So, voice signals can carry the essential characteristics of the human beings as: origin, emotions, age, ascent of language, gender, health status, etc. Sex of the human being can be recognized based on the analysis of the voice signals. This present paper mainly emphasises the gender-wise recall values, i.e., male and female, and identification accuracy of the gender for the proposed model based on several classes of the age groups of the speakers as Old, Teens, and Middle. To compute the gender-wise recall values and accuracy of gender identification for the proposed model, Mel frequency Cepstral Coefficients (MFCCs) are utilized as a parameter of the speech signals. A machine learning algorithm is used for the classification of gender. Recurrent Neural Network Bi-directional Long Short-Term Memory (RNN-BiLSTM) is used as a machine learning algorithm with the adaptive Movement estimation (ADAM) optimization technique. Recall values for the speakers that are belongs to female as well as related to old age category, are the maximum in compare to others categories. In the concern about recall values for the speakers that are related to the male, the teenage category has the maximum. The highest identification accuracy of the gender for the proposed model is computed as 92.20%. This value relates the old age category. After the computation of the recall values in the all-age groups, it is observed that the middle age group shows the worst performance for male as well as female. It is also observed that the identification accuracy of gender for middle age group shows the lowest values.
Classification using Perceptron in Low Feature Space

A perceptron can handle binary problems and can classify linearly separable data only. Solving a linearly non-separable problem with a perceptron generates numerous decision boundaries, and uses a large number of iterations for adjusting weights and bias. Such classification becomes more complicated if the problem belongs to high-dimensional feature space. Thence, either Kernel Functions, additional hidden layers, or modified activation functions are introduced. Since, in low feature space, a perceptron can proficiently handle a non-linear problem without adding layers or using kernel methods. Therefore, the proposed effort suggests the concept of shifting of origin for solving such problems in low feature space. The work also uses the properties of Euclidean distance, a mid-point, and the lines separating the two classes, to identify the efficient perceptron. As obvious, a straight decision boundary can sufficiently distinguish linear data, while a bunch of lines is still not efficacious with non-linear data. Hence, the suggested approach moves the existing origin to different points in the low feature space (or xy-plane), such that each time the origin is shifted, the plane is further divided into four more quadrants. Thus, the entire plane is divided into the smallest possible quadrants comprising multiple lines. Lines possessing a classification accuracy above 80% are selected to form a convex hull (or required perceptron) whose shape depends on the data. It means three lines form a triangular convex hull, eight envision an octagonal convex hull, and so on. Moreover, the one-vs-one approach is used to address the problem of multi-class classification. Finally, the experimental analysis revealed that using only two optimal features, the proposed approach can effectively handle both linearly separable and non-separable problems without increasing the complexity of the problem, which is also evident from the high accuracy rates achieved during classification.
Advanced NLP Based Entity Key Phrase Extraction and Text-Based Similarity Measures in Hadoop Environment

Text Analytics is the process of parsing textual content from massive non-structured sources like different blogs, emails, tweets, HTML files, full-text documents, articles and extracting relevant facts. Text Analytics in big data is a multidisciplinary domain. Information Extraction, named entity extraction, is a subdivision of text analytics. Natural language Processing (NLP) is an extensive area for linguistic analysis of the text by using segmentation, tokenization, POS tagging, and syntactic parsing. Big data along with NLP will overcome major challenges in different domains of analytics. In this paper, we have presented some common and basic approaches like the BOW model, TF-IDF Model, Vector Space Model, and Word2Vec model along with their advantages and limitations and made a comparative analysis over enormous documents for finding accurate content, and semantic relationship between words and resolving ambiguities issues. We will also propose an NLP-based information extraction framework for processing and analysis of text documents in a Hadoop-based big data environment.
Enhanced Smart Home Architecture using Deep Reinforcement Learning and Blockchain

The Advent of the technology has raised the living standard of the society and we have incorporated smart home appliances in to our lives. These smart devices communicate with each other to help us ease the household demands of the residents. These appliances can be in public, or a private network and they share crucial data and information which further helps the residents of a Smart Home. This data management among devices becomes challenging because they are all implemented on a centralized mechanism which is more prone to attack and single point of breakdown. So, with such centralized architectures, service availability can never be assured. Technologies such as combination of Machine learning, Deep Reinforcement learning and Blockchain can help reduce this risk and further helps managing these appliances. This paper will explore the scope of Deep Reinforcement and Blockchain technique in smart home application. We have designed, implemented, and assess the Deep Reinforcement learning technique and Blockchain for managing appliances of smart homes. Furthermore, we evaluate the effectiveness of our approach with that of the conventional system of smart home applications.
PSO based Web Documents Prioritization for Adaptive Websites using multi-Criteria

When a user searches for a query using ad-hoc keywords in any search engine, billions of pages are returned in response to user’s query on internet while user needs a few applicable and required pages. Then there we need to prioritize some top ranked web pages. Finding some top ranked web pages out of huge number of returned results a very challenging task and is NP-Hard problem. For an ordinary user query, millions of results are returned. Many of these pages are irrelevant. So, there is a need to rank these web pages using multi-criteria like keyword frequency, hub and authority values, active time spent on web pages, unique visitors. optimization algorithms give an optimal and acceptable results which could be used for search engine optimization, web recommendation system, web personalization, website reorganization and many more purposes. There are several metaheuristic algorithms used in the literature to address such problems like Particle Swarm optimization (PSO), term frequency inverse document frequency model(TF-IDF) ACO, HBO, FO, TLBO etc. In this paper PSO based optimization using multi criteria has been proposed for prioritizing web pages. Experimentally it has been shown that PSO based prioritization gives better ranked pages in comparison to popular GA based approach.
AVWAC based Gradual transitions detection in presence of high Object-Camera Motions and Uneven Illuminations

Advanced Multimedia Technology had provided a new lease of life to current videos and made life amazing in the field of entertainment. On the other hand, special lightening effects, rapid object-camera motions and scene blending have posed significant challenges in content based video retrieval. These factors have made special effects in close consistency with the gradual transitions to fool even a robust system. We propose an efficient and accurate gradual detection system to detect fade out and fade in transitions using a simple approach. The average value of wavelet approximation component (AVWAC) at level 5 for all the scene frames is used to analyze the presence of fade out and fade in. The position of global maxima and global minima points using AVWAC in successive frames are used to analyze and find the origin and end frames of respective gradual transitions. A fade out is detected when a global maxima is followed by a global minima whereas fade in are result of occurrence of global minima followed by global maxima. A common threshold efficiently determines the occurance of gradual transition. When the minimum value of AVWAC exceeds the threshold the peakvalley algorithm finds the occurance of the gradual transition. The technique is computationally simple and faster since the existence of either of the gradual transitions is verified using an experimentally found threshold before the analysis. The method is effective in high illuminations, rapid object and camera motion videos and fast color changing scenes when tested with real videos.
Handwritten Image Detection using DCGAN with SIFT and ORB Optical Features

This paper presents a novel approach for the Handwritten Image Detection task that combines the deep-convolution GAN (DCGAN) model with optical feature-extraction techniques, including Oriented FAST with Rotated BRIEF (ORB) and Scale Invariant Feature Transform (SIFT) using the Bag-of-Features approach. Our model uses the image and its optical features (SIFT or ORB) as the inputs to a network trained to recognise the shape and orientation of characters from each class. This study examines the effect of both regular and dense optical features. We evaluate our algorithm on the MNIST, EMNIST, and MADBase datasets. The results indicate that GANs using optical feature approaches are superior to classical GANs, with an accuracy improvement of 4.88% in the MNSIT dataset, 6.89% in the EMNIST dataset, and 0.62% in the MADBase dataset. In addition, our approach surpasses the state-of-the-art methods on the MADBase dataset.
Devanagari Lipi Recognition Using Deep Learning Techniques

Convolutional Neural Network in Deep learning is a type of deep neural networks, generally put in an application to analyze visual images. The project name entitled “Devnagari Lipi Recognition using Deep Learning Techniques” is a machine learning-based project in which we are recognizing Hindi characters by gesticulating a Hindi “Akshar” or alphabet in front of our webcam the machine will recognize which letter is being completed. For the project, we will train our machine with all the Devanagari alphabets and after the training, our machine will get expertise in recognizing Hindi alphabets in no time. Not only the machine will recognize the character but it will tell the user how to pronounce it in English by writing its English pronunciation on the screen.
A Novel Hybrid BAT-PSO Approach for Task Scheduling and Workload Forecasting for Cloud Environments

With increasing demands for data storage, processing and advanced computing, cloud computing has become almost indispensable in different domains of engineering and technology. However, cloud-based platforms are resource constrained and need to distribute the tasks in such a way that the workload is balanced. This becomes more streamlined if the estimate for cloud workload is available beforehand. Hence task scheduling and workload forecasting are non-trivial tasks which need to be executed meticulously. In this paper, a heuristic approach for task scheduling has been proposed along with a machine learning based approach for workload forecasting. The performance of the task scheduling approach has been evaluated in view of the response time and CPU utilization. The performance of the workload forecasting approach has been evaluated based on the regression, mean absolute percentage error, mean square error and number of iterations for training.