Reviewerly/exHarmony huggingface dataset:
Enhanced Honey Badger Algorithm for Resource Allocation and Task Scheduling in Cloud Environment

Cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. The major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocation. The time taken to execute the tasks and cost incurred for the computation are the significant objectives that affect the performance of the cloud system. This work proposes a multi-objective task scheduling and resource allocation technique using meta-heuristic optimization algorithm. Enhanced Honey Badger algorithm (EHBA) is employed to schedule the tasks and allocate computing resources effectively while minimizing the time and cost objectives. The performance of the proposed technique is assessed in a simulation environment, CloudSim, which mimics the settings of real cloud computing system. Various measures such as Time-to-Execute, Cost-to-Compute, Task-to-Resource utilization and Time-to-Respond are used to assess the performance of the suggested EHBA method for efficient task scheduling and resource allocation. The experimental results produced by the proposed method is also compared against the state-of-the-art studies that employ meta-heuristic optimization algorithms. The outcomes revealed that EHBA outperformed other methods by executing the tasks in a minimum time with reduced cost and maximum utilization of the computing resources.
ATCRI-21 Neural Network Based Character Recognition with Image Denoising in Ancient Epigraphs

Recognizing appropriate ancient Tamil characters in epigraphy will provide detailed insights into past civilizations. The main challenges in text restoration are perspective distortion, variation in the shape of texts and non-availability of reference images. Most of the inscriptions contain a lot of noises which arise due to corrosion and other environmental factors. Due to the evolution of the language, the clustering of characters results in false positives. The conventional neural networks fail to address the above-mentioned challenges simultaneously. This article aims to assist the epigraphers through image preprocessing for denoising, feature extraction, and classification using Ancient Tamil Character Recognition in Inscription - 21 (ATCRI-21) based convolutional neural network. The preprocessing stage includes grayscale conversion, noise removal and binarization with Otsu's thresholding. Image denoising is achieved by clustering the noise due to corrosion, spots and scratches into a single cluster and the noise in the image is rejected. The character recognition is carried out through ATCRI-21 that includes three sets of a five-layer block (two convolution layers, a Rectified Linear Unit (ReLU) layer, two max-pooling layers and a dropout layer) followed by two sets of a two-layer block (a dense layer and a ReLU layer) followed by the output layer and softmax activation layer. The inclusion of regularisation layer, prevents overfitting of data. This helps us to train the network in less time. The outcome of this model is trained, validated and tested through real-time images collected from Brihadeeshwar temple, Thanjavur. The proposed model displays an validation accuracy of 80%. The outcome of this work will correlate the history of the past civilization. The proposed model unlocks a cooperative potential between epigraphers and artificial intelligence and impacts our study of important periods of history.
WEBARIO: A Web based Portal with Revolutionary Virtual Classroom and Augmented Reality Integration for Enhanced Learning

webARio is a web-based portal designed to enhance the process of learning by streamlining visualization using a revolutionary virtual classroom setup with 3D models and augmented reality integration. In this study, the authors present the development of the webARio platform, its architecture, and the implementation of its key features. Proposed system offers a user-friendly interface that allows users to access the platform from any device with internet access. The revolutionary virtual classroom setup uses 3D models to facilitate learning, enabling users to visualize complex concepts in a more engaging and interactive way. Additionally, the integration of augmented reality enhances the user experience, allowing users to visualize 3D models in the real world. The webARio platform has the potential to revolutionize the way students learn by offering an innovative and effective way to visualize and understand complex concepts. This study presents a detailed analysis of the webARio platform, its features, and its potential impact on the education sector.
Interactive Segmentation of Art Images Based on Hypergraph Convolutional Networks

Existing image segmentation algorithms often suffer from a lack of interactivity and a low level of segmentation accuracy. To solve this issue, this paper proposes the novel interactive segmentation of art images based on the Hypergraph Convolutional Networks (HCN). In order to further improve the accuracy of the image segmentation, this paper applies the interactive method based on center point correction to image segmentation based on graph convolutional neural network, and proposes a new interactive image segmentation method: based on graph convolutional neural network an interactive segmentation method for networks. In the designed model, 2 major ideas are provided, namely: (1) According to the network structure of U-Net, this paper builds an encoder by stacking coding unit blocks, and then the coding part of HGU-Net is composed of multi-layer encoders. (2) An interactive modification method is proposed to correct segmentation results in real time by adding new indicator strokes. Through the comparison testing with CNNs and U-Set, the performance is validated.
A Detailed Review of Data Deduplication Approaches in the Cloud and Key Challenges

Now a day's cloud is widely used as a storage container to store an enormous amount of data. Data is growing on the cloud exponentially as every user whether an organization, business data, or end-users too, uses the cloud to back up their data. Due to this increase in demand for storing and backup data in the cloud, its performance is degrading. While sending the data on the cloud, many users send and store the same data more than one time on the cloud which results in the inefficiency of the storage system on the cloud. As a result, it is becoming difficult to manage the data on available space. Cloud vendors are finding the key to saving disk storage which maximizes disk space capacity. To avoid the storage of redundant data and to save space for upcoming data, two approaches are suggested viz. data compression and data deduplication. Deduplication is divided into various categories. Various deduplication techniques are presented under the umbrella of each category, along with their tabular comparison. These techniques are implemented to avoid redundancy still each technique has its own limitations. Although deduplication provides many solutions for not storing repeated copies yet deduplication is facing a number of challenges. It also gives useful insight into the challenges faced in the implementation of deduplication and reviews techniques and procedures for developing new deduplication strategies.
Blockchain Technology in Rejuvenating the Media &amp; Entertainment Sector

Blockchain technology has been adopted due to its potential benefits in Media & entertainment sector. In the past few decades, the Media & Entertainment industry has experienced notable expansion therein it seeks to address its various difficulties with cutting-edge technical solutions which apparently would remove the significant barriers that stand in the way of their growth. This research study presents the analysis on blockchain smart contract and NFT for drawing attention to how piracy affects the media & entertainment sector, specifically how it harms the industry's economic value chain. Blockchain intended to build a system to stop the income leakages that are constantly occurring in this sector with Ethereum EVM. It demonstrated applications and the processes through which the economic value chain is created. And lastly, the use of blockchain networks could prepare the entertainment sector for better and more reliable payment methods via blockchain Ethereum and NFT.
Gesture-Based Control of Ping-Pong: Developing an Interactive Game Interface

Generally, games are played either through physical touch or through the keys from the keyboard. By playing games through physical touch, gamers may have many limitations like hardware malfunction (keyboard malfunctions, mouse malfunction, and power cuts), effects on the arm, Lack of physical exercise, etc. So, to minimize such limitations a game using hand gestures (dynamic gestures) can be created or developed. To give the user a more natural experience, these gestures play a major role. The major outcome/output of the work is to create a game using hand gestures that reduce manual mistakes or hardware mistakes. The motivation for doing this work is to create a direct interaction between humans with devices. It creates new enthusiasm/experience for the players. To conclude this work, one can control the movement of a keyboard cursor by tracking the user's hand for playing the game. The system has the potential of being a viable replacement for computers, keyboards, and cursors.
Exploring the Efficacy of Image Encryption Using Independent, Combined, and Improved Chaotic Maps

The necessity for enabling reliable and secure ways to preserve sensitive information has recently increased due to the quick development of digital communication and data storage technologies. The security and integrity of visual data are crucially protected by image encryption. Due to their intrinsic complexity and randomness, chaotic maps have become practical tools for image encryption. Here, the effectiveness of encryption methods is investigated utilizing one-dimensional (1D), combined and improved chaotic maps. The security study reveals in all three domains (1D, combined and enhanced), the Logistic, Tent and Sine maps are the best in Correlation, Differential, Histogram and Entropy analysis among the other maps.
A Robust Image Registration Using Multi-Scale Feature Extraction Techniques Using Deep Learning

Image registration is a significant assignment in computer visualization and medical imaging, which involves aligning two or more descriptions of the same division or object taken from different viewpoints or at dissimilar epochs. Deep learning-based approaches have recently demonstrated promising outcomes in several image-processing applications, including image registration. Using multi-scale feature extraction methods and deep learning, the study provides a reliable image registration method in this study. Three primary steps make up the suggested method: feature extraction, feature matching, and transformation estimation. A deep convolutional neural network (CNN) is utilized in the feature extraction process to extract multi-scale information from the input images. The CNN architecture is built to manage large-scale deformations by extracting features at various scales. Following feature extraction, a strong matching technique based on the nearest neighbor distance ratio test is utilized to match the extracted features. The proposed method uses the Random Sample Consensus (RANSAC) algorithm to estimate the transformation parameters robustly, which helps to handle outliers and noise in the feature-matching process. The proposed method is assessed on several benchmark datasets, including the ETH-80 dataset and the TUM RGB-D dataset, and compared with state-of-the-art approaches. The investigational consequences demonstrate that the proposed method outstrips prevailing approaches in terms of accuracy and robustness, especially for images with large-scale deformations. The proposed technique also demonstrates good performance in the presence of noise and outliers, which further demonstrates its robustness.
Determination of Hemoglobin Percentage Through K-Means Algorithm and Image Processing for White Blood Cell Diagnosis

White blood cell (WBC) diagnosis is often carried out by physicians manually, using a microscope to examine blood smears for the presence of WBCs. Due to the fact that it is a time-consuming, difficult, and prone to mistake procedure, an automated approach using a computerized system is ideal. The most critical steps of this automated procedure are the segmentation and categorization of WBCs. This work proposes an automated segmentation approach for microscopic WBC images, with a particular emphasis on images obtained from fresh blood smears. It is recommended that the segmentation be carried out utilising an integration of numerous digital image processing (DIP) techniques, which are implemented in the segmentation process. Sixty microscopic blood images were evaluated, and the suggested approach achieved a cytoplasm segmentation accuracy of 93 percent and a nucleus segmentation accuracy of 89.8 percent, respectively, on the images.
A Novel Procedure for Recognising Human Activity Using Machine Learning

In the past few years it is seen that there has been a boom in the usage of smart devices consisting of sophisticated sensors such as gyroscope or gyro and accelerometer. Human Activity Recognition (HAR) is one of these sensors' applications. Given the dependence on general Machine Learning (ML) techniques, Deep learning techniques are suggested by current research to be suitable for feature extraction from unprocessed sensor data. The study focuses on Human Activity Recognition (HAR) by means of data gathered from smartphone accelerometer sensors and employs Long-Short Term Memory (LSTM) networks in the time-series domain. Furthermore, a hybrid network consisting of a 2-layer RNN-LSTM is proposed to enhance the accuracy of recognition performance. For the input and output activation function in the neural network ReLU and Softmax function respectively are used.
An Embedded 3D Object Detection in Real-Time Using Deep Learning Techniques

The field of computer vision is currently experiencing significant growth in the area of real-time 3D object identification. This research aims to efficiently and precisely identify and locate objects in a three-dimensional space. The demand for efficient detection approaches has escalated due to the growing prevalence of sensors such as LiDAR and depth cameras in various applications, including autonomous vehicles and augmented reality. This study explores the most recent developments in utilizing deep learning algorithms for real-time 3D object recognition. It is worth mentioning that algorithms such as PointRCNN, VoteN et, and 3D-CNN s have exhibited the capability to effectively handle large and sparse point cloud data. Nevertheless, there are ongoing challenges that continue to exist in this field, including the issue of limited availability of data and the high computational demands required for real-time processing. By utilizing optimal neural network designs and integrating multi-modal sensor input, the systems presented demonstrate the potential to overcome these problems. This study provides novel research insights on the prospective trends by highlighting the potential of end-to-end learning and domain adaptability.
Optimising Lung Cancer Screening Through Advanced Techniques for Image Analysis: An Efficient Deep Learning Approach

The objective of this analysis is to identify and classify diverse types of lung cancer and propose an improved system to enhance the current best approach. The proposed system utilizes a preprocessed dataset to train and test various types of lung cancer, including Lung Adenocarcinoma, Lung Large Cell Carcinoma, and Squamous Cell Carcinoma, as well as a category for normal lungs. The accuracy of the detection methods is evaluated using a confusion matrix to ensure maximum accuracy. The detection methods for lung cancer are rated based on their accuracy in detecting the disease. This study emphasizes the significance in detecting abnormal lung cells indicative of cancer as expeditiously as possible to alleviate mortality rates and highlights the importance of computer-aided diagnosis in improving accuracy. The proposed approach achieved an impressive accuracy of 93.11%
Hybrid Characteristics are used in an Efficient Framework for the Identification of Gastrointestinal Diseases

Medical fields frequently use machine learning (ML) algorithms for disease classification and diagnosis. These algorithms base their judgments on features that have been retrieved from images. To achieve high classification accuracy choosing proper feature extraction methods and Machine Learning (ML) algorithms is crucial. This study discovers a dearth of knowledge on computer learning techniques for GI disease classification through the literature review. To address this problem, five classes of GI images are gathered from a freely accessible mdi dataset and extracted textural features like RGB and HSV (Hue, Saturation, and Value) color characteristics GLCM Matrix shape data from HOG. These characteristics are integrated, and the size was decreased by Principal Components Analysis (PCA). Last but not least, the chosen features are classified using the well-known ML classifier OAA-SVM -One Against All-Support Vector Machine, achieved classification precision, recall, FI-Score values, and accuracy of 98.8%, 95.44%, and 98.64% respectively.
An Optimizing Crime Detection in Social Media Platforms Using Multiagent Ontology-Based Approach

Social media platforms have grown exponentially in recent years, creating both opportunities and challenges for crime detection. As criminal activities and suspicious behaviour often manifest through social media content, developing efficient and effective methods for detecting them is essential. The ontology-based agents utilize semantic analysis and natural language processing to identify suspicious patterns and structures in the data. The multi-agent optimization algorithms then utilize this data to optimize the detection process and improve accuracy. This optimization helps to improve the system's efficiency, response time, and resource allocation, the obtained results show that the proposed optimization framework offers improved detection accuracy, faster response times, and better resource allocation compared to traditional approaches. The existing approaches like The Naive Bayes model achieves an accuracy of 78.69%, and the Decision Tree model has the lowest accuracy of 74.78%. Overall, our experiments and comparative analysis Demonstrate the effectiveness of the proposed optimization framework. The results of our analysis show that our optimization framework excels in detection accuracy, response times, and resource allocation compared to traditional approaches. The proposed methods accuracy value is 99.01%, precision value is 98.87%, f1-score value is 98.76, and ROC 98.87%. This reinforces the efficacy of our proposed approach and further validates its potential for use in practical scenarios.
Comparative Analysis on Machine Learning Models for Image Captions Generation

This research paper conducts a comparative analysis on five distinct machine learning models employed for generating signature images using shared data and multiple evaluation metrics. The primary aim is to evaluate the effectiveness of these models and identify their strengths and weaknesses. The assessment of the generated image quality is performed through various criteria including BLEU, ROUGE, METEOR, and CIDEr. The results reveal that each model exhibits varying performance across different metrics, with no single model outperforming the others. However, certain models demonstrate higher scores on specific metrics, indicating their potential suitability for particular applications. The study emphasizes the importance of employing multiple evaluation metrics to accurately assess the quality of generated labels. It underscores the effectiveness of different machine learning models in this task. For instance, the CIDEr metric proves valuable when multiple tags are required for an image, as it considers tag diversity. Conversely, metrics like BLEU and ROUGE are more suitable for applications that prioritize clear and precise text by evaluating the clarity and overlap between generated and reference labels. This research underscores the significance of considering multiple evaluation methods to ensure the accurate assessment of label quality. The findings have practical implications for the development of efficient sign-making systems, providing guidance on the selection of appropriate models and measurements tailored to specific applications.
Sky Sentinel: Pioneering Real-Time Facial Recognition in Intelligent Drones

The goal of this article is to provide a method that makes use of a face recognition-powered unmanned aerial vehicle (UAV). The goal of this approach is to make it easier for law enforcement to identify suspects, track down missing persons, verify identities, and carry out age-based monitoring. A facial recognition system is an electronic tool that uses AI to recognise people by their faces. Identity verification often makes use of facial recognition technologies. The facial features of a picture are analysed in this procedure. This research details the steps required to design, build, and test an unmanned aerial vehicle (UAV) integrated with a camera and face recognition software. The UAV is used as a remote control for a robot equipped with Bluetooth technology. The suggested method proves it can adapt to different drone orientations. Drone camera angles in all 37 directions have an accuracy of 99.3 percent, proving this point. Recovery activities, rescue missions, occurrence inquiries, offender assessment, and crowd surveillance are just a few examples of how specialised software may aid law enforcement in their investigative work.
Futuristic Banking: Streamlining ATM Transactions with Fingerprint and Contactless Authentication

Modern banking relies heavily on Automated Teller Machines (ATMs), although card-based transactions have historically faced security issues. This study proposes Near-Field Communication (NFC) Card Emulation and fingerprint technology as secure alternatives for ATM cash transactions. NFC Card Emulation enables secure data exchanges within a short distance, ideal for handling sensitive information. To augment security during authentication, fingerprint technology is proposed as an additional layer of protection. By employing fingerprint sensors, user identification can be established without reliance on physical cards or NFC tags. This integration ensures a heightened level of security during the authentication process, surpassing the current card-based approach employed in ATMs. The system eliminates the need for physical cards, enhancing security and mitigating risks such as ATM tampering and magnetic strip damage. A high level of security is guaranteed during ATM transactions as authentication becomes more effective and secure. This novel strategy strengthens ATM security and provides users with a seamless and safe banking experience by incorporating NFC Card Emulation and fingerprint technologies.