Reviewerly/exHarmony huggingface dataset:
Enhancing Image Quality: A Comparative Study of Spatial, Frequency Domain, and Deep Learning Methods

Image restoration and noise reduction methods have been created to restore deteriorated images and improve their quality. These methods have garnered substantial significance in recent times, mainly due to the growing utilization of digital imaging across diverse domains, including but not limited to medical imaging, surveillance, satellite imaging, and numerous others.&#x0D; In this paper, we conduct a comparative analysis of three distinct approaches to image restoration: the spatial method, the frequency domain method, and the deep learning method. The study was conducted on a dataset of 10,000 images, and the performance of each method was evaluated using the accuracy and loss metrics. The results show that the deep learning method outperformed the other two methods, achieving a validation accuracy of 72.68% after 10 epochs. The spatial method had the lowest accuracy of the three, achieving a validation accuracy of 69.98% after 10 epochs. The FFT frequency domain method had a validation accuracy of 52.87% after 10 epochs, significantly lower than the other two methods. The study demonstrates that deep learning is a promising approach for image classification tasks and outperforms traditional methods such as spatial and frequency domain techniques.
Hast Mudra: Hand Sign Gesture Recognition Using LSTM

Even though using the most natural way of communication is sign language, deaf and mute people find it challenging to socialize. A language barrier is erected between regular people and D&amp;M individuals due to the structure of sign language, which is distinct from text. They converse by using vision-based communication as a result. The gestures can be easily understood by others if there is a standard interface that transforms sign language to visible text. As a result, R&amp;D has been done on a vision-based interface system that will allow D&amp;M persons to communicate without understanding one another's languages. In this project, first gathered and acquired data and created a dataset, after which extracting useful data from the images. Keywords After verification and trained data and model using the (LSTM) algorithm, TensorFlow, and Keras technology, classified the gestures according to alphabet. Using our own dataset, this system achieved an accuracy of around 86.75% in an experimental test. system uses the (LSTM) algorithm to process images and data.
A Hybrid Resampling Approach for Multiclass Skewed Datasets and Experimental Analysis with Diverse Classifier Models

In real-life scenarios, imbalanced datasets pose a prevalent challenge for classification tasks, where certain classes are heavily underrepresented compared to others. To combat this issue, this article introduces DOSAKU, a novel hybrid resampling technique that combines the strengths of DOSMOTE and AKCUS algorithms. By integrating both oversampling and undersampling methods, DOSAKU significantly reduces the imbalance ratio of datasets, enhancing the performance of classifiers. The proposed approach is evaluated on multiple models employing different classifiers, and the results demonstrate its superiority over existing resampling measures, making it an effective solution for handling class imbalance challenges. DOSAKU's promising performance is a substantial contribution to the field of imbalanced data classification, as it offers a robust and innovative solution for improving predictive model accuracy and fairness in real-world applications where imbalanced datasets are common.
A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique

This To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization.
DAR Model: A Novel Symmetric Key Enabled Security architecture for reliable data transfer in Wireless Sensor Networks

Security is an indispensable aspect in every transaction happening in the network transmissions. Wireless Sensor Networks are pretty vulnerable to the security attacks. Hence a highly efficient architectural model is very much essential in designing the sensor networks. Cryptographic algorithms play a vital role in providing encryption and decryption to the data being transmitted consequently with which security is offered in an elegant manner. In this paper, a reliable design comprising three pioneering algorithms enabled with symmetric key is architected for secure communication in wireless sensor networks from a node to the base station. The design involves two phases. In the former phase two algorithms which are effective in all perspectives are used for data transmission from node to cluster head and in the latter phase another proficient algorithm is used for communication between cluster head to base station. The three algorithms used are Data Encryption Standard (DES), Advanced Encryption Standard (AES) and RC4. Both block and stream cipher algorithms are used to fine tune the performance; and in addition, the data has been compressed with unprecedented techniques to reduce the burden on encryption. This led to an amazing performance in terms of security parameters.
An Analysis of Malicious URL Detection Using Deep Learning

Considerable progress has been achieved in the digital domain, particularly in the online realm where a multitude of activities are being conducted. Cyberattacks, particularly malicious URLs, have emerged as a serious security risk, deceiving users into compromising their systems and resulting in annual losses of billions of dollars. Website security is essential. It is critical to quickly identify dangerous or bad URLs. Blacklists and shallow learning are two techniques that are being investigated in response to the threat posed by malicious URLs and phishing efforts. Historically, blacklists have been used to accomplish this. Techniques based on blacklists have limitations because they can't detect malicious URLs that have newly generated. In order to overcome these challenges, recent research has focused on applying machine learning and deep learning techniques. By automatically discovering complex patterns and representations from unstructured data, deep learning has become a potent tool for recognizing and reducing these risks. The goal of this paper is to present a thorough analysis and structural comprehension of Deep Learning based malware detection systems. The literature review that covers different facets of this subject, like feature representation and algorithm design, is found and examined. Moreover, a precise explanation of the role of deep learning in detecting dangerous URLs is provided.
Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.
Investigation of Image Enhancement Techniques for Advancing Colon Cancer Diagnosis

Colorectal cancer continues to pose a substantial worldwide health challenge, necessitating the development of advanced imaging techniques for early and accurate diagnosis. In this study, we propose a novel hybrid image enhancement approach that combines Total Variation (TV) regularization and shift-invariant filtering to improve the visibility and diagnostic quality of colon cancer images.&#x0D; The Total Variation regularization technique is employed to effectively reduce noise and enhance the edges in the input images, thereby preserving important structural details. Simultaneously, shift-invariant filtering is utilized to address spatial variations and artifacts that often arise in medical images, ensuring consistent and reliable enhancements across the entire image. Our hybrid approach synergistically integrates the strengths of both TV regularization and shift-invariant filtering, resulting in enhanced colon cancer images that offer improved contrast, reduced noise, and enhanced fine structures. This improved image quality aids medical professionals in better identifying and characterizing cancerous lesions, ultimately leading to more accurate and timely diagnoses.&#x0D; To evaluate the effectiveness of the proposed approach, we conducted extensive experimentations on a diverse dataset of colon cancer images. Quantitative and qualitative assessments demonstrate that our hybrid approach outperforms existing enhancement methods, leading to superior image quality and diagnostic accuracy. In conclusion, the hybrid image enhancement approach presented in this study offers a promising solution for enhancing colon cancer images, contributing to the early detection and effective management of this life-threatening disease. These advancements hold significant potential for improving patient outcomes and reducing the burden of colon cancer on healthcare systems worldwide.
Integration of MFCC Extraction and LSTM Algorithm on PYNQ-Z2 for Enhanced Audio Analysis

The need for Speech Emotion Recognition (SER) is growing since researchers have found it difficult to interpret human emotions from speech data. SER is very interesting yet very challenging task of human-computer interaction (HCI). The SER application can be benefitted depending on the type of feature extraction technique and model used for classification. Deep Learning has made a great impact in the field of audio, image, video, EEG and ECG classification. The speech signal characteristics and classification model affect how well the SER application performs. The paper briefs about deploying Deep Learning Algorithm on FPGA based board i.e., PYNQ-Z2. MFCC feature extraction technique and LSTM model used for classification of human emotion is implemented on the board. Emotion can be predicted using led buttons on the board.
Data Mining Technique for Breast Cancer Prediction using Fuzzy Theory

In order to find a reliable approach of breast cancer prediction, Data mining methods are used in the studies provided in this article. This study compares multiple patient clinical data in order to find a reliable model that can predict the occurrence of breast cancer. In this article, the support vector machine (SVM), artificial neural network (ANN), naive bayes classifier, and AdaBoost tree are used as four data mining methods. Furthermore, since it has such a significant impact on the efficacy and efficiency of the learning process, feature space is extensively examined in this work. Combining PCA with other data mining algorithms that use a PCA-like technique to compress the feature space is recommended. This hybrid is intended to assess the effect of feature space reduction. Wisconsin Breast Cancer Database (1991) and Wisconsin Diagnostic Breast Cancer (1995) are two frequently used test data sets that are used to assess the effectiveness of these algorithms. To calculate each model's test error, the method of 10-fold cross-validation is used. The findings of this research show a thorough trade-off between these tactics and also provide a thorough assessment of the models. In practical applications, it is anticipated that feature identification results would help to avoid breast cancer for both doctors and patients.
FLBP: A Federated Learning-enabled and Blockchain-supported Privacy-Preserving of Electronic Patient Records for the Internet of Medical Things

The evolution of the computing paradigms and the Internet of Medical Things (IoMT) have transfigured the healthcare sector with an alarming rise of privacy issues in healthcare records. The rapid growth of medical data leads to privacy and security concerns to protect the confidentiality and integrity of the data in the feature-loaded infrastructure and applications. Moreover, the sharing of medical records of a patient among hospitals rises security and interoperability issues. This article, therefore, proposes a Federated Learning-and-Blockchain-enabled framework to protect electronic medical records from unauthorized access using a deep learning technique called Artificial Neural Network (ANN) for a collaborative IoMT-Fog-Cloud environment. ANN is used to identify insiders and intruders. An Elliptical Curve Digital Signature (ECDS) algorithm is adopted to devise a secured Blockchain-based validation method. To process the anti-malicious propagation method, a Blockchain-based Health Record Sharing (BHRS) is implemented. In addition, an FL approach is integrated into Blockchain for scalable applications to form a global model without the need of sharing and storing the raw data in the Cloud. The proposed model is evident from the simulations that it improves the operational cost and communication (latency) overhead with a percentage of 85.2% and 62.76%, respectively. The results showcase the utility and efficacy of the proposed model.
An AHP-Based Framework for Effective Requirement Management in Agile Software Development (ASD)

In Agile changes may be made at any time throughout the project lifetime in agile projects. These changes, however, often lead to longer turnaround times and higher costs while having a substantial influence on the activities and quality of project management. The suggested framework's main goal is to increase the effectiveness and flexibility of the requirement engineering process by successfully managing requirements changes, particularly in contexts where Agile Software Development (ASD) is practiced. The Agile methodology has gained popularity as a strategy for developing software because it can adjust to changing requirements and deliver software gradually. To make sure that the software being produced satisfies stakeholder expectations and adds value to the firm, good requirement management is essential. Using the Analytic Hierarchy Process (AHP) to prioritize requirements based on their relative relevance and urgency, this article introduces a framework for requirement management in Agile Projects. The most important requirements are taken care of first thanks to this strategy, which enables a more organized and informed decision-making process. We demonstrate the actual use of our framework in real-world contexts and highlight its efficacy in solving the issues faced by Agile projects by including a case study and an accompanying table. The suggested framework also supports the three core tenets of the Agile approach—transparency, cooperation, and continuous improvement—to foster an environment of excellence and ongoing learning within the Agile team. By developing these fundamental ideas, our framework not only supports Agile teams' continual growth and development but also helps them manage and prioritize requirements more efficiently, which ultimately improves project results and increases organizational value.
Text Detection Using Transformation Scaling Extension Algorithm in Natural Scene Images

In recent study efforts, the importance of text identification and recognition in images of natural scenes has been stressed more and more. Natural scene text contains an enormous amount of useful semantic data that can be applied in a variety of vision-related applications. The detection of shape-robust text confronts two major challenges: 1. A large number of traditional quadrangular bounding box-based detectors failed to identify text with irregular forms, making it difficult to include such text within perfect rectangles.2. Pixel-wise segmentation-based detectors sometimes struggle to identify closely positioned text examples from one another. Understanding the surroundings and extracting information from images of natural scenes depends heavily on the ability to detect and recognise text. Scene text can be aligned in a variety of ways, including vertical, curved, random, and horizontal alignments. This paper has created a novel method, the Transformation Scaling Extention Algorithm (TSEA), for text detection using a mask-scoring R-ConvNN (Region Convolutional Neural Network). This method works exceptionally well at accurately identifying text that is curved and text that has multiple orientations inside real-world input images. This study incorporates a mask-scoring R-ConvNN network framework to enhance the model's ability to score masks correctly for the observed occurrences. By providing more weight to accurate mask predictions, our scoring system eliminates inconsistencies between mask quality and score and enhances the effectiveness of instance segmentation. This paper also incorporates a Pyramid-based Text Proposal Network (PBTPN) and a Transformation Component Network (TCN) to enhance the feature extraction capabilities of the mask-scoring R-ConvNN for text identification and segmentation with the TSEA. Studies show that Pyramid Networks are especially effective in reducing false alarms caused by images with backgrounds that mimic text. On benchmark datasets ICDAR 2015, SCUT-CTW1500 containing multi-oriented and curved text, this method outperforms existing methods by conducting extensive testing across several scales and utilizing a single model. This study expands the field of vision-oriented applications by highlighting the growing significance of effectively locating and detecting text in natural situations.
Healthcare Record Sharing and Management using Blockchain

We experience various health problems due to the hectic pace of life, which includes daily hustle and work life, and the deteriorating ecosystem. There are numerous health records produced and every individual has to maintain these many important pieces of private information. Health records are extremely sensitive information that must typically be shared with peers for numerous examinations in the medical field. Healthcare records are managed manually in the current scenario, which raises efficiency, security, and other concerns. This paper puts forward a web application that uses blockchain technology to offer a simple method for securely sharing private medical records. It offers real-time tracking of medical records, enhancing security, boosting productivity, and lowering total costs. By enabling infrequent knowledge transfer between healthcare professionals, insurance agencies, pharmaceuticals, scientists, and caregivers of the patient, it will streamline data exchange, enhance decision-making for healthcare, and ensure availability and genuineness. Doctors could use the application to review the healthcare data of the patients by whom they have been granted access, while individuals could use it to upload their data, grant access to the doctors, and read their own records.
KBD-Share: Key Aggregation, Blockchain, and Differential Privacy based Secured Data Sharing for Multi-User Cloud Computing

In today's era of widespread cloud computing and data sharing, the demand for secure and privacy-preserving techniques to facilitate multi-user data sharing is rapidly increasing. However, traditional approaches struggle to effectively address the twin objectives of ensuring privacy protection while preserving the utility of shared data. This predicament holds immense significance due to the pivotal role data sharing plays in diverse domains and applications. However, it also brings about significant privacy vulnerabilities. Consequently, innovative approaches are imperative to achieve a harmonious equilibrium between the utility of shared data and the protection of privacy in scenarios involving multiple users. This paper presents KBD-Share, an innovative framework that addresses the intricacies of ensuring data security and privacy in the context of sharing data among multiple users in cloud computing environments. By seamlessly integrating key aggregation, blockchain technology, and differential privacy techniques, KBD-Share offers an efficient and robust solution to protect sensitive data while facilitating seamless sharing and utilization. Extensive experimental evaluations convincingly establish the superiority of KBD-Share in aspects of data privacy preservation and utility, outperforming existing approaches. This approach achieves the highest R2 value of 0.9969 exhibiting best data utility, essential for multi-user data sharing in diverse cloud computing applications.