Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Hand-written Digit Classification Using Deep Learning

Humans' control over technology is at an all-time high, with applications ranging from visual object recognition to the dubbing of dialogue into silent films. Using algorithms for deep learning and machine learning. Similarly, the most crucial technologies are text line recognition fields of study and development, with an increasing number of potential outcomes. Handwriting recognition (HWR), also identified as Handwriting Text Acknowledgment, is the capacity of a computer to understand legibly handwritten input from bases such as paper documents, screens, and other devices. Evidently, we have performed handwritten digit recognition using MNIST datasets and SVM, Multi-Layer Perceptron (MLP), and CNN models in this research. Our primary purpose is to compare the accuracy and execution times of the aforementioned models to determine the optimal model for digit recognition.
Digital Transformation and Smart Property Contracts Registration in Egypt Using Blockchain Technology

In recent times, an increasing fascination with cryptographic-based blockchain technology has emerged. Blockchain is a decentralized, distributed database functioning as a comprehensive ledger for storing, timestamping, and encrypting data. Comprising interconnected blocks, this technology is openly accessible and immutable. Smart Property Contract registration are self-executing contracts that are subject to the terms of the agreement between the seller and the buyer. The contract defines the pre-programmed contractual rules so that they are executed when the input data meets the mentioned conditions between the two parties. Smart Property Contract Registration are written in one of the programming languages such as AssemblyScript, JavaScript, and Solidity. Blockchain technology and Smart Property Contract Registration can play a vital role in Property and smart cities in the future. The core aim of this thesis is to design a proposed model for registering Smart Property Contract Registration in Egyptian Egyptian Property Registry Office and solve the traditional registration problem of slow registration and high cost and insecure. The proposed model was proposed and implemented on the NEAR platform. Egyptian Property registration system was built using programming code for Smart Property Contract Registration written in a high-level language such as AssemblyScript and HTML, JS, and React Js programming code, and also present research uses a questionnaire study to ensure the validity of the proposed model. We conducted a questionnaire by asking a set of questions to those interested in the subject of blockchain and Smart Property Contract Registrations. The result showed that the adoption of blockchain technology in Egypt will be beneficial in terms of easy access to Property transactions in a quick time, reliability, and cost reduction.
Novel approach to integrate various feature extraction techniques for the Visual Question Answering System with skeletal images in the healthcare sector

In the realm of medical science, one of the most challenging concepts to grasp is the Medical Imaging Query Response System. The comprehension and classification of the diverse representations of the human body require a significant degree of effort and expertise. Furthermore, it is imperative for users within the healthcare sector to rigorously validate the system. In the domain of human health, a plethora of imaging techniques, including MRI, CT, ultrasound, X-ray, PET-CT, and others, play a pivotal role in the identification of medical issues. These technologies are instrumental in supporting both patient engagement and clinical decision-making. However, the utilization of models, techniques, and datasets for processing textual and visual information introduces complexities that can at times impede the provision of pertinent clinical solutions. The overarching objective of the proposed approach is to conduct a comprehensive comparative analysis of various feature extraction methodologies for both visual and textual information within the Visual Question Answering (VQA) system, focusing on human skeletal images. This endeavor is aimed at enhancing the VQA system's performance with newer datasets and addressing any limitations inherent in existing models. In addition, this research initiative seeks to enable researchers to identify and optimize novel methods that enhance the accuracy of the VQA system. The models under scrutiny in this analysis encompass various methods of feature extraction that help to improve the model and quality of the healthcare industry. The researcher will find the proper methodology for different datasets. To gauge the efficacy of each model in delivering the desired outcomes, an array of metrics will be employed, including classification measurement accuracy, F-classification, C-true positive rate (CTPR), C-precision, C-recall, C-sensitivity, and C-false negative rate (FNR). These metrics are designed to enhance the accuracy of any dataset and optimize the performance of both visual and textual components to ensure accurate responses to the posed queries.
Secure Framework for Cyber Data Using Cryptographic and Steganographic Algorithms

The e-commerce industry has recently seen enormous growth on a global scale. Due to the rising popularity of online shopping, consumers, businesses, and depository financial institutions are extremely worried about debit/credit card fraud and the protection of personal information. It is essential to prevent unwanted access to and use of the information since it is disseminated over insecure channels. Cryptography and steganography are most frequently employed to avoid unauthorized access to sensitive data. However, the combined qualities of these two approaches are not secure enough in the modern world. It might lead to some vulnerability. It is possible to add additional layers of protection and achieve high levels of information security if visual cryptography is used in conjunction with the abovementioned combination. This research work proposed a multi-level information security framework for cyber data using random public key cryptography for the secret text, color image steganography for concealing secret encrypted text in the cover image, visual cryptography for slicing the cover image into two shares, and image steganography again to hide both the shares into two color images, respectively. These security processes significantly increase the confidentiality, dependability, and efficiency of secret messages. While there isn't a parameter to demonstrate the level of security achieved using cutting-edge techniques, the accuracy of the received text data is calculated in terms of MSE and correlation coefficient by comparing the sent and received text data. To evaluate the effectiveness of the suggested strategy, the time required at the transmitter and receiver ends is also calculated. The MATLAB environment is utilized in the implementation, demonstrating that the suggested system has improved robustness when considering steganalysis.
Use of Cryptocurrencies and Intelligent Systems in Agriculture

The digital transformation is remarkably advanced due to the decentralized way of digital transactions using cryptography. The term coined is “Cryptocurrency” and has triggered the world in the fastest pace. The intelligent systems promptly complement it. The use of cryptocurrency is majorly seen in NFT (Non Fungible Token) domains and universally addressed through the digital wallets and websites. Like every other domain, agriculture and agricultural products can also be seen as a major sector to be acquired by the new age technology of cryptocurrencies. The field thus is accompanied by intelligent systems such as the use of artificial intelligence(AI) , Internet of things(IoT) in computer systems. The agricultural domain and its technological progress can be witnessed through the adoption of precision agriculture in various countries. This research paper focuses on the intelligent perspective of the agricultural field and also dives through the sustainability achievement by the same. The paper concludes with the discussion on sustainable agriculture as a new paradigm.
Comparative Analysis of Privacy Preservation Mechanism: Assessing Trustworthy Cloud Services with a Hybrid Framework and Swarm Intelligence

Cloud computing has emerged as a prominent field in modern computational technology, offering diverse services and resources. However, it has also raised pressing concerns regarding data privacy and the trustworthiness of cloud service providers. Previous works have grappled with these challenges, but many have fallen short in providing comprehensive solutions. In this context, this research proposes a novel framework designed to address the issues of maintaining data privacy and fostering trust in cloud computing services. The primary objective of this work is to develop a robust and integrated solution that safeguards sensitive data and enhances trust in cloud service providers. The proposed architecture encompasses a series of key components, including data collection and preprocessing with k-anonymity, trust generation using the Firefly Algorithm, Ant Colony Optimization for task scheduling and resource allocation, hybrid framework integration, and privacy-preserving computation. The scientific contribution of this work lies in the integration of multiple optimization techniques, such as the Firefly Algorithm and Ant Colony Optimization, to select reliable cloud service providers while considering trust factors and task/resource allocation. Furthermore, the proposed framework ensures data privacy through k-anonymity compliance, dynamic resource allocation, and privacy-preserving computation techniques such as differential privacy and homomorphic encryption. The outcomes of this research provide a comprehensive solution to the complex challenges of data privacy and trust in cloud computing services. By combining these techniques into a hybrid framework, this work contributes to the advancement of secure and effective cloud-based operations, offering a substantial step forward in addressing the critical issues faced by organizations and individuals in an increasingly interconnected digital landscape.
Hybrid Spam Filtering using Monarch Butterfly Optimization Algorithm with Self-Adaptive Population

Spam causes bottlenecks and congestion, reducing the speed, processing power, available memory, and bandwidth. Existing spam email classification methods need to be more accurate because of the large dimensionality of hybrid spam datasets. This makes the need for a feature dimensionality reduction technique that uses only associated features of the problem instead of all features in the dataset. This paper presents a feature selection based on the monarch butterfly optimization (MBO) algorithm that emphasizes less complexity and few features. This method is efficient and produces a more accurate classification. To improve further standard MBO algorithm performance, we introduce the population size in both subpopulations 1 and 2 will experience dynamic variations as the algorithm proceeds along its linear way. As the idea of a self-adaptive and greedy strategy is modified, the self-adaptive population monarch butterfly optimization (SPMBO) method is introduced, and only newly generated SPMBO individuals are eligible for the next generations if they are better individuals earlier before. Later, this paper proposes an email classification system based on k-nearest neighbors (k-NN) based on two distance metrics, explicitly Euclidean, and Manhattan, that also uses the SPMBO technique. This method seeks to determine whether a hybrid email is a spam. The efficiency of the proposed SPMBO algorithm was compared with standard MBO based on three datasets Dredze, Image spam hunter, and Spambase. Thus, the use of SPMBO results has shown superior as related to other authors' works in relevant fields.
Construction of Communication Protocol Using Ring-LWE-Based Homomorphic Encryption in Iot-Cloud Environment

The rapid development of wireless communication and sensor networks is the basis for forming an Internet of things(IoT) infrastructure. In IoT-based applications, the cryptographic encryption and access control at cloud must be robust to withstand current attacks. The majority of security protocols are based on integer factorization and discrete logarithm problems, which are proved vulnerable to quantum attacks. In this paper, we proposed a scheme for the security and privacy of the user data in a cloud environment. Various types of homomorphic encryption schemes are studied for data privacy in the cloud. The Ring-LWE-based encryption scheme is presented for privacy protection in the cloud which meets the homomorphic properties. The scheme is analysed for security, privacy, reduced messaging overhead and computation overhead. The objective of this paper is to Design and Construct a Ring-LWE-based homomorphic encryption(HE) communication protocol for authenticated user message encryption in a IoT cloud computing environment. The evaluation function in holomorphic encryption defined&#x0D; based on Ring-LWE encryption for a practical sharing-enabled cloud storage. Then, formally proving the security of the proposed protocol for classical and quantum attacks in cloud environment like Manin-the-middle (MITM) attack, Denial of Service (DoS) and Replay Attack.
ALGORITMA K-NEAREST NEIGHBOR (K-NN) DAN SINGLE LAYER PERCEPTRON (SLP) UNTUK KLASIFIKASI PENYAKIT ALZHEIMER

Alzheimer's disease is a brain disorder that causes memory loss, decreased thinking skills, communication difficulties, and behavioral changes. Early detection of this disease is very important for proper treatment and planning of medical needs. However, there is currently no drug that can cure Alzheimer's. Therefore, this study aims to develop accurate early predictions for Alzheimer's disease by comparing two algorithms: K-Nearest Neighbor (KNN) and Single Layer Perceptron (SLP) using the percentage split method. The results showed that testing using the K-NN algorithm resulted in an accuracy of 96%. The precision and recall values for class 0 (nondemented) are 93% and 100%, respectively, while for class 1 (demented) are 100% and 91%. On the other hand, testing using the SLP algorithm produces an accuracy of 99%. The precision and recall values for class 0 (nondemented) are 97% and 100% respectively, while for class 1 (demented) are 100% and 98%. Based on a comparison of the values for accuracy, precision, and recall, as well as the performance of the two classification methods, it can be concluded that the implementation of the Single Layer Perceptron algorithm provides the best prediction for early detection of Alzheimer's disease. These findings provide potential use of this algorithm in facilitating early diagnosis and timely intervention for patients with Alzheimer's.
Exploring Sentiment Analysis in Social Media: A Natural Language Processing Case Study

Social media plays an integral role in our daily lives, influencing and reflecting global perspectives through the consumption and creation of content. Platforms like YouTube are incredibly active, with a constant influx of video uploads, views, and comments. While the YouTube app allows us to browse videos and comments, it offers only a limited glimpse into the interests and trends of others. Analysing this vast data pool, encompassing diverse language styles, presents a significant challenge. This article delves into the YouTube Data API and its application in Python for accessing raw data. The process involves data cleaning using advanced Natural Language Processing (NLP) techniques, harnessing Python-based machine learning to explore social media interactions, and automating the extraction of trends and influential factors. The journey towards trend analysis is meticulously detailed, featuring examples that leverage a variety of open-source Python tools.
A Study on Techniques and Challenges in Sign Language Translation

Sign Language Translation (SLT) plays a pivotal role in enabling effective communication for the Deaf and Hard of Hearing (DHH) community. This review delves into the state-of-the-art techniques and methodologies in SLT, focusing on its significance, challenges, and recent advancements. The review provides a comprehensive analysis of various SLT approaches, ranging from rule-based systems to deep learning models, highlighting their strengths and limitations. Datasets specifically tailored for SLT research are explored, shedding light on the diversity and complexity of Sign Languages across the globe. The review also addresses critical issues in SLT, such as the expressiveness of generated signs, facial expressions, and non-manual signals. Furthermore, it discusses the integration of SLT into assistive technologies and educational tools, emphasizing the transformative potential in enhancing accessibility and inclusivity. Finally, the review outlines future directions, including the incorporation of multimodal inputs and the imperative need for co-creation with the Deaf community, paving the way for more accurate, expressive, and culturally sensitive Sign Language Generation systems.
Penguatan Kompetensi Literasi dan Numerasi Guru Sekolah Dasar di Kabupaten Pandeglang dan Kota Serang Banten

&lt;p&gt;This paper aims to describe the implementation of strengthening literacy and&lt;br /&gt;numeracy competencies in the city of Serang and Pandeglang district. The&lt;br /&gt;subjects of dedication, namely SDN Banjarwangi and SDN Purut were chosen&lt;br /&gt;as partners because they have the potential for the majority of teachers to&lt;br /&gt;have high motivation and awareness to develop their potential and&lt;br /&gt;professionalism. The method of implementing community service is carried&lt;br /&gt;out through four stages, including 1) Preparation, which is carried out to&lt;br /&gt;prepare for the implementation of community service. 2) Implementation, this&lt;br /&gt;stage is carried out through training/workshop activities for teachers in&lt;br /&gt;selected elementary schools. 3) Assistance, this stage is carried out by forming&lt;br /&gt;a group team and assisting the service participants. and 4) Reporting, this stage&lt;br /&gt;is carried out by the team by compiling a report on the implementation of the&lt;br /&gt;service. The results of this community service activity include&lt;br /&gt;training/workshop activities and mentoring that can provide knowledge and&lt;br /&gt;experience in improving literacy and numeracy professional competence as&lt;br /&gt;well as skills in developing literacy and numeracy-based learning tools suitable&lt;br /&gt;for learning for elementary school students.&lt;/p&gt;
Create a Model to Detect Audiovisual Videos by Breaking Down Superscribing Tensor and Using Less Frequency and a Lower Ranking

The aim of this study is to develop a model for audiovisual video detection by decomposing superscribing tensors and using reduced frequency and lower rank. This model will be used for identifying videos that have audio with low frequencies and visual frames with low rankings. The proposed model would use a convolutional neural network (CNN) and a recurrent neural network (RNN) to detect and classify the audiovisual characteristics. The Convolutional Neural Network (CNN) will be used to record the video frames with high frequency, while the Recurrent Neural Network (RNN) will be utilised to capture the audio characteristics with low frequency. The training process will use an extensive dataset of audiovisual videos. The performance of the model will be assessed by testing it using a validation dataset. Ultimately, the model will be used in a live setting to identify audiovisual recordings with low occurrence rates.
Spatial Data Analysis Utilizing Grid Dbscan Algorithm in Clustering Techniques for Partial Object Classification Issues

Clustering algorithms to solve problems with partial object categorization in spatial data analysis is the topic of this research, which explores the usefulness of these techniques. In order to do this, the Grid-DBSCAN method is offered as an effective clustering tool for the purpose of resolving issues involving partial object categorization. A grid-based technique is included into the Grid-DBSCAN algorithm, which is derived from the DBSCAN algorithm and is designed to increase its overall performance. A number of datasets taken from the real world are used to evaluate the method, and it is then compared to existing clustering techniques. The findings of the experiments indicate that the Grid-DBSCAN method is superior to the other clustering algorithms in terms of accuracy and resilience, and that it is able to locate the most effective solution for jobs involving partial object categorization. It is also possible to enhance the Grid-DBSCAN technique so that it can handle different kinds of complicated datasets. The purpose of this study is to offer an understanding of the efficiency of the suggested method and its potential to perform partial object categorization problems in spatial data analysis.
AI Adoption in the Chinese Food and Beverage Industry: An Exploratory Study

&lt;em&gt;This study aims to investigate the phenomena of how artificial intelligence (AI) as one of the cutting-edge technologies benefits restaurant services and what the obstacles to implementing AI in a restaurant are. Due to the rapid pace of life, people tend to have less and less eating time, especially white-collar workers. More and more restaurants implement AI technology to improve their cooking efficiency and reduce service time, such as fast payment systems (QR and facial recognition payment), AI-enabled bots, AI-powered self-ordering kiosks, and robot chefs. Since the COVID-19 pandemic begins in early 2020, food safety and sanitation become increasingly important when people eat outside. Chef and waiter robots are good options for avoiding intimate contact. This study employed qualitative research with in-depth interviews. We interviewed three restaurant managers in China. The findings suggest that adopting AI technology in restaurant services can minimize high costs, better manage customer relationships, and provide more convenient in-store services. This study contributes to the managerial gap in AI restaurants. &lt;/em&gt;
“Dynamic Shield: An Algorithm for Secured Data Transmission in Dynamic WBAN Scenarios”

Healthcare monitoring, sports performance monitoring, industrial wearable sensors, smart retail clothes, and emergency response wearable are all used to evaluate the suggested algorithms. The results show how crucial it is to tailor your choice of cryptographic algorithms to your specific use case and available resources. Data privacy and integrity can be maintained in ever-changing network topologies with the help of the study's suggested strategies for achieving this goal. Building trust and confidence in the dependability of WBAN systems, the suggested algorithms provide powerful security against data breaches, unlawful access, and data loss. With the growing popularity of WBANs in crucial settings, this research provides a solid groundwork for safe and reliable data transmission, opening the door to the easy incorporation of wearable technology in a wide range of uses.