Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Image Compression for Efficient Wireless Communication in IOT

This abstract emphasizes the transformative potential of deep learning-based image compression techniques as a solution to these issues and a way of allowing efficient wireless communication within IoT networks. The exploratory outcomes show that the recreated calculation outflanks the Key Part Investigation (PCA), Discrete Cosine Change, and Quick Fourier Change (FFT) on factor pressure proportions while utilizing the Terrific St-Bernard metrological dataset. The mimicked calculation has a lower root mean square blunder (RMSE) and a higher coefficient of assurance (R 2) esteem. Also, while utilizing the LUCE metrological dataset, it showed lower RMSE and higher-pressure proportion values on factor blunder limits contrasted with the Lightweight Worldly Pressure (LTC) procedure. It was consequently shown that the mimicked approach gives preferable pressure devotion over the customary procedures without a mistake bound system.
A Semi-Supervised Quantitative Inference Model for Accurate Breast Cancer Detection

Breast cancer poses a significant threat to women's lives, necessitating the development of effective detection and treatment methods. Accurately classifying tissue samples as benign or malignant is crucial for appropriate cancer diagnosis and treatment planning. However, utilizing data mining algorithms for this task is challenging. This study proposes a semi-supervised approach to develop a quantitative inference model for breast cancer tissues. The methodology involves the initial clustering of the dataset using the farthest-first clustering method, followed by the classification of clustered records. The performance of the proposed model is evaluated using a confusion matrix, utilizing the Wisconsin Original breast cancer dataset from the UCI Repository. Our results demonstrate that the proposed method achieves the highest accuracy, with a random forest accuracy of 98% and decision tree accuracy of 95.2%. These findings highlight the potential of our approach in improving breast cancer tissue classification accuracy, contributing to more effective diagnosis and treatment strategies.
Construction of Ontology Graphs in a Cyber Security Framework (OCSF)

The paper develops ontology for cyber security to provide information from huge collection of structured and unstructured web data. It deals with identification of several aspects related to cyber security including vulnerability, attacks identification, virus, snort alerts and many more. Therefore, it is mandatory to develop cyber security knowledge graph by incorporating the use of ontologies and reasoning rules.
An Ambitious Approach to Smart Internet Voting System

The “Online Voting System” is a platform where citizens can vote. It can be done electronically, through a website, mobile app, or anything connected to an internet device. The physical voting system is very inconvenient as it involves many issues such as physically visiting the polling station. Therefore, an “Online Voting System” overcomes these issues and will help in providing timely, convenient, and fast voting services. Also, this system will generate a more precise outcome. This paper proposed a voting system named as an ambitious approach to smart internet voting system, which is fast, easy to use, and provides high-end security by using the proper authentication method.
Enhancing Data Compression: A Dynamic Programming Approach with Huffman Coding and Burrows-Wheeler Transform

Data compression is a vital method for reducing the size of digital data to save space and make transmitting it quicker. More advanced compression techniques, such as Huffman coding and Burrows-Wheeler Transform (BWT) algorithms, have been developed to achieve highly efficient compression. This study introduces a new data compression method that combines both Huffman coding and Burrows-Wheeler Transform (BWT) to compress images and demonstrates that this combined approach produces better results than using them separately. The BWT rearranges the order of characters in data to make it more suitable for compression, while Huffman coding assigns shorter codes to frequently occurring characters to make compression efficient. The code reads image data in portions, applies these transformations, and calculates compression statistics to show how effectively the compression is performed. Moreover, dynamic programming techniques are utilized to optimize the algorithm and enhance its efficiency. The study presents experimental findings that evaluate the performance of the proposed algorithm across various image types. The outcomes reveal that the proposed algorithm achieves higher compression ratios compared to conventional methods. Furthermore, the algorithm is efficient in both encoding and decoding processes, rendering it suitable for real-world applications. In conclusion, the proposed data compression algorithm that combines Huffman coding and the BWT algorithm is a successful strategy for compressing digital images. The integration of dynamic programming techniques has significantly elevated the algorithm's efficiency. This algorithm holds potential in diverse applications, including storing and transmitting large images.
Enhancing Guest Experience and Operations Efficiency in Homestays Through Artificial Intelligence: A Conceptual Framework

Artificial intelligence (AI) in homestays could improve the tourist experience and operational efficiency. This article proposes a comprehensive conceptual framework for AI in homestays, encompassing visitor experience enhancement, personalized suggestions, predictive analytics, inventory management, maintenance scheduling, and data analytics. AI algorithms allow homestay owners to make personalized recommendations for activities, local sights, and meals. AI-powered home automation systems let guests customize lighting, temperature, and entertainment. AI-powered chatbots and virtual assistants help visitors with questions and requests. Predictive analytics models help operators optimize pricing, estimate demand, and allocate resources, improving revenue management and operational efficiency. AI-based inventory management systems optimize inventory levels, reduce waste, and ensure supplies. Proactive maintenance using AI lowers downtime and enhances the visitor experience. AI-enabled data analytics systems measure occupancy rates, revenue, and visitor satisfaction in real time for informed decision-making and ongoing development. AI in homestays must handle ethical issues including openness, fairness, and accountability, as well as privacy and data security. Cost, technological knowledge, and user approval are other challenges. This article concludes that AI technology in homestays can improve tourist experiences and operational efficiency. Future research should address ethical, privacy, and implementation issues to fully fulfill AI's transformational potential in homestays.
Enhancing Face Forgery Detection in a Decentralized Landscape: A Federated Learning Approach with ResNet

Recent advancements in technologies could be the reason for fake image and video generation over the internet. This may be the cause of fake identity creation over the internet for forgery. These types of acts may be the reason for security issues in society. The legacy fake forgery method is not that capable of recognizing such forgery as the methods are trained with publicly available centralized datasets and never focus on privacy and security issues and adversely influence the forgery detection. Hence, the objective of this research is to provide decentralized ways to handle these issues effectively. The issue is taken care of with an effective federated learning-based deep learning technique. In the proposal, several deep learning models were used to generate a residual feature map from the available image dataset and later federated learning was used to generate a decentralized infrastructure for collaborative client machines. The complete experiment setup is established with a publicly available dataset and various variable parameters are used such as the number of clients and round of communication. Afterward, deep learning models’ performance is compared for forgery detection under federated learning environments, and it has been observed that ResNet deep learning outperforms with an accuracy rate of 87.83% in FaceForensic dataset.
Multi-Target Multi-Camera Tracking with Spatial-Temporal Network

Multi-Target Multi-Camera Tracking (MTMCT) aims to link objects of interest between frames and different camera views, which has many application potentials in the fields of surveillance and autonomous driving. Existing methods usually extract motion or appearance features from single-view trajectories to perform cross-view association, but struggle with camera view variations and occlusions. To this end, this paper proposes a novel method that integrates spatial-temporal cues for more robust cross-view matching. The proposed method firstly introduces a spatial-temporal trajectory selection module to enhancing feature saliency and reducing the impact of erroneous trajectories on feature matching and then extracts discriminative video-level appearance features for each trajectory with a temporal complementary learning network. Cross-view targets are finally associated by computing the cosine distances of the trajectory features. Experimental results on the public DIVOTrack dataset validate the effectiveness of the proposed method.
Android Based Instant Messaging Application

In today's digital era, instant messaging has become an integral part of our communication landscape. With the rapid growth of mobile technology, Android-based instant message applications have gained immense popularity. This abstract presents “InstantConnect,” an Android application that offers seamless and efficient instant messaging capabilities.
Mobile Sensing Based Classification System For Human Fall Detection

The elderly are at a considerable risk of falls, which is a primary cause of hospitalization and injury. Statistics from the CDC pointed out that one in four Americans 65 years of age or older are susceptible to falling incidents. Each year, resulting in over 800,000 hospitalizations and 27,000 deaths annually. Fall detection and rescue systems based on technology have been developed to address this issue, provide prompt emergency assistance, and reduce the incidence of injuries and associated healthcare costs. This paper systematically reviews sensor-based systems for detecting fallen individuals. This study aims to stimulate additional research in this area of study. One low-cost approach to fall detection illustrated in this paper involves using the built-in sensors of iOS phones to detect falls. A dataset and a classification system were developed to identify fall events utilizing the phone's accelerometer and gyroscope. This approach could benefit elderly individuals living alone without access to more expensive Systems designed to detect falls. Other sensor-based fall detection systems use various sensors, such as accelerometers, gyroscopes, pressure sensors, or cameras. By utilizing the Artificial Neural Network methodology, the gathered data has been evaluated, resulting in a proposed model with an impressive accuracy rate of 99%. This showcases the efficacy of the algorithm in identifying incidents of human falls.
Online Payment Fraud Detection Using Machine Learning

With the rise of web surfing and online shopping, so came the use of credit cards for online transactions, as did the prevalence of online financial fraud. Just in 2018, credit card theft cost the globe 24.26 billion USD. Many innocent individuals have lost a significant amount of money due to these scams, which have stopped them from ever engaging in online payment operations. Older folks, who are less skilled in technology, are the most frequent targets of these scams. Traditionally, To locate credit card fraud, automated learning techniques have been utilised through machine learning and deep learning approaches. However, automated learning approaches are labelled, which necessitates the acquisition of prior information. Our Hybrid technique outperformed the k-means clustering strategy in terms of accuracy, precision, and recall.
Application of Hadoop for Analyzing Data Using Mapper Files

In contemporary times, a substantial quantity of valuable and indeterminate data is readily accessible in numerous practical applications. This information might be used by many different industries and government agencies to get useful insights. This data has the potential to assist managers in improving their strategies and optimizing their decision-making processes. Several private enterprises and governments have started investing and funding initiatives to increase earnings and make the best use of resources. The analysis of large-scale datasets is crucial to the development and growth of future efforts, and the phrase “big data” is used to describe such massive amounts of data. This study presents an analysis of Big Data analytics based on empirical findings. The present study involves an analysis of New York Stock Exchange data through the utilization of two mapper files within the Hadoop framework. The annual computation of the highest and lowest values of each stock exchange, as well as the mean stock price, is performed.
Credit Card Financial Fraudster Discovery with Machine Learning Classifiers

Credit card financial fraudster activity is a critical issue in today's technological era for financial institutions and their customers. In this study, we investigate the use of multiple machine learning models such as support vector classifier, logistic regression, K-Nearest Neighbor (KNN) classifier, random forest classifier, decision tree classifier, a machine learning algorithm, to detect fraudulent credit card transactions in a dataset where only a tiny fraction of transactions is fraudulent and need to investigate the detection with at most accuracy. To address the issue of imbalanced data, we employ under-sampling of the majority class and oversampling of the minority class. Our results show that the logistic regression, random forest classifier, support vector classifier, decision tree classifier, and KNN classifier model can achieve 99% accuracy in detecting fraudulent transactions. Additionally, we perform feature importance analysis to identify the most significant variables that contribute to fraud detection. The approach used in this study can contribute to the development of more sophisticated fraud detection models for financial institutions. It also provided the pattern of fraud detection daily on the week chart. Overall, our findings have practical implications for financial institutions and researchers seeking to improve their fraud detection capabilities, which can lead to a more secure and trustworthy financial system for all stakeholders involved.
The Prospect of Virtual Technology in Education Field

The virtual technology industry is experiencing exponential growth on a global scale, particularly during and after the pandemic, due to the escalating demand for remote work, online education, digital entertainment, and other related sectors. Currently, virtual technology has made significant progress and breakthroughs across multiple fields and levels; however, it still faces certain technical difficulties and bottlenecks that need to be overcome. This paper focuses on the application of virtual technology in the field of education and its transformative impact on traditional education. From an educational perspective, this paper explores the influence of virtual technology on the current education landscape. Additionally, it analyzes the positive impact and benefits of virtual technology on the overall development of students in education. The paper further discusses and summarizes the transformative changes brought about by virtual technology in the realm of traditional education. For a comprehensive understanding, this paper meticulously categorizes and discusses various applications of virtual technology in the education sector, such as virtual reality experiences, distance learning, cross-cultural communication, art, and creation, as well as vocational and simulation training. The paper concludes with a summary and outlook.
AI-Empowered Intruder Alert System for Enhanced Security Surveillance

In today’s contemporary society, ensuring security and safety is of paramount importance. For a nation to maintain a robust economy, it must prioritize the establishment of a secure environment. Surveillance cameras are widely employed to monitor and detect illicit actions. However, the current approach often relies on human intervention and monitoring, primarily after an intrusion has occurred. To address this issue, an automated system capable of identifying illegal activities without constant human supervision is necessary. This will help local security. Current local retailers of CCTV systems in Kerala lack AI-enabled intruder alert systems. The proposed project focuses on developing a software to integrate with the current software of the CCTV companies, which provides customizable intruder detection solutions. This will help the local security companies sustain themselves against international competitors by applying machine learning algorithms that take real-time video as input and detect the presence of intruders on the premises and alerts the user/admin. A machine learning model will be built using Convolutional Neural Networks and trained using a large dataset of real life CCTV footage as inputs. The model will be trained to recognize humans with or without weapons, animals, and unidentified moving objects separately and specify the same in the alert provided and also specify familiar faces or not. Users can specify a certain alert time during which the intruder detection system will be activated. This model will be cross-platform integratable, i.e, can be integrated with a mobile/desktop/web application as per the requirement of the client. Accuracy, Precision, Recall and F-measure has been computed for the proposed model.
Optimizing Search and Sort Algorithms: Harnessing Parallel Programming for Efficient Processing of Large Datasets

This research study investigates the impact of parallel programming techniques on the performance of searching and sorting algorithms. Traditional sequential algorithms have been the foundation of data processing for decades, but the increasing availability of parallel computing resources opens new possibilities for improving efficiency and reducing execution times. By leveraging parallel programming models, such as shared memory and distributed memory systems, this study explores how the parallelization of search and sort algorithms can enhance their speed and scalability. Through comprehensive experimentation and analysis, this study aims to provide valuable insights into the benefits and challenges of parallel programming in the context of searching and sorting algorithms.
An Adaptive Path Planning Method Based on the Frenét Frame

In the realm of mobile robot path planning, this study integrates local path planning with the Frenét Frame, presenting an adaptive path planning approach. Firstly, the A* algorithm is employed to extract the shortest path from the map. Subsequently, cubic spline interpolation is applied to the path to generate a smooth global reference trajectory. Next, a cost function based on lateral offset distance is introduced to enhance the proximity of the trajectory to the reference trajectory, thereby improving efficiency. Lastly, an adaptive sampling strategy is incorporated to enhance the robot's dynamic obstacle avoidance performance. Experimental results validate the efficacy of the proposed path-planning approach.
A Blockchain Approach for Securing Certification Management System

Accountability and openness are essential elements of any technical effort. Recent technologies like blockchain encourage these traits because of the way their infrastructure is constructed and how well they function as record-keeping systems. This paper aims to use NFTs to figure out its application in the field of Digital Certifications to solve various problems which plague the current scenarios which are forgery, stealing and counterfeiting etc.
A Novel Survey on Hand Sign Recognition

Motion recognition is an essential research part of computer vision with many applications including human-computer interaction and signal interpretation. In this article, we propose a convolutional neural network (CNN) model for motion recognition using live cameras. We gathered information about hand-drawn illustrations of the English alphabet (A-Z) before removing the background and converting them to black-and-white images. Previous images were used to train the CNN model and 95% accuracy was achieved on the test data. We implemented a model in Python and combined it with a user-friendly software interface that provides a real-time prediction of hand movements using a camera system. Our system has the potential to be an essential tool in many industries and businesses, including managing electronic devices and helping people with communication disabilities. The proposed models and methods provide robust and accurate solutions for real-time operational information and can be used in a variety of situations.
Gender and Age Classification Using Caffe Network

The availability of labelled data at scale has contributed to the rapid expansion of Deep Learning. This results show that training deep-cnn learning representations can lead to huge increases in performance on specific tasks. The caffe net model surpasses existing modernization approaches for gender and age approximation when tested against the Adience benchmark. It is suggested convolutional net architecture is simple and accurate to 95%.
Dissemination of the Importance of Numbering Medical Records at the Parak Karakah Padang Health Center

Puskesmas is a health service facility that organizes community health efforts and first level individual health efforts. The medical record is a record or record of who, what, why, when and how services are provided to patients during the treatment period. The family folder is a record of health conditions. Family folder is a record of health conditions, as a result of health problems or diseases that are stored in one medical record document folder. The medical record storage system is one of the most important factors in health care institutions. Because the storage system can facilitate medical record files stored in storage racks, speed up retrieval or retrieval of stored medical record files Purpose of numbering medical records Facilitate search of medical record files or documents when patients then come back for treatment at health care facilities as well as for continuity information, by using a numbering system, the information can be sequential and minimize lost information.
Machine Learning Based Graduate Admission Prediction

For academic institutions, choosing graduate applicants for admission is an important task. A reliable system for estimating the likelihood of a candidate being accepted into a graduate school is crucial given the fierce competition for limited spots. In this research, a machine learning strategy for forecasting graduate admission is presented. The suggested technique builds a prediction model using machine learning (ML) algorithms like logistic regression (LR), support vector machines (SVM) and random forest classifier (RFC) using data from previous applicants' graduate record examinations (GRE) scores, cumulative grade point average (CGPA), and university rankings. A variety of performance criteria, including recall, accuracy, and precision, are used to evaluate a model. The findings demonstrate that the suggested machine learning model performs better than current methods and offers a solid resource for predicting graduate admission. This study can help educational institutions find qualified applicants for their graduate programmes and enhance the admissions procedure as a whole.