Reviewerly/exHarmony huggingface dataset:
Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods

This paper introduces a pioneering Hybrid Parallel Multi-linear Face Recognition algorithm that capitalizes on multi-linear methodologies, such as Multi-linear Principal Component Analysis (MPCA), Linear Discriminant Analysis (LDA), and Histogram of Oriented Gradients (HOG), to attain exceptional recognition performance. The Hybrid Feature Selection (HFS) algorithm is meticulously crafted to augment the classification performance on the CK+ and FERET datasets by amalgamating the strengths of feature extraction techniques and feature selection methods. HFS seamlessly incorporates Principal Component Analysis (PCA), Local Discriminant Analysis (LDA), and HOG. The primary aim of this algorithm is to autonomously identify a subset of the most distinctive features from the extracted feature pool, thus elevating classification accuracy, precision, recall, and F1-Score. By amalgamating these methodologies, the algorithm adeptly diminishes dimensionality while conserving pivotal features. Experimental trials on facial image datasets, CK+ and FERET, underscore the algorithm's supremacy in terms of accuracy and computational efficiency when contrasted with conventional linear techniques and even certain deep learning approaches. The proposed algorithm proffers an encouraging solution for real-world face recognition applications where precision and operational efficiency are of paramount significance.
An Intensive Spectrum for Intention Mining Analysis

There is huge volume of data in the social networks. This data can be retrieved and integrated to extract useful meaning and come out with the insights which is called as intentions. This can be used in different fields like business, recommender systems, education, Scientific research, games, etc. Also, there are various intention mining techniques which can be applied to several fields as information retrieval, business, etc. There is no specific definition of intention mining and also there is very less existing literature present. Accordingly, there is need to conduct systematic literature review of the very recent research area. Understanding intention mining, purpose of intention mining, categories and techniques of intention mining is the need. The paper endorses a spectrum for intention mining so that further literature review of intention mining can be completed. We validate our work through dimensions, categories and techniques for intention mining.
The Analysis of Data Tampering and Forensics in a Cloud Environment

In cloud systems, where sensitive data is stored and processed remotely, data manipulation is a serious security problem. The issues of data tampering in cloud environments are explored in this research, along with the importance of forensic investigation in reducing its effects. We explore several data tampering techniques and illustrate the need for strong security measures to guard against unlawful behavior. This paper also covers forensic analysis methods, tools, and techniques that are crucial in locating, analyzing, and minimizing data tampering instances in cloud systems. The security posture associated with cloud infrastructures can be greatly improved by integrating cutting-edge forensic procedures, ensuring data integrity, confidentiality, and overall system dependability.
An Effective Supply Chain Model using Blockchain in IoT with Trust Enabled Hybrid Concensus Algorithm

With the rapid growth of multiagent systems, concerns about privacy and security have assumed a position of paramount importance. Blockchain technology has gained a lot of attention since it first appeared because of its benefits in terms of decentralization, accessibility, traceability, and the capacity to be trustless. The increasingly complicated supply chains in today's world face significant issues regarding traceability and integrity. Blockchain technology holds out the possibility of developing a new concept for supply chain traceability, lapping these worries. Thus, in this research, the trust enable hybrid consensus algorithm is proposed for establishing consistency in the network with the irrelevant traders. The authorization is identified by the proposed model for accessing the Blockchain network depending on the accessibility rules. Depending on the various information sources, the authenticity of data is calculated, which makes the interaction between both the agent as well as the resource. The efficiency of the proposed model is determined by three different measures for secure data transfer. The attained minimal transaction time is 0.856 ms, memory usage is 87.684 KB, and responsiveness is 3.599 ms, respectively.
Image Retrieval Using Auto Encoding Features In Deep Learning

The latest technologies and growth in availability of image storage in day to day life has made a vast storage place for the images in the database. Several devices which help in capturing the image contribute to a huge repository of images. Keeping in mind the daily input in the database, one must think of retrieving those images according to certain criteria mentioned. Several techniques such as shape of the object, Discrete Wavelet transform (DWT), texture features etc. were used in determining the type of image and classifying them. Segmentation also plays a vital role in image retrieval but the robustness is lacking in most of the cases. The process of retrieval mainly depends on the special characteristics possessed by an image rather than the whole image. Two types of image retrieval can be seen. One with a general object and the other which may be specific to some type of application. Modern deep neural networks for unsupervised feature learning like Deep Autoencoder (AE) learn embedded representations by stacking layers on top of each other. These learnt embedded-representations, however, may degrade as the AE network deepens due to vanishing gradient, resulting in decreased performance. We have introduced here the ResNet Autoencoder (RAE) and its convolutional version (C-RAE) for unsupervised feature based learning. The proposed model is tested on three distinct databases Corel1K, Cifar-10, Cifar-100 which differ in size. The presented algorithm have significantly reduced computation time and provided very high image retrieval levels of accuracy.
New Era of Micro Cipher Algorithm (MCA) : Cipher Text for any Length of Plaintext

Cipher text is text which is in an unreadable format. It is the text which is transmitted between the people as an exchange of secret messages. The secret message is also called cipher text. In General, the existing algorithm-generated cipher text size is equal to or greater than the actual plaintext. In a cryptosystem, there is a chance to reduce the cipher text. We introduce a new algorithm called “Micro Cipher Algorithm (MCA)” in this connection. It is an algorithm that produces a little bit of cipher text for any length of the plaintext. This feature achieves many things like unbreakable code, transmitting data through different kinds of networks and machines very easily and quickly (simply throughput) and storage of cipher at different machines as a minute. It also reduces congestion and increases throughput in a networking. throughput in networking.
Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases

With the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain.
МЕТОДЫ ИЗУЧЕНИЯ ЗАСОЛЕНИЯ ПОЧВ, УРОВНЯ СТОКА, МИНЕРАЛИЗАЦИИ СТОКА В СЫРДАРЬИНСКОЙ ОБЛАСТИ

Recent research has demonstrated that the geographical mapping, tracking, assessment, and monitoring of soil salinity, seepage water levels, and seepage water mineralization may be effectively accomplished by integrating conventional approaches with geoinformation systems methods. Geoinformation systems provide you the ability to validate the veracity of GIS-based findings and promote the findings of subsequent field studies.
Otomatisasi Sistem Bagasi Terminal Internasional Di Bandar Udara Jenderal Ahmad Yani Semarang

The increasing number of flights proves that the public believes air transportation is one of the most desirable. Of course, the operation of the claim baggage conveyor will increase along with the number of existing passenger luggage. For this reason, the operation of the baggage claim conveyor needs to be done with the help of technology. This research was conducted to assist the work of airline porters, including time and energy efficiency through technology. This tool uses the Arduino Uno microcontroller as the program processing centre. This system will work when the Infrared sensor detects baggage after an x-ray, so the conveyor will be activated automatically. After the conveyor runs, another Infrared sensor will see the baggage on the conveyor. The conveyor will stop automatically when the Infrared sensor does not detect any baggage for a particular time. This tool is equipped with a Push Button so that the baggage claim conveyor can be operated manually and an LCD to explain the instrument's condition and the amount of baggage available. The amount of luggage can be monitored periodically via a smartphone using the ESP8266 WiFi module.
Rancang Bangun Sistem Kamera Pengawas dengan Pengenalan Wajah untuk Keamanan Berbasis Blynk Legacy

Covid-19 pandemic that has occurred since the beginning of 2020 has brought down all aspects of the country, starting from community activities to the economy. This has an impact on increasing the number of crimes committed by the community such as theft, robbery or other crimes. In this study, a room security system is proposed that uses a surveillance camera with a face recognition ability that records the face image of an intruder and records events as evidence of an intrusion. This system sends information quickly and automatically to the Android application user if an intruder who the camera doesn't recognize enters his house. The smartphone application user can control camera movements inside the house to monitor the movement of intruders and record the incident. This system uses 5 ESP32-CAM cameras. One camera is used to recognize and record the intruder's face image placed in front of the house and four cameras as surveillance and face recognition cameras are placed inside of the house. Each camera is driven by a servo motor controlled by a ESP8266 microcontroller. From the test results it is known that the maximum distance that the cameras still recognize the face image of an intruder or the home owner's face image is 2 meters when the light is bright. When it is dim, the camera in front of the house recognizes the face images up to 0.5 meters while the cameras inside of the house recognize the face images up to 1 meter. The average delay time for sending data from the camera system to application user is 201 ms to 617 ms.&#x0D; &#x0D;
Deep Learning-based Copy-Move and Spliced Image Forgery Detection

This paper proposes a Deep Learning (DL) based pre-trained AlexNet model for detecting and localizing copy-move and spliced forgery in photos. To localise forgeries in a photo, a binary mask is constructed using sobel operators. Further feature vectors are extracted patch wise from the input pictures. The Spatial Rich Model (SRM) is employed to address the generalisation issues in the DL model. There are three datasets used: Columbia Uncompressed Image Splicing Detection Evaluation (CUISDE), CASIA 1, and CASIA 2. The overall performance of the model has a 98.59 percent total accuracy as against 98.176% reported in the existing literature.
Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

Predicting progression and deciding on the best follow-up techniques for breast cancer patients is difficult because the illness is diverse and characterized by varying relapse risks. Due to its prevalence, breast cancer has become the top cause of mortality among women worldwide, making diagnosis and prognosis particularly challenging areas of medical study. In addition, the fear of a cancer relapse is a major factor influencing cancer patients' quality of life. The study aims to help doctors determine the likelihood of a breast cancer relapse by applying ensemble learning techniques. In this research, artificial neural networks (ANN) and deep neural networks (DNN) ensembled with Weighted averaging, minority, and majority voting approaches have been investigated for performance enhancements on the breast cancer recurrence dataset sourced from the UCI-ML repository. The empirical analysis shows that this ensemble learning-enabled proposed novel approach shows improved accuracy, precision, sensitivity, specificity, and F1-score of 96.21%, 96.59%, 98.84%, 84.62%, and 97.41%, respectively. The findings of this study can aid doctors in making more informed treatment decisions, thereby improving patient outcomes.
Enhancing Sign Language Recognition through Fusion of CNN Models

This study introduces a pioneering hybrid model designed for the recognition of sign language, with a specific focus on American Sign Language (ASL) and Indian Sign Language (ISL). Departing from traditional machine learning methods, the model ingeniously blends hand-crafted techniques with deep learning approaches to surmount inherent limitations. Notably, the hybrid model achieves an exceptional accuracy rate of 96% for ASL and 97% for ISL, surpassing the typical 90-93% accuracy rates of previous models. This breakthrough underscores the efficacy of combining predefined features and rules with neural networks. What sets this hybrid model apart is its versatility in recognizing both ASL and ISL signs, addressing the global variations in sign languages. The elevated accuracy levels make it a practical and accessible tool for the hearing-impaired community. This has significant implications for real-world applications, particularly in education, healthcare, and various contexts where improved communication between hearing-impaired individuals and others is paramount. The study represents a noteworthy stride in sign language recognition, presenting a hybrid model that excels in accurately identifying ASL and ISL signs, thereby contributing to the advancement of communication and inclusivity.
Advancing Chronic Respiratory Disease Care with Real-Time Vital Sign Prediction

Cardiovascular and chronic respiratory diseases, being pervasive in nature, pose formidable challenges to the overall well-being of the global populace. With an alarming annual mortality rate of approximately 19 million individuals across the globe, these diseases have emerged as significant public health concerns warranting immediate attention and comprehensive understanding. The mitigation of this elevated mortality rate can be achieved through the application of cutting-edge technological innovations within the realm of medical science, which possess the capacity to enable the perpetual surveillance of various physiological indicators, including but not limited to blood pressure, cholesterol levels, and blood glucose concentrations. The forward-thinking implications of these pivotal physiological or vital sign parameters not only facilitate prompt intervention from medical professionals and carers, but also empower patients to effectively navigate their health status through the receipt of pertinent periodic notifications and guidance from healthcare practitioners. In this research endeavour, we present a novel framework that leverages the power of machine learning algorithms to forecast and categorise forthcoming values of pertinent physiological indicators in the context of cardiovascular and chronic respiratory ailments. Drawing upon prognostications of prospective values, the envisaged framework possesses the capacity to effectively categorise the health condition of individuals, thereby alerting both caretakers and medical professionals. In the present study, a machine-learning-driven prediction and classification framework has been employed, wherein a genuine dataset comprising vital signs has been utilised. In order to anticipate the forthcoming 1-3 minutes of vital sign values, a series of regression techniques, namely linear regression and polynomial regression of degrees 2, 3, and 4, have been subjected to rigorous examination and evaluation. In the realm of caregiving, a concise 60-second prognostication is employed to enable the expeditious provision of emergency medical aid. Additionally, a more comprehensive 3-minute prognostication of vital signs is utilised for the same purpose. The patient's overall health is evaluated based on the anticipated vital signs values through the utilisation of three machine learning classifiers, namely Support Vector Machine (SVM), Decision Tree and Random Forest. The findings of our study indicate that the implementation of a Decision Tree algorithm exhibits a high level of accuracy in accurately categorising a patient's health status by leveraging anomalous values of vital signs. This approach demonstrates its potential in facilitating prompt and effective medical interventions, thereby enhancing the overall quality of care provided to patients.
SISTEM INFORMASI PEMBELAJARAN DIGITAL BERBASIS WEB PADA MATERI PEMBELAJARAN BASIS DATA KELAS XI RPL SMK PEMBANGUNAN AMPEL

Ampel Development Vocational School is a private vocational high school located on JL. Baru Kaligentong Village, Gladagsari District, Boyolali Regency. Ampel Development Vocational School has 3 majors, namely RPL (Software Engineering), AK (Accounting), and TBSM (Motorcycle Engineering and Business). The implementation of the learning activity process at the Development Vocational School is still conventional.&#x0D; The aim of this research is to create a web-based digital learning information system that can support the student learning process at Ampel Development Vocational School. The system development method used is R&amp;D (Research and Development) and uses Unifield Modeling Language (UML) modeling using Use Case Diagrams, Activity Diagrams.&#x0D; The results of this research are able to support the teaching and learning process at Ampel Development Vocational School by using a web-based digital learning information system that can be used by teachers and students by accessing it via the internet.
Implementasi Aplikasi Mobile HRIS Pada PT YKK Zipper Indonesia Menggunakan Metode Borg and Gall

Human resources are organizational resources that need to be managed effectively and efficiently. The amount of information that needs to be managed related to human resources creates very complex problems. The importance of managing very complex human resources in a company makes the company have to form a new department, namely the Human Resources Department or Personnel Department. PT Ykk Zipper Indonesia is currently still using a desktop-based application Hris system where the obstacles of employees when submitting Overtime, Leave and Salary Slips must be in the office environment, from these problems researchers try to implement this mobile application it is hoped that the problem of submitting Overtime, Leave and mobile salary slips can be resolved. The research method is based on the Borg and Gall method. This method includes several steps to make a product. The results of this study are presented in the form of an application, according to this method the results of this application have only reached the initial stage of product development.
Multi Factor Authentication and IP Address Restriction based Question Paper Delivery System for Indian Universities

The traditional question paper delivery (QPD) procedure in Indian institutions is discussed in this paper with a focus on its downsides, including complexity, time requirements, security issues, environmental impact, and cost. This method is manual, which makes it prone to mistakes, rigid, and open to leaks and tampering, especially when it comes to the security of the exam questions. It also looks at security issues involving question paper leaks, highlighting ongoing difficulties in preserving the integrity and confidentiality of the Indian educational system. These occurrences highlight the requirement for a more reliable and secure QPD system. &#x0D; In this paper workflow of the cloud-based software system is described in detail, with examples showing how it makes it easier to distribute safe exam papers and assures stakeholder accountability. Barcodes are integrated into the system, and their improved tracking, processing, and distribution of exam papers is highlighted. A cutting-edge cloud-based Question Paper Delivery System (OQPDS), which automates and streamlines the process of generating, distributing, and giving exams for Indian colleges, is the suggested cure. Modern security elements are used by the system, including two-step authentication, barcode-based data storage, data encryption during data transfer, IP address-based access restrictions, and time-based content delivery. The incorporation of these security elements enhances the overall security and efficiency of the QPD system. The proposed system's workflow is discussed in detail along with examples that demonstrate how it facilitates the distribution of secure exam papers and ensures stakeholder responsibility. The system incorporates barcodes, and its better distribution, processing, and tracking of exam papers is praised. The system's capacity to limit access based on IP addresses is described, along with how it might help with traceability and prevent unwanted access. Additionally, the paper proposes watermarking to label test papers with the IP address of the testing facility, offering benefits including improved security, tamper detection, and traceability. It is highlighted that encryption is a key security tool for preserving data integrity, maintaining secrecy, and protecting against online dangers. Finally, the cloud-based Question Paper Delivery System is a solution to the security issues and inefficiencies of the current QPD system. This system intends to transform the examination procedure in Indian institutions by offering a more safe, effective, and transparent approach to question paper administration. It does this through contemporary security provisions and cutting-edge technology. This paper also discusses comparative analysis between traditional and proposed automated Question paper delivery system with respect to efficiency, security, cost reduction and flexibility.