Search

As healthcare systems transition into an era dominated by quantum technologies, the need to fortify cybersecurity measures to protect sensitive medical data becomes increasingly imperative. This paper navigates the intricate landscape of post-quantum cryptographic approaches and emerging threats specific to the healthcare sector. Delving into encryption protocols such as lattice-based, code-based, hash-based, and multivariate polynomial cryptography, the paper addresses challenges in adoption and compatibility within healthcare systems. The exploration of potential threats posed by quantum attacks and vulnerabilities in existing encryption standards underscores the urgency of a change in basic assumptions in healthcare data security. The paper provides a detailed roadmap for implementing post-quantum cybersecurity solutions, considering the unique challenges faced by healthcare organizations, including integration issues, budget constraints, and the need for specialized training. Finally, the abstract concludes with an emphasis on the importance of timely adoption of post-quantum strategies to ensure the resilience of healthcare data in the face of evolving threats. This roadmap not only offers practical insights into securing medical data but also serves as a guide for future directions in the dynamic landscape of post-quantum healthcare cybersecurity.

Abstract This article discusses an effective technique for detecting abnormalities in Hajj crowd videos. In order to guarantee the identification of anomalies in scenes, a trained and supervised FCNN is turned into an FCNN using FCNNs and temporal data. By minimizing computational complexity, incorrect movement detection is utilized to achieve high performance in terms of speed and precision. This FCNN-based architecture is designed to handle two primary tasks: feature representation and the detection of incorrect movement outliers. Additionally, to overcome the aforementioned issues, this research will generate a new crowd anomaly video dataset based on the Hajj pilgrimage scenario. On the proposed dataset, the UCSD Ped2, Subway Entry, and Subway Exit datasets, the proposed FCNN-based technique obtained ultimate accuracy of 100%, 90%, 95%, and 89%, respectively. Additionally, the ResNet50-based technique achieved ultimate accuracy of 96%, 89%, 94%, and 92%, respectively, for the proposed dataset, the UCSD Ped2, Subway Entry, and Subway Exit datasets.

Masih adanya kepala sekolah baik Negeri maupun Swasta yang memiliki latar belakang pendidikan yang berbeda bukan hanya yang berpendidikan guru tetapi ada juga yang non kependidikan. Bahkan semakin lamanya mereka menjabat menjadi Kepala Sekolah akan menimbulkan suatu kejenuhan tanpa ada mutasi sehingga berpengaruh terhaadap kinerja kepala sekolah. Tujuan penelitian pada artikel ini adalah untuk mengetahui pengaruh antara kemampuan kerja dan motivasi kerja terhadap kinerja kepala sekolah SD se kecamatan Bangko Pusako kabupaten Rokan Hilir. Penelitian menggunakan metode survey dengan teknik korelasi. Populasi penelitian melibatkan 137 guru SD se kecamatan Bangko Pusako kabupaten Rokan Hilir. Besarnya sampel adalah 101 orang guru. Data dalam penelitian dikumpulkan dengan teknik angket. Hasil penelitian menunjukkan bahwa terdapat pengaruh positif dan signifikan kemampuan kerja terhadap kinerja kepala sekolah SD se kecamatan Bangko Pusako kabupaten Rokan Hilir.

This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully convolutional neural network (FCNN)-based method to monitor crowd analysis, especially for the classification of crowd density. This study aims to address the current technological challenges faced in video analysis in a scenario where the movement of large numbers of pilgrims with densities ranging between 7 and 8 per square meter. To address this challenge, this study aims to develop a new dataset based on the Hajj pilgrimage scenario. To validate the proposed method, the proposed model is compared with existing models using existing datasets. The proposed FCNN based method achieved a final accuracy of 100%, 98%, and 98.16% on the proposed dataset, the UCSD dataset, and the JHU-CROWD dataset, respectively. Additionally, The ResNet based method obtained final accuracy of 97%, 89%, and 97% for the proposed dataset, UCSD dataset, and JHU-CROWD dataset, respectively. The proposed Hajj-Crowd-2021 crowd analysis dataset and the model outperformed the other state-of-the-art datasets and models in most cases.

In recent years, the number of studies using whole-slide imaging (WSIs) of histopathology slides has expanded significantly. For the development and validation of artificial intelligence (AI) systems, glass slides from retrospective cohorts including patient follow-up data have been digitized. It has become crucial to determine that the quality of such resources meets the minimum requirements for the development of AI in the future. The need for automated quality control is one of the obstacles preventing the clinical implementation of digital pathology work processes. As a consequence of the inaccuracy of scanners in determining the focus of the image, the resulting visual blur can render the scanned slide useless. Moreover, when scanned at a resolution of 20× or higher, the resulting picture size of a scanned slide is often enormous. Therefore, for digital pathology to be clinically relevant, computational algorithms must be used to rapidly and reliably measure the picture’s focus quality and decide if an image requires re-scanning. We propose a metric for evaluating the quality of digital pathology images that uses a sum of even-derivative filter bases to generate a human visual-system-like kernel, which is described as the inverse of the lens’ point spread function. This kernel is then used for a digital pathology image to change high-frequency image data degraded by the scanner’s optics and assess the patch-level focus quality. Through several studies, we demonstrate that our technique correlates with ground-truth z-level data better than previous methods, and is computationally efficient. Using deep learning techniques, our suggested system is able to identify positive and negative cancer cells in images. We further expand our technique to create a local slide-level focus quality heatmap, which can be utilized for automated slide quality control, and we illustrate our method’s value in clinical scan quality control by comparing it to subjective slide quality ratings. The proposed method, GoogleNet, VGGNet, and ResNet had accuracy values of 98.5%, 94.5%, 94.00%, and 95.00% respectively.

Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating additional dataset we divide the process of images augmentation into two routes. In the first route, we utilized magnitude extraction followed by the polar magnitude. In the second route, we performed morphological operation followed by transforming the image into skeleton. This paper presented a solution to the challenge of measuring crowd density using a surveillance camera pointed at a distance. An FCNN-based technique for crowd analysis is included in the proposed methodology, particularly for classifying crowd density. There are several obstacles in video analysis when there are a large number of pilgrims moving around the tawaf area, with densities of between 7 and 8 per square meter. The proposed DHCDCNNet method has achieved accuracy of 97%, 89% and 100% for the JHU-CROWD dataset, the UCSD dataset and the proposed Hajj-Crowd dataset, respectively. The proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had accuracy of 98%, 97% and 97%, respectively, using the VGGNet approach. Using the ResNet50 approach, the proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had an accuracy of 99%, 91% and 97%, respectively.

Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

Penelitian ini bertujuan untuk melihat dan mengetahui apakah musik daerah khususnya musik Jawa dapat mereduksi tingkat stres pada mahasiswa. Rancangan penelitian ini menggunakan One Group Pre and Posttest Design. Subjek penelitian ini yaitu mahasiswa sebanyak 9 mahasiswa dan diperoleh dengan cara purposive sampling. Alat ukur yang digunakan adalah skala stres dan SUDS (subjective units of distress scale). Hasil penelitian menunjukkan nilai (t) = 6,214 dan nilai (p) = 0,000 dimana hasil tersebut menyatakan bahwa ada penurunan skor stres yang signifikan pada mahasiswa sebelum dan sesudah mendengarkan musik Jawa

Artikel ini bertujuan untuk menganalisis asas konsensualisme (asas kesepakatan para pihak) dalam perjanjian di Lembaga Keuangan Syariah. Asas konsensual adalah perjanjian itu ada sejak tercapai kata sepakat antara pihak yang mengadakan perjanjian yang berlaku dalam sistem hukum perjanjian Indonesia. Sedangkan dalam Islam dinamakan asas kerelaan (Al Ridha), Asas ini menyatakan bahwa semua kontrak yang dilakukan oleh para pihak harus didasarkan kepada kerelaan semua pihak yang terlibat di dalamnya. Implementasi asas konsensualisme/asas kerelaan dalam perjanjian/aqad lembaga keuangan syariah adalah perjanjian/aqad yang ada dalam lembaga keuangan syariah itu sudah tersedia tanpa melibatkan calon nasabah, nasabah tinggal membaca dan menelitinya, tanpa bisa merubah isi perjanjian/aqad, kalau dia sepakat maka tinggal membubuhkan tanda tangannya. Bentuk penanda tanganan kedua belah pihak ini, menunjukan kesepakatan para pihak. Kata Kunci : Asas Konsensualisme, Lembaga Keuangan Syariah, Hukum Perjanjian ANALYSIS OF CONSENSUALISM IN SHARIA FINANCIAL INSTITUTION Abstract This article attempts to analyze consensualism (consensus of all parties) in Sharia Financial Institution’s agreement within Indonesian contract law system. In Islam, it is referred to as al-ridha. This basis states that every contract should be based on mutual consensus among parties involved. Implementation of consensualism in Sharia financial institution’s agreement usually initiated by the institution only. Customers have just read and analyzed it without any chance to modify the content. Should they agree with the contract, they can sign it. The signature of both parties shows mutual consensus. Keywords: Consensualism, Sharia Financial Institution, Contract Law

This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This paper aims to propose an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

Background: Autism Spectrum Disorder (ASD) is a complex developmental condition that involves persistent challenges in social interaction, speech, and nonverbal communication, in addition to repetitive or restrictive behaviours. For decades, children with ASD have been familiarising themselves with information and communication technologies (ICT) in their training and diagnosis. One of the ICT areas, namely non-immersive virtual reality (NIVR), has become a noticeable tool to help ASD children in their social training. It provides extensive virtual interaction, a safe environment, and is affordable. An NIVR application is developed to assist the intervention on ASD children. However, the whole experiences of the training need to be validated to conclude its effectiveness. Methods: A case study was employed as the research method. An evaluation of NIVR application using multiple sources of evidence was guided by Kirkpatrick Model of Evaluation (KME) which was executed via questionnaires, pre- and post-test. The main objectives of this research were to evaluate level 1 and 2 of KME. The target for Level 1 is to assess the reactions to the NIVR application. Level 2 is to gauge the knowledge, confidence, or mindset of participants. Level 2 covers the evaluation prior to the training (pre-test) and after the training (post-test). Results: On average the ASD children had good experiences and were able to improve their social skills with the NIVR application. Therefore, the combination of serious game, analytics and specific VR type provides good data assessment, facilitate comfortable training, and can be an effective intervention for children with ASD. Conclusion: The positive trend on both levels shows that the application has a good potential to be used in ASD training. The results could be improved in a higher number of participants. Currently, only a limited number of research participants could be obtained due to the COVID-19 pandemic.