Reviewerly/exHarmony huggingface dataset:
Peran Orang Tua Pada Pembelajaran Jarak Jauh Ditinjau Dari Aspek Hasil Belajar

With the emergence of the Covid-19 outbreak, the Ministry of Education, Culture, Research, and Technology issued Circular Letter number 15 of 2020 which contains about organizing learning from home during the emergency period of COVID-19's spread. That policy makes teaching and learning activities were carried out remotely. Researcher uses kualitatif deskriptif with data collection technique interview. The researcher uses triangulation to check the validity of the data which obtained from several sources. The result of the study shows that some parents are able to know enough about their role as parents in distance learning. It has been described in the indicators of student learning, students who get unsatisfactory learning outcomes can be indicated that the role of parents is low, it can be seen that the role of parents is important in achieving the success of student learning, especially during distance learning.
Use Case Smart Contract for Lease Agreements using Blockchain Technology

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
The Effect of Education through Digital Media WhatsApp Auto Responding (WAR) on Knowledge about Anemia in Pregnant Women

Anemia in pregnant women is still a problem in Parung Panjang village, Parung Panjang sub-district, Bogor district. The main cause is a lack of knowledge about the importance of iron for pregnant women. To address this problem, a quasi-experimental pre-posttest one group design study was conducted with a sample of 18 pregnant women and preconception women. The knowledge measurement tool used was a questionnaire. The measuring instrument used was a questionnaire on knowledge about anemia in pregnant women. The results showed a significant increase (p &lt;0.05) in the knowledge of pregnant women and preconception women about anemia after the education and counseling activities were carried out, and they were enthusiastic and active in the discussion. The conclusion of this study is that the WhatsApp Auto Responding (WAR) media can be effectively used as one of the efforts to improve knowledge and prevent anemia in pregnant women in Parung Panjang village. The recommendation is to make education and counseling activities related to anemia using WAR a routine activity in Parung Panjang Village.Keywords: Anemia, auto responding, online education, pregnant women, WAR.
Comparing Deep Learning and Traditional ML for Detecting Spam and Trolls on Video Sharing Sites

This study compares the performance of deep learning and conventional machine learning (ML) models in identifying spam and trolling comments on video sharing websites. Convolutional and recurrent neural networks, as well as other deep learning (DL) architectures, are contrasted with more conventional machine learning techniques like KNN and Support Vector Machines using a sizable dataset of comments. Utilizing user data, text sentiment analysis, and other metadata, the models are assessed. The findings demonstrate that RNNs and CNNs, in particular, perform better in terms of accuracy, recall, precision and F1 score than do classic machine learning models. Traditional machine learning models, such as KNNs, decision trees, and SVMs, performed badly in comparison. The study highlights the potential of deep learning models for better spam and trolling identification on video sharing platforms and identifies other research areas for improving the models' performance in practical implementations.
Advanced Privacy Preserving Model for Smart Healthcare Using Deep Learning

The rise of intelligent healthcare, the rise of the internet of medical things, the corona virus pandemic, and the rise of big data-based medical applications. The generated biomedical data is extremely private and confidential. Unfortunately, traditional medicine systems are unable to handle the enormous volume of biomedical data. Consequently, data is usually the cloud is used to store and share data. Afterward, several uses for the shared data are made such as investigation and the finding of novel information. Biomedical information typically appears in written form (such as test results, medication recommendations, and diagnoses). Unfortunately, such data is exposed to a variety of security risks and attacks, including privacy and confidentiality breaches. Despite significant advancements in the security of biological data, most current methods produce significant delays and are unable to handle real-time responses. In order to enhance the healthcare system, this study presents an innovative, fog-enabled, privacy-preserving paradigm termed “r sanitizer.” This work is based on a Convolutional Neural Network with Bidirectional Long Short Term Memory (LSTM) and recognizes medical entities efficiently. The test results show that with a recall of 91.15%, precision of 92.6%, and a F1 score of 92%, r sanitizer beats the most recent models. Comparing the sanitization model to the state-of-the-art reveals a 28.77 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> improvement in privacy preservation.
Artificial Intelligence-Based Learning Approaches for Medical Imaging

Artificial Intelligence is the most debated and discussed topic in the field of the health sector, especially in the research of medical imaging. It has the power to process many medical images and uncover those diseases that are not appreciated by the naked eye. AI-based models such as machine and deep learning can recognize patterns that aid medical diagnoses. In fact, these learning models have outperformed human capabilities in most applications, such as computer-aided diagnosis/detection, prediction of diseases, segmenting images, image generation, etc. Hence, this paper discusses the role and significance of AI and its techniques, such as machine and deep learning models, in medical imaging. Besides this, the works of the researchers who have used these techniques to process medical images are also presented along with their challenges that still persist.
TrackerBot: Parallel Object Tracking and Collision Avoidance Design for Unmanned Ground Vehicles

Autonomous rovers are mobile robots, designed to operate in various conditions without any human intervention. These rovers are equipped with sensors, actuators, and control systems that enable them to navigate and interact with their environment. They are widely employed in fields like exploration, search and rescue, environmental monitoring, and surveillance. In this paper, we propose TrackerBot, which is an autonomous rover that integrates parallel object tracking, videography, and collision avoidance in real-world scenarios. The proposed rover can navigate safely and capture high-quality visual data of its targeted moving object. The rover control system is operated via a proportional-integral-derivative (PID) controller, and is accompanied by computer vision capabilities. It enables it to seamlessly detect and track objects at a parallel level in its environment, avoid obstacles and collisions, and record video footage with minimal hardware requirements. The proposed system performance is evaluated through its ability to navigate complex environments and capture high-quality visual data.
Deep Learning Techniques for Sentiment Analysis: A Comparative Study

In today's digital era, exposure to the internet has changed the economic space; now people give their views regarding products and services provided to them online so that others can decide based on these reviews, and service providers can also take benefits from these to improve their product or service. This ultimately helpful in economy growth. In this direction, sentiment analysis provides a means to gain insight into people's opinions. This study examines the use of three distinct deep learning models: LSTM, GRU, and ELECTRA to apply sentiment analysis to the Spotify music app. Comparing the accuracy of each model, it is discovered that LSTM and GRU perform better than ELECTRA, with accuracy values of 77.21 % and 77.32%, respectively, as opposed to ELECTRA's accuracy value of 72.03 %. These findings imply that the particular dataset and attributes utilized for training these models may affect their efficacy. In conclusion, this work sheds light on the effectiveness of deep learning models for sentiment analysis and emphasizes the significance of careful model and feature selection for obtaining correct findings.
A Survey and Comparative Analysis of Relevant Approaches of Recommendation System

From the past decade, there are many recommended system are being used for determining various problem in different field like entertainment, social media and e-commerce. However, RS systems have been gained significant success in giving recommendations of items using different classification methods but still many problems has been encountered in this field like sparsity and cold start. In this study, we are providing the insight of research trends which connects the new technical sides of RS employed in many research areas. To get the reliable survey of the recommendation system, various studies were explored to analyze the different filtering methods, algorithms and challenges associated with the RS. This study revealed an interaction between the process and quantifiable development of several in-depth analyses of recommendation systems and the commercial expansion of the actual employed industry of services. In addition to giving a thorough overview of recommendation systems, this study sheds light on a variety of technologies and trends in the service industry, where recommendation systems are used, for the benefit of several academics who are interested in recommendation systems.
Use of Neural Machine Translation in Multimodal Translation

Multimodal Neural Machine Translation (MNMT) is a type of Machine Translation that allows the translation of source language that contains various forms of information, such as images, video, audio, and signs, into the target language while maintaining the intended meaning of the source language. In MNMT, the use of deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are required to process different modalities and to generate an appropriate translation. The use of attention mechanisms, early and late fusion techniques, and hierarchical fusion have significantly improved the accuracy and fluency of translation. The performance of different MNMT systems can be evaluated in terms of various metrics such as BLEU score, accuracy, and fluency. MNMT has the potential to revolutionise the way we communicate by bridging the gap between different languages and cultures. The integration of MNMT in various industries, such as e-commerce, tourism, and healthcare, can bring significant benefits in terms of customer experience and accessibility. It states how Neural Machine Translation plays a significant role in processing different modalities in Multimodal Neural Machine Translation. More research work in this field can produce effective and innovative ideas to improve Multimodal Neural Machine Translation.
Multimedia Security Using Blockchain and IoT-Based Distributed System

This article explores how IoT device systems handle multimedia data and presents a secure multimedia framework utilizing blockchain technology. The proposed framework high- lights how blockchain ensures the safety and transparency of multimedia records, document access, and shipping processes between owners and customers. To address these challenges, we introduce a decentralized peer-to-peer multimedia file-sharing platform based on the Inter Planetary File System (IPFS) built on blockchain technology. Additionally, we propose a multimedia file block approval consensus mechanism. Recently, blockchain has been recognized as a potential technology to enforce critical security requirements in IoT devices. It offers protection for IoT networks that require sharing multimedia data, including applications in smart cities, retail, energy, security surveillance, and healthcare.
A Direct Proof of Katsevich’s Algorithm in 2D Case

In this work, we propose a direct proof of Katsevich’s algorithm in two-dimensional (2D) case where the proof starts off directly from the 2D fan-beam projection data. Our proof connects the fan-beam data to the Fourier data directly. The geometric meaning in our approach manifests Tuy’s condition. During the proof, we identify that the 2D version of Katsevich’s algorithm is valid for a broader class of scanning trajectories. Also, during the proof, we identity that the so-called filtered backprojection (FBP) algorithm along π-lines comes out of Katsevich’s algorithm very naturally in 2D case.
Automatic Face Mask Detection Using Deep Learning-Based Mobile-Net Architecture

COVID-19 affects public health and economies around the world. Proper wearing of masks plays a vital role in daily life to overcome this epidemic situation. To reduce human contamination, adequate people monitoring is required in public places, i.e., malls, railway stations, bus stands, markets, airports, universities, etc. In highly populated areas, manual inspection of the face mask is not easy, so an automatic face mask detection model helps detect the face mask. This paper proposed mobile Net architecture for face mask detection. This advanced architecture considers the colour image from the standard and its own database for extracting the face feature in-depth to identify the face mask. To validate our work, we used five-fold cross-validation techniques. Finally, the accuracy achieved by the proposed work on two standard databases is 96.4% and 95.7%, showing the outperforming face mask detection results.
Research and Application of English Question Answering System Based on BiLSTM Model Under Heuristic Teaching Concept

In order to improve the accuracy and effectiveness of the named entity recognition model in the English question answering system, this paper proposes a BiLSTM named entity recognition model based on multi-head attention mechanism. Based on the classical named entity recognition model BILSTM-CRF, the model introduces multi-head attention mechanism for improvement, and it adopts parallel mode to combine BiLSTM and multi-head attention mechanism, thereby improving recognition effect of the model. Experimental results show that among models with four different combinations, the parallel combination BiLSTM-CRF-Attention model adopted has higher recognition accuracy, F1 value is 4.94% higher than that of the secondary model, and the recognition effect is better. Under the premise of parallel combination, the F1 value of the BiLSTM-CRF-Attention* named entity recognition model adding CPF layer and multi-head attention mechanism reaches 0.8942, which is 28.24%, 27.01%, and 9.53% higher than the model constructed based only on BiLSTM, the model built based on BiLSTM-CRF, and BiLSTM-CRF-Attention model introducing a single self-attention mechanism. As can be seen, the performance improvement effect is significant, which means the model is worthy of further research and promotion.
Employing Convolutional Neural Network for IoT Healthcare Attack Detection in Intensive Care Unit

The Internet of Things helps the healthcare sector to embrace the methodologies with emerging technologies and facilitate technological change from traditional to highly equipped technology. Some of the frequent uses of the Internet of Things in healthcare include monitoring patient healthcare, enhancing proper healthcare and searching medical resources. This digitalization and increase in the use of internet-connected sensors in healthcare have taken to a new height through advancement in technology. But security and efficient use of IoT in healthcare is still a critical challenge. Cyber-attacks are very common at present with the main motive of hacking private patient data and stealing other health information from the devices equipped for fetching patient's well-being. This research paper shows the employment of a convolutional neural network for detecting attacks on Intensive care units in hospitals. The results achieved by the convolutional neural network technique prove that deep learning methodologies are a promising solution to provide reliable and trustworthy communication in healthcare sectors with the Internet of Things devices.
Sentiment Analysis for Product Reviews Using Deep Learning Techniques

Sentiment analysis or opinion mining, is the process of identifying and extracting subjective information from source materials. It is important in the context of user-generated content, such as tweets or Facebook posts, or Amazon product reviews, to understand the overall sentiment towards a certain topic or brand. This can provide valuable insights for businesses and organizations looking to improve their online reputation and engagement with customers. Traditionally, sentiment analysis has been handled through manual methods such as reading through and categorizing individual pieces of content. However, with the advent of big data and machine learning, automated methods have become more prevalent in the field. Methods such as natural language processing and machine learning algorithms are commonly used in sentiment analysis. This research paper focuses on a hybrid method for sentiment analysis on product reviews. Compared to other methods of deep learning and machine learning this hybrid approach excels in overall performance.
Fall Detection Methods for Elderly People- A Comprehensive Survey

This survey research gives a thorough analysis of sensor technology and picture preprocessing methods for fall detection systems for elderly people. Caretakers face serious health hazards and difficulties when an older person falls. Numerous sensor-based and vision-based solutions have been put forth to deal with this problem. This survey talks about the difficulties with effective implementation of these techniques, such as sensor location, data fusion, and power management. It also examines several pictures' preprocessing methods, including background removal, object detection, tracking, and feature extraction, which are crucial for improving the precision and responsiveness of vision-based systems. This study also examines the benefits and drawbacks of the various sensor technologies used, such as accelerometers, gyroscopes, depth cameras, infrared sensors, pressure mats, and wearables. Important data on elderly falls is also provided to further emphasize how urgent it is to have effective fall detection systems. With the help of improved fall detection systems, this study will serve as a significant resource for researchers and practitioners, leading future developments to increase the safety and well-being of senior people.
Deep Neural Network Deception Using A Single Pixel Assault

Previous studies have shown that Deep Neural Networks' (DNN's) outputs is not consistent and very susceptible to inputs vectors perturbations of any size, and as a result, a number of techniques have been developed for creating effective perturbations on the connections. In this research, we offer a novel divergent transformation method for visually computing very small adversarial perturbations (few-pixel attacks). It works with a wider range of DNN different classifiers and calls for far less adverse data. The findings indicate that, on average, 73.8percent of total of the test photos can be modified to create hostile images by changing just one pixel. Additionally, it is well recognized that analyzing the DNN resilience problem can provide crucial hints for comprehending the geometric characteristics of the DNN decision. a high-dimensional inputs area map. In contrast to earlier research, the outcomes of performing few-pixel attack give quantifiable data and analyses to the geometric knowledge, Genetic algorithm (GA)
Natural Language Processing and Deep Learning Techniques to Improve Sentiment Analysis in Social Media Texts

Sentiment analysis (SA) is a mechanized strategy for finding and understanding the feelings depicted in text. Over the most recent decade, SA has altogether expanded in ubiquity in the Natural Language Processing (NLP) people group. Because of the inescapable utilization of social media and web stages, SA is presently urgent for firms to get client info and shape their promoting approach. The motivation behind this study is to utilize a deep learning-based way to deal with natural language process to give an exhaustive investigation of the opinion of sentiment of social media content. The technique then builds a comparable model that may be applied to replicate the relevant social process. In instance, by dynamically choosing the most significant phrase in the current state depending on the information presented, the network can effectively recognize the significant material that is always changing. This prompts the semantic comprehension of the total sentence toward the finish of each phase of the interaction. Besides, an absence of context oriented data will prompt an incorrect and muddled semantic portrayal of natural language on the grounds that relevant data is fundamental for that portrayal. In this paper, we take a gander at the unimodal and multimodal social network sentiment analysis algorithms and foster two models for text sentiment investigation and picture-text multimodal sentiment examination.
Improving Data Integrity of IPFS On-Chain Proof

Decentralized cloud storage is made possible via the IPFS design, which makes use of peer-to-peer networking and content addressing. Its 200,000 network nodes, 2 million weekly users, and 125 TB of gateway traffic as of August 2021 make IPFS a well-liked subject for extensive operational network research. Although research on its structure and operation is scarce, it is used as a building component in a variety of projects and applications. As a result, we have provided some information on the architectural design, operating principles, characteristics, features, and difficulties that it presents. In general, IPFS offers a variety of capabilities and features that can be leveraged to create a fantastic decentralized application.
Distress Detection Using a Hybrid SVM - CNN Classifier

Due to the escalating frequency of reported crimes, there has been a surge in research endeavors focused on various approaches to enhance monitoring and surveillance techniques. As opposed to vision-based applications, which are currently the most used framework for monitoring purposes, audio-based systems can be more flexible and relatively less intrusive. While current research studies predominantly utilize images as the primary input for Deep Learning (DL) algorithms, it is worth noting that sound can also serve as a valuable source of input for these models. In this paper, we propose and develop a novel hybrid deep learning model for identifying and detecting people in distress by their screams. The working of our proposed system is built by integrating sound detection module and DL model which will help us to detect if a person is in distress or not. The system uses hybridization concept consisting of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models, with the audio snippet undergoing 3 levels of classification, with the accuracy of each level found to be 93%, 100% and 92% respectively. The audio data is finally classified as an instance of distress or no-distress.