Popular Applications Research Articles

Implementasi Algoritma Naïve Bayes dan Algoritma C4.5 Untuk Melakukan Analisis Sentimen terhadap Ulasan Komentar Pengguna TikTok di Google Play Store

TikTok is a popular application among young people. TikTok was an application initially launched in China before landing in Indonesia at the end of 2017. Unfortunately, the popularity of TikTok stems from personal lack of self-image, for example wearing sexy clothes, dancing in erotic and inappropriate moves. This is based on many positive and negative comments from TikTok users. So we need a way to automatically classify reviews through sentiment analysis. The purpose of this study is to classify TikTok user comments on Google Play Store using Naive Bayes and C4.5 algorithms. This study used 1330 data, of which 602 data were negative and 728 data were positive. The results show that the Naive Bayes algorithm produces accuracy values ​​of 79.00%, 79.00% precision, 78.00% recall, and 78.00% F1 score. The C4.5 algorithm produces 68.00% accuracy, 68.00% precision, 68.00% recall, and 68.00% F1 score. We can conclude that the Naive Bayes algorithm is the best algorithm compared to the C4.5 algorithm. The Naive Bayes algorithm achieves an accuracy value of 79.00%.

Panoramic Review on Progress and Development of Molecular Docking

In structural molecular biology and computer-assisted drug creation, molecular docking is a crucial tool. Predicting the prevailing binding mode (s) of a ligand with a protein having a known three-dimensional structure is the aim of ligand-protein docking. Effective docking methods use a scoring system that correctly ranks candidate dockings and efficiently explore high-dimensional spaces. Lead optimization benefits greatly from the use of docking to do virtual screening on huge libraries of compounds, rate the outcomes, and offer structural ideas for how the ligands inhibit the target. It can be difficult to interpret the findings of stochastic search methods, and setting up the input structures for docking is just as crucial as docking itself. In recent years, computer-assisted drug design has relied heavily on the molecular docking technique to estimate the binding affinity and assess the interactive mode since it can significantly increase efficiency and lower research costs. The main concepts, techniques, and frequently utilized molecular docking applications are introduced in this work. Additionally, it contrasts the most popular docking applications and suggests relevant study fields. Finally, a brief summary of recent developments in molecular docking, including the integrated technique and deep learning, is provided. Current docking applications are not precise enough to forecast the binding affinity due to the insufficient molecular structure and the inadequacies of the scoring mechanism.

Generation-based fuzzing? Don’t build a new generator, reuse!

Generation-based fuzzing is effective in testing programs that require highly structured inputs. However, building a new generator often requires heavy manual efforts to summarise a large body of grammar rules to generate correct structures.In this paper, we introduce the idea of generator reuse, aiming to avoid the manual efforts required to build new generators. Our key insight is that for a format X (e.g., PDF) whose grammar rules are not yet supported by existing generators, we can often find generators that support the grammar rules of a different format Y (e.g., HTML) and converters between Y and X (e.g., HTML-to-PDF converters). Reusing the generators for Y and the converters, we can effortlessly assemble a generator to support the grammar rules of X (e.g., given an HTML generator and an HTML-to-PDF converter, we can connect them to form a PDF generator).To explore the validity of our idea of generator reuse, we apply the idea to build a PDF generator, reusing a popular HTML generator and a set of mainstream HTML-to-PDF converters. Throughout the application, we also gain an initial understanding of the limitations of our generator reuse idea and introduce a set of strategies aiming to mitigate those limitations. We evaluated Arcee using 6 infrastructural, popular PDF applications and libraries. Our empirical results show that the PDF generator is indeed useful: running it on the 6 applications and libraries for 2 weeks, we discovered 39 bugs, 28 of which are new bugs and 23 of which have explicit security implications. Our empirical results also show that the PDF generator is a better tool than existing PDF fuzzers: it outperforms the fuzzing tools that can be applied to PDF software (using code coverage and the number of discovered bugs as metrics).

Condition based Ensemble Deep Learning and Machine Learning Classification Technique for Integrated Potential Fishing Zone Future Forecasting

Artificial Intelligence (AI) technologies have become a popular application in order to improve the sustainability of smart fisheries. Although the ultimate objective of AI applications is often described as sustainability, there is yet no proof as to how AI contributes to sustainable fisheries. The proper monitoring of the longitudinal delivery of different human impacts on activities such as fishing is a major concern today in aquatic conservation. The term "potential fishing zone" (PFZ) refers to an anticipated area of any given sea where a variety of fish may congregate for some time. The forecast is made based on factors including the sea surface temperature (SST) and the sea superficial chlorophyll attentiveness. Fishing advisories are a by-product of the identification procedure. Normalization and preliminary processing are applied to these unprocessed data. The gathered attributes, together with financial derivatives and geometric features, are then utilised to make projections about IPFZ's Technique are used to get the final determination (CECT). In this study, we offer a technique for identifying and mapping fishing activity. Experimentations are performed to validate the efficacy of the CECT method in comparison to machine learning (ML) and deep learning (DL) methods across a variety of measurable parameters. Results showed that CECT obtained 94% accuracy, while Convolutional neural network only managed 92% accuracy on 80% training data and 20% testing data.

Why New Media app during the Healthcare Crisis? WhatsApp Based Study

WhatsApp is one of the most popular mobile applications providing instant messaging and ease of access. It uses web-based systems to send and receive texts, calls, and videos and even offers video chat. However, during the current healthcare crisis, WhatsApp's role is comparatively more prominent and needs more consideration. In this context, we conducted this research to examine the factors motivating students to rely more on WhatsApp during the Covid-19 pandemic. Based on the Uses and Gratifications Approach, we adopted a cross-sectional design and gathered data from university students in Islamabad, Pakistan. Findings from n= 302 university-level students affirmed communication (p≥ .000), learning (p≥ .002), Teamwork (p≥ .041), and information sharing (p≥ .000) as the motivating factors behind increased WhatsApp usage (p≥ .011) during the healthcare crisis. Similarly, path analysis results also validated a strong correlation between the variables, further affirming the strength of the proposed conceptual framework. Thus, WhatsApp not only provided ease of communication during Covid-19 but also helped sustain educational activities. Here we recommend more studies to examine other different factors, especially personality factors that can affect one's WhatsApp usage.

A survey of collaborative decision-making: Bibliometrics, preliminaries, methodologies, applications and future directions

Complex and uncertain decision-making environment increasingly requires the collaborations of participants, the exchange and interaction of knowledge of different actors can effectively promote and improve the decision-making process. Collaborative decision-making (CDM), a prevailing decision-making method, is extensively studied by scholars, in which different actors can communicate mutually and then reach a common goal. Hence, in view of the advantages and usefulness of CDM, an overview is conducted for showing the state of the art of CDM, pointing out the current issues and indicating the future research directions. To achieve those goals, firstly, the bibliometric analysis is implemented to briefly review the previous research topics. Then, the definitions, comparisons, weights and consensus concerning CDM are recalled, which are followed by the summarization of methodologies used for solving CDM problems. Subsequently, the current research directions and popular application areas are highlighted. Finally, the challenges and potential research directions are concluded from five perspectives.

Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review

With the increasing popularity of the Internet of Things and massive Machine Type Communication technologies, the number of connected devices is rising. However, although enabling valuable effects to our lives, bandwidth and latency constraints challenge Cloud processing of their associated data amounts. A promising solution to these challenges is the combination of Edge and approximate computing techniques that allows for data processing nearer to the user. This article aims to survey the potential benefits of these paradigms’ intersection. We provide a state-of-the-art review of circuit-level and architecture-level hardware techniques and popular applications. We also outline essential future research directions.

A Design of an Indoor Delivery UAV

With the rapid development of UAV-related directions, intelligent UAVs have been applied to various industries of modern production and life. One particularly popular application is “delivery UAV.” By giving commands to the UAV, It can accomplish complex tasks such as target point flight and cargo delivery autonomously. On the one hand, human resources can be liberated; on the other hand, fully automated operations can greatly improve production efficiency. Our team used STM32 produced by Stmicroelectronics as the main control of the UAV, OpenMV from SingTown Technology, and K210 from Canaan Technology as the vision processor to design and develop a delivery quadrotor. The UAV uses an optical flow module to achieve hover and cruise and is equipped with a laser ranging module to assist the UAV in obtaining height. K210 and OpenMV are used for target detection, and the Hanging basket can be raised and lowered to simulate “delivery.” The UAV can realize automatic cruise, target-point “delivery,” target detection, obstacle avoidance, and many other functions.

Perceived Influence of Digital Competence on Knowledge Sharing Behaviour of College Librarians in the South-East and South-South Geo-Political Zones of Nigeria

Knowledge and the competence to share it in the digital age have become potent tools for the attainment of competitive advantage and sustainable development in organizations and institutions of learning. This awareness has necessitated the need to embark on a study with the main objective of determining how digital competence variables influence the knowledge sharing behaviour of librarians in college libraries in the South–East and South–South of Nigeria. The research was supported by the Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM). Survey research method was used to investigate a sample of 264 librarians. A validated and structured questionnaire with a correlation coefficient of 0.92 was used to collect data for the research through the census method. A response rate of 82% (217) was received and analysed. Findings reveal that librarians in college libraries possess the ability to use and manipulate a broad range of digital devices like smart phones, tablets, lap/desk tops; have a working knowledge of the World Wide Web (www); use social media platforms to maintain workplace collaboration; and use popular library application software packages. The study concludes that librarians in the institutions studied have shown significant skills in the use of ICT in their knowledge sharing behaviour and therefore should be sustained. The research recommends amongst others regular ICT training, workshops and conferences to enhance the knowledge sharing behaviour of the librarians and the creation of digital platforms and infrastructure for knowledge creation and sharing for professional and institutional ranking and visibility. Keywords: Digital Competence, Knowledge Sharing Behaviour, College Librarians, Nigeria DOI: 10.7176/IKM/13-3-03 Publication date: March 31 st 2023

A Deep Learning approach for fire object detection in Autonomous vehicles

In recent years, object detection has emerged as a crucial component of many popular consumer applications, including video surveillance and security systems, mobile text recognition, etc. The potential for autonomous vehicles (AVs) to increase driver satisfaction and decrease fatalities and injuries in traffic accidents has attracted a lot of interest in recent years. Object detection is critical to autonomous driving infrastructure. Autonomous automobiles need precise environmental interpretation to drive safely. Locating and identifying these things in real time is a significant challenge, but deep learning-based object detectors play a crucial role in this endeavour. In this paper, a prototype of the autonomous vehicle controlled by a microcontroller for fire object detection is proposed. Localization of the fire object in a picture using the deep learning model is performed using the live video feed from the camera installed on the prototype remote-operated car. The gadget may also pick up on specifics and alert the user. Experimental findings showed that the suggested prototype with deep learning architecture recognised and alerted devastating fires with high speed and accuracy in diverse weather conditions—sunny or overcast, day or night.

Software Correction of Speed Measurement Determined by Phone GNSS Modules in Applications for Runners

This paper presents the results of a study on software correction of speed measurements taken by GNSS receivers installed in cell phones and sports watches. Digital low-pass filters were used to compensate for fluctuations in measured speed and distance. Real data obtained from popular running applications for cell phones and smartwatches were used for simulations. Various measurement situations were analyzed, such as running at a constant speed or interval running. Taking a very high accuracy GNSS receiver as the reference equipment, the solution proposed in the article reduces the measurement error of the traveled distance by 70%. In the case of measuring speed in interval running, the error could be reduced by up to 80%. The low-cost implementation allows simple GNSS receivers to approach the quality of distance and speed estimation of very precise and expensive solutions.

Classifying Message Application Using Kotlin

Over the past few years, short message service (SMS) usage has significantly increased. This service is used to deliver text messages by billions of people. Service providers have launched a number of popular applications, including mobile banking, summons checkpoints, SMS chat, and others. This chapter explores the numerous SMS applications that are available to users and provides an outline of how this service is provided. We examine the causes of its success and the problems that need to be solved. We also look at upcoming trends and the difficulties that to improve this service, certain obstacles must be overcome. This chapter should help you understand how SMS applications work and what to expect from them going forwards given the improvements to current SMS and technological advancement. We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340 ms in the case where the dictionary size of Bob’s model includes all words (n 5200) and Alice’s SMS has at most m 160 unigrams. In the case with n 369 and m 8 (the average of a spam SMS in the database), our solution takes only 21 ms.

HPC Hardware Design Reliability Benchmarking With HDFIT

Chips pack ever more, ever smaller transistors. Fault rates increase in turn and become more concerning, particularly at the scale of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">High-Performance Computing</i> (HPC) systems: on one hand, hardware fault protection is costly - more than 10% silicon area for floating-point units; on the other, HPC users expect correct application output after the anticipated time of computation, but workloads are seldom bit-reproducible and tolerances in output are allowed for. Benign hardware faults causing errors within these tolerances are therefore acceptable: however, with abstract reliability targets such as ’undetected failures per time,’ current HPC system design does not allow for pursuing trade-offs between reliability and performance with respect to faults. To address the above, we propose a user-centric reliability benchmark to specify HPC system reliability targets, allowing for better performance optimizations in hardware design, while meeting HPC user expectations. Our open-source <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hardware Design Fault Injection Toolkit</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HDFIT</i> ) enables - for the first time - end-to-end hardware design reliability experiments: from netlist-level fault injection to application output error. In a proof of concept we present an HPC <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general matrix multiply</i> (GEMM) reliability study, targeting a series of popular applications, and using HDFIT to benchmark an open-source GEMM accelerator.

Design of RCC Well Using StaadPro Software

Abstract: Water is human basic needs for daily life. Sufficient water distribution depends on design of a water tank in certain area. Many new ideas and innovation have been made for the storage of water and other liquid materials in different forms and fashions. Water retention structures, often known as water storage tanks, are an essential component of any distribution system. During periods of low demand, water is pumped into the storage tank, and during periods of high demand, water is pushed out of the storage tank and into the distribution system. STAADpro is a popular structural analysis application known for analysis, diverse applications of use, interoperability, and time-saving capabilities. STAADpro helps structural engineers perform 3D structural analysis and design for both steel and concrete structures.

ANALISIS KEBUTUHAN BACKPACKER TERHADAP MOBILE APPLICATION EFEKTIF DAN EFISIEN

Labuan Bajo as a super priority, world class and super premium destination, has tourism differentiation compared to other tourist destinations, so that it attracts foreign tourists to visit. However, for backpacker tourists, seeking integrated information related to traveling to Labuan Bajo is one of the obstacles in the pre-trip process. Backpackers have to compare the cheapest prices from one application to another, so they experience difficulties in organizing trips manually. This resulted in the pre-trip process being inefficient and ineffective. Based on this description, this study aims to identify the need for backpacker travel guides to facilitate the pre-trip process effectively and efficiently. The research was conducted using a qualitative approach through distributing questionnaires, in-depth interviews, comparative studies, and literature studies. The results of filling out the questionnaire by the respondents were then analyzed using a quantitative approach. The results of the analysis state that Indonesia has many popular travel applications, but none yet accommodate backpacker problems, so a special backpacker travel application is needed to make the travel process efficient and effective.Keywords: mobile travel application, backpacker, Labuan Bajo, organise effective and efficient travel

SPOKEN-DIGIT CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK

Audio classification has been one of the most popular applications of Artificial Neural Networks. This process is at the center of modern AI technology, such as virtual assistants, automatic speech recognition, and text-to-speech applications. There have been studies about spoken digit classification and its applications. However, to the best of the author's knowledge, very few works focusing on English spoken digit recognition that implemented ANN classification have been done. In this study, the authors utilized the Mel-Frequency Cepstral Coefficients (MFCC) features of the audio recording and Artificial Neural Network (ANN) as the classifier to recognize the spoken digit by the speaker. The Audio MNIST dataset was used as training and test data while the Free-Spoken Digit Dataset was used as additional validation data. The model showed an F-1 score of 99.56% accuracy for the test data and an F1 score of 81.92% accuracy for the validation data.

Assessing the accuracy of free automated plant identification applications

AbstractWidely available and inexpensive mobile phone applications offer users, whether professional ecologists or interested amateurs, the potential for simple and rapid automated identification of species, without the need to use field guides and identification keys. The increasing accuracy of machine learning is well established, but it is currently unclear if, and under what circumstances, free‐to‐use mobile phone applications are accurate for identifying plants to species level in real‐world field conditions.We test five popular and free identification applications for plants using 857 professionally identified images of 277 species from 204 genera. Across all applications, 85% of images were identified correctly in the top five suggestions, and 69% were correct with the first suggestion. Plant type (woody, forbs, grasses, rushes/sedges, ferns/horsetails) was a significant determinant of identification performance for each application. For some applications, image saliency was also important; exposure and focus were not significant.Applications performed well, with at least one of the three best‐performing applications identifying 96% of images correctly as their first suggestion. We conclude that, subject to some caveats, free phone‐based plant identification applications are valid and useful tools for those wanting rapid identification and for anyone wanting to engage with the natural world.Read the freePlain Language Summaryfor this article on the Journal blog.

Recycling polymer composite granulate/regrind using big area additive manufacturing

Carbon fiber filled acrylonitrile butadiene styrene (CF-ABS) is an emerging 3-D printable, recyclable thermoplastic composite. A popular application for CF-ABS is printed molds using large scale additive manufacturing (AM) processes produced for a variety of composites end-users. Other applications have not been fully realized as CF-ABS currently has a higher cost than other polymer composite feedstocks. The cost of CF-ABS could be greatly reduced if recycled CF-ABS material was accepted and utilized industrially, which would further expand its application space. The impacts of mechanical recycling on the properties and performance of CF-ABS after AM or other processes are not yet fully understood. As such, most AM CF-ABS is discarded as waste after use. In this work, AM CF-ABS molds used for concrete castings were recycled using an industrial shredder/granulator system. The granulate was then used as feedstock for large area AM to print test specimens. The elastic moduli in the printing direction and transverse to printing direction after mechanical recycling were found to decrease by 25% and 10%, respectively, most likely caused by reductions in the fiber content and polymer molecular weight. It was found that a significant portion of carbon fiber is lost during mechanical recycling through dust generation, while the thermomechanical extrusion during printing caused the polymer degradation. Although the mechanical performance of CF-ABS was reduced after recycling, the recycled feedstock retained properties for reuse as printed molds using large area AM or another suitable application.

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Biomedical text summarization (BTS) is proving to be an emerging area of work and research with the need for sustainable healthcare applications such as evidence-based medicine practice (EBM) and telemedicine which help effectively support healthcare needs of the society. However, with the rapid growth in the biomedical literature and the diversities in its structure and resources, it is becoming challenging to carry out effective text summarization for better insights. The goal of this work is to conduct a comprehensive systematic literature review of significant and high-impact literary work in BTS with a deep understanding of its major artifacts such as databases, semantic similarity measures, and semantic enrichment approaches. In the systematic literature review conducted, we applied search filters to find high-impact literature in the biomedical text summarization domain from IEEE, SCOPUS, Elsevier, EBSCO, and PubMed databases. The systematic literature review (SLR) yielded 81 works; those were analyzed for qualitative study. The in-depth study of the literature shows the relevance and efficacy of the deep learning (DL) approach, context-aware feature extraction techniques, and their relevance in BTS. Biomedical question answering (BQA) system is one of the most popular applications of text summarizations for building self-sufficient healthcare systems and are pointing to future research directions. The review culminates in realization of a proposed framework for the BQA system MEDIQA with design of better heuristics for content screening, document screening, and relevance ranking. The presented framework provides an evidence-based biomedical question answering model and text summarizer that can lead to real-time evidence-based clinical support system to healthcare practitioners.

Is TikTok a high-quality source of information on thyroid cancer?

Increased Internet users tend to use online video platforms as a source of medical information. There are many thyroid cancer-related videos on TikTok, the most popular short-video application in China. However, quality evaluation of these videos remains absent. The aim of this study is to evaluate the quality of thyroid cancer-related videos on TikTok. A search was performed on TikTok with the keyword "thyroid cancer". The top 100 videos in the search results were selected. Characteristics of videos including video length, duration on TikTok, thumbs up, thumbs up/day, comments, comments/day, favorites, favorites/day, reposts, and reposts/day were collected. The Patient Education Materials Assessment Tool (PEMAT), modified DISCERN (mDISCERN), Video Information and Quality Index (VIQI), and Global Quality Score (GQS) were applied to assess the quality of videos. Among the 100 videos in this study, most were uploaded by surgical physicians (n = 50), and the most common topic was anatomy/basic facts (n = 21). The mean scores of the PEMAT, PEMAT-U, and PEMAT-A were 71.18 ± 16.56, 82.23 ± 15.00, and 44.00 ± 37.57, respectively. Among the five questions of the mDISCERN tool, 71, 90, 82, 9, and 28 videos were evaluated as "yes", respectively. The mean scores of mDISCERN, VIQI, and GQS were 2.80 ± 1.03, 14.87 ± 2.27, and 3.72 ± 1.00, respectively. The characteristics and quality of videos are significantly different based on the uploader and content. The quality of thyroid cancer information on TikTok is unsatisfactory. There is a need for high-quality thyroid cancer-related information on short-video platforms for patient education.