Journal of Science & Technology (JST)
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Use of Mathematics in Stock Market
Get PDFStock market plays a key role in economical and social organization of a country. Stock market forecasting is highly demanding and most challenging task for investors, professional analyst and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. Prediction of stock market is a crucial task and prominent research area in financial domain as investing in stock market involves higher risk. However with the development of computational intelligent methods it is possible to reduce most of the risk. In this survey paper, our focus is on application of computational intelligent approaches such as artificial neural network, fuzzy logic, genetic algorithms and other evolutionary techniques for stock market forecasting. This paper presents an up-to-date survey of existing literature on stock market forecasting based on computational intelligent methods. The key result is that the probability distribution function of market timing returns is asymmetric, that the highest probability outcome for market timing is a below median return. Put another way, simple math says market timing is more likely to lose than to win—even before accounting for costs. The median of the market timing return probability distribution can be directly calculated as a weighted average of the returns of the model assets with the weights given by the fraction of time each asset has a higher return than the other. For the time period of the data the median return was close to, but not identical with, the return of a static 60:40 stock: bond portfolio. The according to six main point of view: (1) the stock market analyzed and the related dataset, (2) the type of input variables investigated, (3) the pre-processing techniques used, (4) the feature selection techniques to choose effective variables, (5) the forecasting models to deal with the stock price forecasting problem and (6) performance metrics utilized to evaluate the models. The major contribution of this work is to provide the researcher and financial analyst a systematic approach for development of intelligent methodology to
forecast stock market. This paper also presents the outlines of proposed work with the aim to enhance the performance of existing techniques
On Making Cloud More Secure and Trustworthy
Get PDFCloud computing has grown in popularity as a result of recent advancements in technology. Academics and businesses alike place a high value on computing because of the abundance of data and the emergence of new AI paradigms. The problems of scalability and availability were quickly solved by cloud computing. However difficult it has been, cloud service providers have managed to provide low-cost options. Although servers may delete rarely viewed files to save space, certain cloud firms protect data integrity. However, they can't control server and network difficulties, thus they can't guarantee data availability. Customer data integrity, availability and exposure are feared by customers. Amazon S3 and Amazon EC2, Gmail email deletion, and the Sidekick cloud disaster validated customers' concerns. – Amazon.com A customer's primary concern is the safety of their personal information. Trust and security are handled by cloud companies. Proof and data are difficult to obtain. This paper describes the security principles and security measures of the cloud environments and presents some of the security assessment criteria based upon the deployment aspects of the cloud based application services in the autonomous environment
Synthesis, Characterization and Antimicrobial Evaluation of a new Macrocyclic Ligand and Its Co (II), Ni (II,) Cu (II) and Zn (II)Complexes
Get PDF:Coordination complexes derived from macrocyclic ligand metal complexes of Co (II), Ni (II,) Cu (II) and Zn (II) have been synthesized. The Schiff base ligand was synthesized by condensation of 1-(5-bromo -2- hydroxyphenyl) -3- (furan -2-yl) propane -1,3-dione with o-phenylenediamine. The metal complexes ML (H2O)nare reported and characterised by conductivity, UV- visible, FTIR, H1NMR, TGA-DTA and XRD analysis. All the complexes were found to be non-electrolytic in nature. The ligand acts as a hexadentate and coordinates through four nitrogen atoms of azomethine groups. The antibacterial and antifungal activities of the ligand and its metal complexes, has been screened in vitro against Gentamycin, Ampicillin, Chloramphenicol, Ciprofloxacin, Norfloxacin, Nystatin and Griseofulvin used as standard drug. All these complexes show higher biological activities than standard drugs
A benchmark study of machine learning models for online fake news detection
Get PDFThe widespread circulation of false information via online platforms is a growing cause for alarm because of the havoc it may wreak. Several machine learning strategies have been proposed for spotting hoaxes. However, the vast majority of them concentrated on a certain category of news (like politics), raising the issue of dataset bias in the used models. Here, we provide the results of a benchmark study that compares three datasets to determine which machine learning technique performs best. To the best of our knowledge, we are the first to investigate and evaluate the performance of many state-of-the- art pre-trained language models for false news detection, alongside the performance of classical and deep learning models. When it comes to detecting false news, we discover that BERT and other comparable pre-trained models perform the best, even when working with a tiny dataset. Because of this, these models are a much superior choice for languages with few electronic contents (i.e., training data). Additionally, we analyzed the models' efficacy, article topics, and article lengths, and shared our findings and insights. We hope that our benchmark study will encourage additional investigation in the field of false news identification and enable news sites and blogs choose the most effective approach.
 
A SUMMARY OF CLOUD COMPUTING FOR DEVELOPING THE PROCESS OF E-LEARNING
Get PDFOnline communication platforms are used to support e-learning, a kind of virtualized computing, and remote learning as a tool in the teaching-learning process. In the last two years, e-learning platforms have grown significantly. When the learning process is digitized, data mining for education information processing leverages information produced from internet databases to improve the educational learning paradigm for educational purposes. A potential platform for enabling e-learning systems is cloud computing. By offering a scalable solution for long-term transformation of computer resource use, it may be automatically changed. When engaging with large e-learning datasets, it is also easier to employ data mining methods in a distributed setting. The research offers an overview of cloud computing's present condition as well as illustrations of infrastructure that has been specifically created for a system like this. It also talks about e-learning techniques and cloud computing demonstrations.
 
Electronic Visits in Primary Care: Modeling, Analysis, and Scheduling Policies
Get PDFPakistan is endowed with vast coal reserves, primarily in Sindh, totaling 184.623 billion tons. Our country's market has been rapidly expanding. As we all know, accidents are becoming more often in mines due to a shortage of competent workers, miners' safety cannot be guaranteed, and coal manipulation is impossible. Men who are currently employed in coal mining must confront environmental constraints. Temperature, carbon dioxide, and methane are all threats to them. As a result, we must provide security for the men and women who are now employed in coal mining. The goal of this study is to provide a solution to mining through communication and security monitoring. Coal mining has a unique function in the modern world; it has the potential to save the lives of coal miners by creating particular gadgets that can be extremely beneficial to the industry's workers. People working in underground coal mines must utilize several characteristics such as smart helmets with sensors such as removers, collision detectors, gas detectors, and the helmet. Here, we must organize our circuit within the Smart Helmet in order to provide security to the man who is now employed in coal mining. Coal mining remains a hazardous activity that can have a variety of negative environmental implications, such as the discharge of hazardous gases during mining operations. The helmet is equipped with a Wi-Fi-based monitoring system that communicates with all of the trackers via Wi-Fi networks to provide data. As a result, the Smart Helmet indication takes the required precautions to avoid any potentially dangerous situations and sends out an alert through buzzer and Cloud Based Monitoring. The data is collected using ESP32 Arduino created tracker circuitry. It aids in the mapping of worker locations
Cluster Computing for Web-Scale Data Processing
Get PDFPower efficiency and real-time processing capability are two major issues in today‘s mobile video applications. We proposed a novel Motion Estimation (ME) engine for power-efficient real-time MPEG- 4 video coding based on our previously proposed content-based ME al- gorithm [8], [13]. By adopting Full Search (FS) and Three Step Search (TSS) alternatively according to the nature of video contents, this algo- rithm keeps the visual quality very close to that of FS with only 3% of its computational power. We designed a flexible Block Matching (BM) Unit with 16-PE SIMD data path so that the adaptive ME can be performed at a much lower clock frequency and hardware cost as compared with previousFS based work. To reduce the energy cost caused by excessive external memory access, on-chip SRAM is also utilized and optimized for paral- lel processing in the BM Unit. The ME engine is fabricated with TSMC
0.18 µm technology. When processing QCIF (15 fps) video, the estimated power is 2.88 [email protected] MHz (supply voltage: 1.62 V). It is believed to bea favorable contribution to the video encoder LSI design for mobile appli-cations.
 
Influence of heat treatments in H2 and Ar on the E1 center in β-Ga2O3
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We looked at the possibility of the defect level E1 in heat treated n-type bulk β-Ga2O3 in hydrogen
(H2) and argon (Ar) gases. The presence of hydrogen (H) doped β-Ga2O3 during H2 annealing at 900 °C has been verified via the use of Fourier transform-infrared spectroscopy. Studies using deep-level transient spectroscopy reveal that the addition of H increases the concentration of the E1 level, which contradicts the results seen in samples heat-treated in an Ar flow. Regardless of the presence or absence of a reverse-bias voltage, the E1 level does not change after heat treatments at 650 K. Possible causes of E1's flaw may be investigated using the hybrid-functional computations. We find three types of hydrogen-containing defect complexes that are consistent with E1 in terms of their charge-state transition levels. These include arrangements of singly hydrogenated Ga-O vacancies, shallow Ga-substitutional hydrogen-passivated donor impurities, and substitutional hydrogen at one of the triple coordinated O sites. The observed thermal stability of E1 is compatible with only the second kind of hydrogen binding energies.
 
Hybrid Diagonal Transposition Cipher Model for Securing Data in Software Defined Networks
Get PDFData is an important asset for every organization and hence this article is proposed to secure data from common breaches in software defined network. In this article, hybrid cipher model is proposed to safeguard the communication of data transmitted among the layers in software defined networks. The logic of hybrid cipher model is incorporated in software defined controller which encrypts open flow request and response messages. Software Defined Network is adapted for implementing hybrid cipher model as the network provides customizable platform and act as a unmanned security featured software controller. The proposed Hybrid Diagonal Transposition algorithm is incorporated with software defined wireless sensing node for encrypting user’s data. Hence the unmanned security featured wireless sensing node is situation-aware, it detects malicious traffic flows and encrypts user’s data. Hybrid Diagonal Transposition algorithm prevents data breaches in Software Defined Networks. Results are interpreted for various network and sensor metrics such as routing hops, participating node temperature, battery voltage, humidity, lights, received packets per node, number of network hops, power consumption, radio duty cycle, temperature of sensors, beacon interval, network hops, routing metric and the same work will be extended in future for comparative results
A Qualitative Investigation of African Americans’ Decision to Pursue Computing Science Degrees: Implications for Cultivating Career Choice and Aspiration
Get PDFPearson (2002) claims that underrepresented groups are underrepresented in STEM (science, technology, engineering, and mathematics) fields. African Americans are one of these under-represented populations. For the sake of the American economy, it is crucial to increase the number of people studying computer science and other STEM subjects (J. F. L. Jackson, Charleston, George, and Gilbert, in press; Moore, 2006; Pearson, 2002). The author sheds light on the lives of African American students pursuing degrees in computing at the undergraduate, graduate, and doctoral levels via the use of the grounded theory methodology. There are implications for encouraging participants to pursue computer science degrees and for encouraging them to pursue their professional goals in general that stem from the findings of this research. The research yielded a heuristic approach for increasing access to computers