Kinds Of Problems Research Articles

Peran Pengalaman Kerja Dalam Personal Performance Planning: Sebuah Tinjauan Teoritis Dan Empiris

Work experience is very necessary in improving employee performance in the company. Employee work experience shows the level of mastery of skills. Employees who have had more years of service will have various kinds of work experience in solving various kinds of problems within the company. It is expected that employees will have good quality resources in improving work performance. The purpose of this study is to describe the role of work experience in improving employee performance scientifically based on the results of the existing literature review. If employees have a lot of work experience, then the abilities possessed by employees will be in accordance with the work given, so that employees have high performance. This employee performance refers to the results of work in completing the work. This role has a positive nature which means the more work experience an employee has, the employee's performance will increase.

Psychology Analysis of Main Character in the Novel Gitanjali by Febrialdi R

This study aims to describe the psychology of main characters in the novel Gitanjali by Febrialdi R. In detail, the purpose of this study is to describe the psyche of the main character when facing various kinds of problems in his life. This research uses the library method. The subject of this research is a novel entitled Gitanjali by Febrialdi R which was published by mediakita publisher in 2018. This study focuses on problems related to the psychological conflict experienced by the main character. The results showed that: First, the main character of the novel Gitanjali received injustice. An accident that happened when he worked at a restaurant made him dismissed from the restaurant because of his disability. Second, the main character experiences despair in his life and love relationship. He feels hopeless about his love relationship which is not going well due to the demanding parents of his partner. It made him insecure, moreover his partner was a lecturer while he was just an ordinary man. Third, the main character lives alone because since childhood he never knew his biological parents and only lived in an orphanage. Fourth, the main character is depressed due to the problems that come his way.

Predictive Analysis of Student Stress Level Using ML

College students are suffering from many mental health problems including mental stress, somatization, obsessive, interpersonal sensitivity, depression, anxiety, hostility, fear, paranoia and psychosis, which can bring a lot of negative effects to them. Obviously, the mental health problems of college students not only directly affect their own growth, but also affect the stability of the campus. Most colleges also pay more and more attention to students' psychological crisis monitoring and prevention. All Colleges simply analyze whether students have mental health problems or what kind of problems they have. It cannot find hidden relationships in the psychological data. We need a system to handle the student mental health problems, here we are mainly concentrating on student stress prediction. There are so many factors related to stress such as work load, assignments, family issues, friends issues, attendance, teaching etc. Machine learning is a subject to predict future based on the past data. Using machine learning techniques we predict student stress level and also proposed system will give suggestions based on the stress levels of student.

MicroROM: An efficient and accurate reduced order method to solve many-query problems in micro-motility

In the study of micro-swimmers, both artificial and biological ones, many-query problems arise naturally. Even with the use of advanced high performance computing (HPC), it is not possible to solve this kind of problems in an acceptable amount of time. Various approximations of the Stokes equation have been considered in the past to ease such computational efforts but they introduce non-negligible errors that can easily make the solution of the problem inaccurate and unreliable. Reduced order modeling solves this issue by taking advantage of a proper subdivision between a computationally expensive offline phase and a fast and efficient online stage. This work presents the coupling of Boundary Element Method (BEM) and Reduced Basis (RB) Reduced Order Modeling (ROM) in two models of practical interest, obtaining accurate and reliable solutions to different many-query problems. Comparisons of standard reduced order modeling approaches in different simulation settings and a comparison to typical approximations to Stokes equations are also shown. Different couplings between a solver based on a HPC boundary element method for micro-motility problems and reduced order models are presented in detail. The methodology is tested on two different models: a robotic-bacterium-like and an Eukaryotic-like swimmer, and in each case two resolution strategies for the swimming problem, the split and monolithic one, are used as starting points for the ROM. An efficient and accurate reconstruction of the performance of interest is achieved in both cases proving the effectiveness of our strategy.

Analysis of Machine Learning Approaches’ Performance in Prediction Problems with Human Activity Patterns

Prediction problems in timed datasets related to human activities are especially difficult to solve, because of the specific characteristics and the scarce number of predictive (input) variables available to tackle these problems. In this paper, we try to find out whether Machine Learning (ML) approaches can be successfully applied to these problems. We deal with timed datasets with human activity patterns, in which the input variables are exclusively related to the day or type of day when the prediction is carried out and, usually, to the meteorology of those days. These problems with a marked human activity pattern frequently appear in mobility and traffic-related problems, delivery prediction (packets, food), and many other activities, usually in cities. We evaluate the performance in these problems of different ML methods such as artificial neural networks (multi-layer perceptrons, extreme learning machines) and support vector regression algorithms, together with an Analogue-type (KNN) approach, which serves as a baseline algorithm and provides information about when it is expected that ML approaches will fail, by looking for similar situations in the past. The considered ML algorithms are evaluated in four real prediction problems with human activity patterns, such as school absences, bike-sharing demand, parking occupation, and packets delivered in a post office. The results obtained show the good performance of the ML algorithms, revealing that they can deal with scarce information in all the problems considered. The results obtained have also revealed the importance of including meteorology as the input variables, showing that meteorology is frequently behind demand peaks or valleys in this kind of problem. Finally, we show that having a number of similar situations in the past (training set) prevents ML algorithms from making important mistakes in the prediction obtained.

Monitoring of Cardiovascular Diseases: An Analysis of the Mobile Applications Available in the Google Play Store

Cardiovascular diseases have always been here, but there has been an increase in their numbers over time. Even though there are in the digital world a few applications to help with this kind of problem, there are not enough to fulfill the needs of the patients. This study reviews mobile applications that allow patients to monitor and report cardiovascular diseases. It presents a review of 14 mobile applications that were free to download in Portugal and classified and compared according to their characteristics. The selection criteria combined the following keywords: “patient”, “cardiac/or heart”, “report”, and (“tracking” or “monitoring”). Based on the analysis, we point out the errors of the applications and present some solutions. To finish, we investigated how mobile applications can help patients track and self-report cardiovascular diseases.

A statistical learning perspective on the inversion of NMR relaxation data

T 2 distribution is a powerful tool in the low-field nuclear magnetic resonance technique. The T2 distribution obtained from time-domain data involves an ill-posed inverse Laplace transformation. Tikhonov regularization with an L2 penalty term is most commonly used in this kind of problem, and the discrepancy principle, generalized cross-validation, L-curve, and S-curve methods are widely used in the selection of the regularization parameter. However, these selection approaches require prior knowledge, such as an accurate estimation of the threshold level of noise or setting a default value. In this paper, we propose a new method—the stability-enhanced k-fold cross-validation (SECV) approach—to perform a robust automatic search for the regularization parameter from a statistical learning perspective. In addition to considering test set residuals, additional terms—the Pearson’s correlation coefficients of the solutions of the disjoint subsets—are put forward to enhance the stability of the solution and make a trade-off between its imitative effect and interpretability. A bimodal T2 distribution model was constructed, and abundant echo trains with different noise levels were generated for the validation of the proposed method. The relative error of the estimates is used as a measure to evaluate the performance. The inversion results from the SECV method were compared with the solutions from the conventional methods, and the results showed that the proposed method is robust without manual intervention and suitable for both low- and high-signal-to-noise ratio data. Finally, mercury injection and nuclear magnetic resonance experiments were carried out on rock core samples to verify the correctness of our method.

Expert Opinion Fusion Framework Using Subjective Logic for Fault Diagnosis.

Fault diagnosis plays a critical role in maintaining and troubleshooting engineered systems. Various diagnosis models, such as Bayesian networks (BNs), have been proposed to deal with this kind of problem in the past. However, the diagnosis results may not be reliable if second-order uncertainty is involved. This article proposes a hierarchical system diagnosis fusion framework that considers the uncertainty based on a belief model, called subjective logic (SL), which explicitly deals with uncertainty representing a lack of evidence. The proposed system diagnosis fusion framework consists of three steps: 1) individual subjective BNs (SBNs) are designed to represent the knowledge architectures of individual experts; 2) experts are clustered as expert groups according to their similarity; and 3) after inferring expert opinions from respective SBNs, the one opinion fusion method was used to combine all opinions to reach a consensus based on the aggregated opinion for system diagnosis. Via extensive simulation experiments, we show that the proposed fusion framework, consisting of two operators, outperforms the state-of-the-art fusion operator counterparts and has stable performance under various scenarios. Our proposed fusion framework is promising for advancing state-of-the-art fault diagnosis of complex engineered systems.

Ethereum Blockchain Wallets

Abstract: Blockchain technology is an evolving technology which is revolutionizing the IT industry by providing better security and efficiency. This technology can help to solve different kinds of problems in the industrial sphere, such as trust, transparency, security and reliability of data processing. I theory, the use of Blockchain technology shows great and positive results. Ethereum is the most widely used blockchain platform because of its unlimited block size. Many sophisticated problems with smart contracts can be solved with Ethereum and the removal of any third party organization which may interfere in transactions and it is easier to implement compared to other blockchain technologies. In this paper the benefits and drawbacks of wallet based on Ethereum blockchain are analyzed. Many already implemented wallets on Blockchain technology were studied, as well as affected success or problems factors during the implementations. This paper aims to analyze conveniences and difficulties related to the Blockchain integration to a wallet and implementation in the different fields of modern industry

МЕТОД И АЛГОРИТМ ПЛАНИРОВАНИЯ ОПЕРАЦИЙ НА ОСНОВЕ МОДЕЛИ НЕЧЕТКОГО КОНЕЧНОГО АВТОМАТА

In this paper the planning and scheduling problem as an important optimization problem inmany transportation and robotic applications is discussed. To solve planning problems, the mainapproaches are based on optimization methods, sampling-based methods, and usually such kindsof problems are NP-hard and high dimensional. In this work, the method for planning and schedulingbased on the fuzzy finite state machine model is developed. Fuzzy graph presentation of thescheduling problem and operation planning is given. The paper presents two approaches to theformulation of the planning problem with limited resources and temporal variables: state-oriented(with transitions between states), temporal ordering-oriented (on a time scale). Temporal modelingfor planning problems implies a qualitative approach to managing the distribution of operationsor topological ordering, as well as a quantitative approach to handling imprecise durationsrelationships between operations in multiple parameters. The concepts of fuzzy intervals and fuzzyrelations are introduced for planning operations on a graph. A planning algorithm based on thetheory of automata and temporal modeling under uncertainty has been developed. Using this formalism,a path planning problem is solved by successively altering a state using various operationsuntil a solution is found. The idea of temporal-ordered partial schedule associated with theplanning state of the system is discussed. A model of a finite automaton for a planning system underconditions of uncertainty is proposed. A method and algorithm for scheduling operationsbased on a non-deterministic finite automaton and an enumeration scheme have been developed.The non-deterministic computation for a scheduling problem is a decision tree whose root correspondsto the beginning of the scheduling process, and each branch point in the tree correspondsto a computation point at which the machine has multiple choices. And the finite state machinemodel (automata) for the planning system under uncertainty is suggested.

PROBLEMATIKA GURU DI SEKOLAH

Abstrak P This study aims to determine the understanding of teacher problems, the kinds of problems experienced by teachers in schools and how to minimize these problems. The research method that the author uses is a qualitative approach with the type of library research (library research) with research data sources coming from various literatures, such as journals, books, personal documents, newspapers and so on. The results of the study can be concluded as follows: 1) Teacher problems are problems or problems experienced by teachers both when carrying out their duties in educating, teaching, and guiding students while at school. 2) Teacher problems are generally divided into two groups, namely internal problems that come from within the teacher and external problems that come from outside the teacher's personality. 3) The author provides several ways to minimize teacher problems that can be applied in schools, namely: Emphasizing Teacher Professionalism, Teacher Motivation and Implementing Continuing Professional Programs (PKB).

DAMPAK PANDEMI COVID-19 BAGI PEMASARAN UMKM SYARIAH DI INDONESIA

The existence of Covid-19 has presented various kinds of problems, including problems that have occurred in marketing in Indonesia. The Covid-19 pandemic may be viewed by some business actors as a disaster, but if we are smart in managing or managing a business by looking at the situation and by understanding changes in consumer behavior, we will be able to boost sales even in the midst of this pandemic. The impact that occurred on offline sellers was that on average, micro, small and medium enterprises felt a decline in turnover during the Covid-19 presence. however, several micro, small and medium enterprises remained stable and experienced increased sales due to these increased efforts. Adjusting products and developing several strategies to survive, online sales are not much different from offline sales, in online marketing there has also been a decline and increase,E-commerce has played a role in the Indonesian economy during the Covid-19 pandemic for businesses, consumers and the government. . For business actors, e-commerce is a solution to overcome the impact of Covid-19 to increase business scale because many investors are investing in this field. The reason people choose to buy in online sales is because it is safer and more effective when making online transactions. The reason is because it is done without physical contact and continues to carry out health protocols.

Email Spam Filtering using Machine Learning

In todays world, all activities depend upon the internet. In that Receiving Spam email send messages is a major problem. Many times, this kind of mail contains viruses and hacking links and they affect our system. For solving this kind of problem, we need some method that can filter spam mails and non spam emails. In this paper, we presented one machine learning method that filters spam and non-spam emails. Our algorithm generates the dictionary and features vector and trains them with a machine learning algorithm. Email is one such communication medium that comes to mind when we think of secure communication. As the popularity of email increases, the number of unsolicited data has also increased rapidly. A lot of unwanted stacks of emails called as Spam has created a need for further development Nowadays Machine learning methods have been able to detect and filter out spam emails. The purpose of this current project is to receive a spam email in the morning or effectively using the Multinomial Naïve Bayes method. Naïve approach is a machine-readable algorithm used to classify sample email as spam or not. This filter can be used by other email service providers as fully functional spam filters.

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

With the development of financial globalization and financial market volatility, credit risk has become more prominent and serious, and how to establish an effective enterprise credit evaluation system and bank credit risk evaluation model, provide scientific quantitative decision-making basis for bank decision-making, reduce non-performing loans, and improve the quality of credit assets is a common research topic faced by domestic banks. At present, domestic banks have not been effective to establish risk prevention as the core of credit culture and long-term mechanism, the existence of nonperforming loans is still not fully resolved, new risks continue to appear, and there is a lack of a perfect and effective credit risk evaluation system. With the development of the Internet and financial institutions and the fusion, banks and financial institutions drastically increase the recorded data, and this provides a good prerequisite for the application of intelligent algorithms. In view of the shortcomings of BP neural network in the establishment of credit risk assessment model, such as poor promotion ability and long prediction time, and considering that support vector machine (SVM) can deal with some multi-classification problems, this paper introduces SVM method into the field of bank credit risk assessment and establishes an optimization model of credit risk assessment. This paper discusses the structure and algorithm principle of SVM classification method and proposes an integrated SVM based on fuzzy integral to solve this kind of problem. The results show that the algorithm can effectively improve the prediction accuracy, solve the problem of high computation cost, reduce the occupied memory space, improve the operation efficiency, shorten the training time, and provide a more reliable basis for the rapid and effective evaluation of bank credit risk. On the one hand, the research results expand the application of artificial intelligence technology in the field of economic research; the evaluation model can continuously and accurately measure credit risk is obtained, which provides the necessary basis for upgrading and optimizing credit decision-making, so it has high theoretical value and practical value.

The problems democracy can solve

ABSTRACT This article argues that current attempts to demonstrate the plausibility of democratic procedures’ tendency to produce good/wise/better outcomes at least partially fail. The examples given, while suggestive of a power in group decision-making, rarely reference anything resembling an actual political problem. The reason the examples do not do the work purveyors of them think they do, is that political problems seem much more complicated than the examples suggest. The author goes on to contend that a more productive strategy is to embrace the complexity of political problems and think through which dimensions of complexity invite democratic and epistemic opportunities. As such, the author closes by suggesting there are three significant dimensions of political problems, and only in some cases do those problems lend themselves to a democratic procedure producing a wiser/better outcome. Epistemic reliability therefore depends on the kind of problem a democratic procedure is attempting to solve.

METODE BAYESIAN PADA SISTEM ANTREAN PELAYANAN MENGGUNAKAN GUI R (Studi Kasus: Antrean Pelayanan di Kantor Dinas Kependudukan dan Pencatatan Sipil Kota Semarang)

The increase population of Semarang City has given many kinds of problem from births, deaths, marriages and other important events. The change of population identity data causes the number of visitors to the Semarang City Dispendukcapil to increase so that the service system becomes busy. The study aims to determine whether the service system in the Dispendukcapil is good or not. This can be known by determining the distribution of arrival patterns and service patterns to obtain a queuing system model and system performance measures. In this study, the distribution of arrival patterns and service patterns is determined by finding the posterior distribution using the Bayesian method. The Bayesian method was chosen because it is able to combine the distribution of the sample in the current study with previous information for the same case. Posterior distribution can be obtained if it has elements, namely prior distribution and likelihood function. The distribution of arrival patterns and service patterns obtained from prior information, follows the Discrete Uniform and Log-Normal distribution. Based on the calculation and analysis of the posterior distribution, the service system model of the Dispendukcapil Semarang City is obtained, namely for the Customer Service counter, and for the legalization counter and the population document service counter with a good service system.Keywords:Population, Dispendukcapil Semarang City, queue, Bayesian, prior distribution, posterior distribution, queuing system model, Beta, Gamma, Inverse Gamma.

Swarm of Honey Bees for Association Rule Mining Using CUDA

Association Rule mining (ARM) is well studied and famous optimization problem which finds useful rules from given transactional databases. Many algorithms already proposed in literature which shows their efficiency when dealing with different sizes of datasets. Unfortunately, their efficiency is not enough for handling large scale datasets. In this case, Bees swarm optimization algorithm for association rule mining is more efficient. These kinds of problems need more powerful processors and are time expensive. For such issues solution can be provided by graphics processing units (GPUs) and are massively multithreaded processors. In this case GPUs can be used to increase speed of the computation. Bees swarm optimization algorithm for association rule mining can be designed using GPUs in multithreaded environment which will efficient for given datasets.

Privacy Protection Technology Based on Machine Learning and Intelligent Data Recognition

The machine learning algorithm is gradually being applied to various fields and has become the core technology to achieve artificial intelligence. The success of machine learning cannot be achieved without the support of large amounts of data and computing power, which are usually collected through crowdsourcing and learned online. The data collected for machine learning training often contains some personal and sensitive information, including personal mobile phone numbers, ID numbers, and medical information. How to protect these private data at low cost and efficiently is an important problem. Aiming at this kind of problem, this article starts with the privacy problem in machine learning and the way of being attacked and summarizes the privacy protection methods and characteristics in the machine learning algorithm. Then, for the classification accuracy of the different algorithm that uses noise to protect privacy, a deep difference privacy protection method combined with a convolutional neural network is proposed. This method perfectly integrates the features of difference and Gaussian distribution and can obtain the privacy budget of each layer of the neural network. Finally, the stochastic gradient descent algorithm's gradient value is employed to set the Gaussian noise scale and preserve the data's sensitive information. The experimental results demonstrated that by adjusting the parameters of the depth differential privacy model based on differences in private information in the data, a balance between the availability and privacy protection of the training data set could be reached.

Afterword: Some Illustrations

Afterword: Some Illustrations

If you are late, you are Beyond help: Disinformation and Authorities in Social Media

Fast paced, seemingly vast and ever-growing social media is a challenging environment for public authorities to communicate optimally. One challenge is malicious disinformation, which is intentionally disseminated to deceive and cause harm to citizens and authorities. It is known that exceptional circumstances create opportunities for malicious actors to negatively influence democratic societies. Disinformation is often designed to cause uncertainty towards information that public authorities offer and to decrease the overall trust in public authorities. The aim of disinformation is often to cause polarisation in society and to weaken national security. Furthermore, in a crisis, it is essential that authorities are able to deliver official information quickly, clearly and accurately to citizens. Communication between authorities and citizens in time-sensitive situations is typically online. One challenge to public authorities is how they can mitigate and repair the effects of disinformation and information influencing in complex and time-sensitive circumstances. In this article, our aim is to describe the challenges that public authorities face when communicating in social media spaces where disinformation is present. The empirical data, including 16 government official interviews, was collected in September 2021. The main theme of the interviews was related to how situational awareness about disinformation is formed in their organisations. Our research questions focus on how public authorities detect and counter disinformation in social media and what kind of problems and pressures they have when communicating in such environments. This study follows a qualitative design and the data was analysed using inductive content analysis. This study is part of larger project related to counterforces and the detection of disinformation. The results will provide a broader understanding of how different types of public authorities, from health to security organisations, and from agencies to ministries, communicate in complex environments such as social media.