Miracle-dz/UniFAR-FTUs huggingface dataset:
Türkiye Sanayi Elektrik Enerjisi Tüketiminin 2017-2023 dönemi için Yapay Sinir Ağları ile Tahmini

Ulkelerin gelismesinde sanayinin buyuk bir rolu olup gecmisten gunumuze kadar sanayi faaliyetleri hiz kesmeden ilerlemistir. Bu gelisime ayak uyduran ulkeler ucuz hammaddeleri isleyip yuksek ucretlere satarak hazinelerini genisletmislerdir. Endustri 4.0 devriminin safaginda bu gelisimden geri kalinmamasi gerekmekte olup gerek sanayi gerekse teknoloji birlikte gelistirilmelidir. Sanayilesmedeki en buyuk ihtiyaclardan biri elektrik enerjisi olup Turkiye’de elektrik enerjisi tuketiminin sanayi icin oranlari yillara gore %40 ile %60 arasinda degismektedir. Bu oranlar dusunuldugunde elektrik tuketiminin buyuk bir payi sanayiye ait olup ileriye yonelik planlamalarin yapilmasina kesinlikle ihtiyac duyulmaktadir. Turkiye’nin Endustri 4.0 ile birlikte gelecek planlarinda elektrik enerjisi sikintisina dusmemesi icin ileriye yonelik tahminleme ve buna uygun yeni tesislerin kurulumlarinin planlanmasi gerekmektedir. Bu calismada, Turkiye’de 1970-2016 yillarina ait sanayi icin elektrik tuketimleri yapay sinir aglari ile modellenmis olup elde edilen model daha sonra 2017-2023 yillarindaki tuketimi tahmin etmek icin kullanilmistir. Yapay sinir agi birisi-disarida capraz dogrulama yontemi ile test edilmis olup elde edilen sonuclara gore; ortalama karesel hatalarin karekoku degeri 8.99, ortalama mutlak yuzde hata %31.6 ve belirleme katsayisi ise 0.94 olarak elde edilmis olup bu sonuclar modelin iyi kuruldugunu ortaya koymaktadir. Ayrica 2023 yilina kadar olan tahmin degerleri de Turkiye Elektrik Iletim A. Ş. Genel Mudurlugu’nun kendi tahminleri ile paralellik gostermektedir.
Preventing Human Error on Online Transaction (A Case Study of B.com)

Buying and selling transactions using internet media has advantages related to time and costs. However, buyers often feel difficult when accessing online websites. There are several types of errors that are experienced by buyers when using online buying and selling site services. It is including mistakes in selecting display menus, difficulties in finding items needed because there are too many choices available, errors in interpreting menus used, and sometimes difficulties in knowing product specifications because no relevant information is available on the site. In this study, we discuss the application of the HTA and SHERPA method to assess one of the online buying and selling sites currently used by Indonesian people, namely B.com. There are 100 respondents participated in this study. The study methods are including error identification, analyzing the error using SHERPA, and evaluating the website design. The result of the study provides some recommendation to the online buyer such as ensuring the quality of internet network, filling the data carefully, and confirming the purpose and nominal value of the transaction before it sent to the seller.
Open-ended design : local re-appropriations through imperfection

Design for Sustainability (DfS) focuses on wicked problems that cannot be modelled in reductionist ways. Furthermore, when bottom-up local interventions prove to have positive effects in their context, they remain hard to spread and might face failure if transferred the other contexts. Here, a research through-design approach is present-ed for highlighting a new paradigm, that questions the very nature of both design process and outcomes. Specifically, Open-ended Design (OeD) is introduced pursuing the creation of unfinished and ever-evolving outcomes (im-perfect by intention), embracing the out-of control local instances. In this approach balance between openness and over-design is sought, to facilitate both the global diffusion of design outcomes and their local re-appropriation. The aim of the research is to highlight existing connections between OeD and DfS, listing its values and limitations through some reported cases. In conclusion, designers might start designing for emergent aspects of the designed solutions, supporting multiple local re-appropriations.
Proposal and Evaluation of Detour Path Suppression Method in PS Reinforcement Learning

: Proﬁt sharing (PS) is well known as a kind of reinforcement learning. In a PS method, a reward is generally distributed with a geometrically decreasing function, and the common ratio of the function is called a discount rate. A large discount rate increases the learning speed, but a non-optimal policy may be learned. On the other hand, a small discount rate improves the performance of the policy, but the learning may not proceed smoothly because of the shallow learning depth. In this paper, in order to cope with these problems, we propose a method that reinforces both the detour path and the non-detour path with di ﬀ erent discount rates. Finally, this method is applied to a maze problem and an altruistic multi-agent environment to conﬁrm its e ﬀ ectiveness.
Use of Technology in the Management of Cancer Pain

The purpose of this study was to explore issues surrounding pharmacologic therapies used with advanced pain technology (APT) for cancer pain management. This study used a multi-site audit of randomly selected charts of patients advanced from oral analgesics to either patient controlled analgesia (PCA) or continuous intravenous (IV) infusion of opioids. A chart audit tool was developed and tested to establish reliability and validity. One hundred and thirty-six (136) chart audits were completed across nine acute care settings in three states. Data is also provided from a questionnaire mailed to health care professionals regarding the use of pain technology (N = 442). Study findings illustrate inconsistent indications for “high tech” strategies and important clinical implications for interventions such as PCA. Clinicians caring for patients with pain have an opportunity to develop standards for use of pain technology which can impact health policy and insure patient comfort.
Reusable firmware development : a practical approach to APIs, HALs and drivers

ion is revealing functionality and software features while hiding the implementation details. Encapsulation is wrapping related data and code together into a single unit. Objects are any entity that has a state or known behavior. Classes are a logical software entity that is a collection of objects. Inheritance is when a class inherits the characteristics of another class. Abstractions and Abstract Data Types (ADTs)ions and Abstract Data Types (ADTs) An abstraction hides the underlying implementation details while making the functionality available to developers. For example, a well-implemented GPIO driver will provide an interface that tells a developer what can be done with the driver, but the developer doesn’t need to know any details about how the driver is implemented or even on what hardware it runs. Abstractions hide the details from developers, creating a black box that simplifies what they need to know to use the software. Abstractions don’t only apply to component interfaces. Abstractions can just as easily be applied to data types. Abstract data types (often written as ADT for short) are data types whose implementation details are hidden from the view of the user for a data structure. There are several different methods that can be used to create an ADT in C. One method that is straightforward can be done in five easy steps. Let’s look at how we can create an ADT for managing a memory stack. First, a developer defines the abstract data type. The ADT in C is usually defined as a pointer to a structure. The ADT is declared within a header file without any underlying details, leaving it up to the implementer to fully declare the ADT in the source module. An example of an ADT would be a StackPtr_t, NodePtr_t, or QueuePtr_t, to name a few. If a developer were to define an ADT for a stack, they would start by defining the code shown in Figure 3-14 in the stack.h file. The details for the members in StackStruct_t are completely hidden from the users’ perspective. Any interaction with StackPtr_t must be done using predefined operations. 8 http://www.javatpoint. com/java-oops-concepts Chapter 3 DeviCe Driver FunDamentals in C
Development a Hybrid Conjugate Gradient Algorithm for Solving Unconstrained Minimization Problems

صخلملا بلا اذى يف مت ثح حا رت يا تيمزراوخ تاتيمزراوخ نتم ة قتفا رتملا جردت لا تت بثم دتيو . ةتينيجيلا دنع يفاك رادحنا وذ ثحب هاجتا ايل نإ ة يرطلا . را ركت لك رطلا هذى تريظم ديو ة ي لماش برا ت تايتضرفلا ضعب تحت ترتيظمو . ةتيددعلا جئات نلا ةت يرطلا هذتيل افك اتيت لاود ىتمع اتي يبطتب كتلذو ةاطعم رابتخا . Abstract In this paper, a new hybrid nonlinear conjugate gradient method are presented, which produce sufficient descent search direction at every iteration. This methods showed globally convergent under some assumptions. The numerical results show that all this new hybrid method are efficient for the given test problems.
Measurement of health service performance through machine learning using clustering techniques

The health center is the first level public health service center that is run by the Indonesian government. Organizing quality health center services of health center is the hope and satisfaction of every patient. The dimensions of patient satisfaction vary greatly and the scope is very wide. Patient satisfaction can be used as an indicator of quality and service performance of health center. The purpose of this study was to measure the performance of health center services in order to determine the level of patient satisfaction and grouping the Patient Satisfaction Index. With the knowledge of the dimensions of performance and quality of health services provided by the health center, it will facilitate the government in carrying out the function of guidance and control of the health center. This study uses a Machine Learning technology approach with clustering techniques, by grouping the Patient Satisfaction Index with K-Means (Hard Clustering) and Fuzzy C-Mean (Soft Clustering) methods. Based on the subsets produced, clustering techniques can be divided into 2 methods, namely hard clustering techniques and soft clustering techniques. The K-Mean method is widely used in clustering techniques. K-Mean has advantages in computational speed and relatively easy process stages. While the Fuzzy C-Means method has advantages in terms of flexibility in determining clusters so that there is little possibility of converging failure. The experimental results of the Patient Satisfaction Index show that the K-Means method provides better performance with a value of 96% compared to the Fuzzy C-Means method with a value of 76%.
On the Optimization of PV Cells’ Orientation Angles and Their Deployment at Base Stations for Energy-efficient Cellular Networks

The inherent problem of solar-powered base stations (BSs) will be tackled in this thesis, i.e., the problem that the energy generation of the photovoltaic (PV) cells does not match the energy consumption of the BS in time, which results in energy being wasted. In Chapter 1, a comprehensive literature review is given. In Chapter 2, the orientation angles of N PV cells powering one BS are jointly optimized to improve the match between the two profiles on a daily timescale. The energy generation profiles of randomly inclined and oriented PV cells are analytically derived based on the Reindl model. The energy drawn per day from the main grid by the BS given its energy consumption profile is used as the performance metric to determine the optimal set of orientation angels. The main results are that deploying one PV cell (or several PV cells) with the (same) optimized orientation angle is recommended for BSs with an energy consumption profile that has one significant local maximum between sunrise and sunset. Deploying two PV cells (or two equal-sized groups of PV cells) where the two orientation angles (of the two groups) are jointly optimized is recommended for BSs with an energy consumption profile that has significant local maxima in the morning as well as in the afternoon or with a constant energy consumption profile. In Chapter 3, a battery model is added to the system model. The battery model is based on a Markov chain. The effects of different battery capacities on the optimal PV cell orientation angle are investigated. It is shown that PV cell orientation angle optimization should be done for BSs deployed with small batteries. In Chapter 4, the system model is extended to a multi-cell cellular network and a mixed-integer linear programming problem is developed to determine how energy harvesters with anti-correlated energy generation profiles should be deployed to every BS. In Chapter 5, the thesis is concluded.
Staff Development Practices, Attitude to Work and Work Performance among Academic Staff in Tertiary Institutions in Ghana

This study investigated staff development practices attitude to work and work performance among academic staff in tertiary institutions in Ghana. The variable under staff development practices used was institutional commitment to staff development and work performance. One hypothesis was formulated which accommodate the study’s major variable. The survey inferential design was adopted for the study. The instruments used were academic staff development practice and attitude to work questionnaire and work performance questionnaire which covered the independent and dependent variables. After a pilot test, the instrument was administered to 540 academic staff and students randomly sampled from these universities and two polytechnics. The data collected were statistically analyzed using Pearson Product Moment Correlation Coefficient (r) at 0.05 level of significance. The study major finding was that there was a significant relationship between institutional commitment to staff development and academic staff work performance. Therefore it was observed that academic staff work performance and attitude to work is determined by the level of institutional commitment to academic staff development. Based on the above findings, it was suggested that institutional commitment to staff development must be tenaciously encouraged in order to get the best from the academic staff work performance.
Using learning analytics to support students’ engineering design: the angle of prediction

ABSTRACT Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows instructors to offer in-time intervention and support for these at-risk students. Specifically, we develop a task model to characterize the engineering design process so that the data features can be associated with the abstract engineering design phases. A two-stage feature selection method is proposed to address the data sparsity and high dimensionality problems. Then, instead of relying on probability-based algorithms such as Bayesian Networks to represent the task model, this study used the Radial Basis Function based Support Vector Machines for prediction to identify the struggling students. Next, we employ an extra-tree classification method to rank the importance of these features. Teachers can integrate the feature importance ranking with the abstract task model to diagnose students’ problems for scaffolding design. The results show that the proposed approach can outperform the baseline models as well as providing actionable insights for teachers to provide personalized and timely feedback to students. Implications of this study for research and practice are then discussed.
Interpolated Rigid Map Neural Networks for Anatomical Joint Constraint Modelling

- The demand for accurate individual and general kinematic joint models is increasing with growing applications in fields such as animation, biomechanics, motion capture, ergonomics and robot human interaction modelling. Many approaches have exploited unit quaternions to eliminate singularities when modelling orientations between limbs at a joint, leading to the development of a number of novel joint constraint validation and correction methods. A number of machine learning approaches have been applied to this modelling problem, as depending on training data either individual or general joint models can be created. Recent work has demonstrated the use of Rigid Maps to model regular conical constraints on the orientation of the limb. In this paper we extend this work deploying a modified Rigid Map Network with a continuous output.
Hand Geometry Techniques: A Review

 Abstract — Biometrics is used for identification and verification of individuals using their physical or behavioral characteristics. Biometrics has gained importance in today’s world where information security is essential. Automated biometric systems have emerged as a more reliable alternative to the traditional personal identification solutions. Hand geometry, one of the most well-known biometrics, is implemented in many verification systems with various feature extraction methods. It is one of the most popular biometrics due to its ease of use, non-intrusiveness and public acceptance. Hand geometry based biometric systems are used in the applications where low to medium security is required. Hand biometrics is extensively used for personal authentication
Design and Implementation of CMOS Telescopic Op-Amp for Bio-Medical Applications

Operational Amplifiers (Op–Amps) are the basic integral part of all analog VLSI designs and mixed VLSI designs. For bio-medical applications, the Op-Amp with higher common mode voltage and low noise is essential to evaluate qualitatively the electrocardiogram (ECG) signal and electroencephalography (EEG) signal in the diagnosis process for more accurate treatment of patients. Medical instruments with qualitative signals help the doctors to efficiently reduce the time in the diagnosis process. Generally folded cascode Op-Amp find uses in many of the applications, but it has heavy current consumption to achieve the required transconductance (gm), it is noisier and consumes more power. There is a need for an Op-Amp with less power, less noise and more gm. Folded cascode Op-Amp can be replaced with telescopic Op-Amp at the output stage to enhance the common mode input voltage. Telescopic circuits require half of the supply current as that of folded cascode circuit to obtain the same amount of peak current output and transconductance, with much reduced noise in telescopic Op-Amp. As this configuration takes lower currents, the speed of operation increases. Our work reported here concentrates on the design of higher gain and high slew rate Op-Amp, which would be able to measure bio-medical signals like ECG, EEG etc with higher clarity. The Op-amp is able to produce a gain of 81dB, Phase Margin greater than 450, improved Unity Gain Bandwidth (UGB) of 4.03MHz, with a power less than 190μW. The design can be implemented with 0. 18μm CMOS technology with 1.8V power supply. The cadence virtuoso tools were used for this design.
Defending Cross-Site Request Forgery (CSRF) Attacks on Web Applications

Defending Cross-Site Request Forgery (CSRF) Attacks on Web Applications by Roshan A. Shaikh Submitted in partial fulfillment of the requirements for the degree of Doctor of Professional Studies in Computing April 2019 This work presents the most current and comprehensive understanding of a not very well understood web vulnerability known as the CSRF (Cross-Site Request Forgery) and provides specific solutions to identify and defend CSRF vulnerabilities. The immediate benefits of this work include tangible and pragmatic application framework for use by individuals, organizations and developers, either as consumers or providers of web services. This work responds directly to the challenges of keeping pace with the evolving cyber technologies and vulnerabilities that increasingly expose businesses towards privacy and identity theft specific attacks, where the traditional anti-virus and anti-spyware approaches fail. The urgency to come up with appropriate detection and defense mechanism against the lethal CSRF attacks is indicated due to expanding cloud based technologies, HTML5, Semantic Web, and various emerging security frameworks comprised of inchoate vestigial of “Big Data” that demand exceedingly evolved defense mechanisms. A methodical approach is used to investigate CSRF attacks and remedies are proposed by introducing a novel distinctive set of algorithms that use intelligent assumptions to detect and defend CSRF. In this work, design details of a CSRF Detection Model (CDM), implantation and experimentation results of CDM are elaborated to detect, predict and provide solutions for CSRF attacks on contemporary Web Applications and Web Services environment. Additionally, CDM based recommendations for users and providers of cyber security products and services are presented.
Learning to Teach in an Urban Teacher Residency

In this article, I employ sociocultural theory to analyze the learning to teach process of two novice teachers enrolled in one Urban Teacher Residency (UTR). Findings show that Genesis and Jackie were differentially drawing on programmatic, disciplinary, relational, experiential, and dispositional resources as they learned to teach in an urban context. I show that programmatic resources of supervision and classroom management requirements (i.e., Doug Lemov’s Teach Like a Champion) not only differentially influenced teachers’ learning and development but also differentially impacted the development of trust with students.
Churn prediction in Turkey's telecommunications sector: A proposed multiobjective–cost‐sensitive ant colony optimization

Players in the telecommunications sector struggle against the competition to keep customers, and therefore they need effective churn management. Most classification algorithms either ignore misclassification cost or assume that the costs of all incorrect classification errors are equal. But as in real life, many classification problems have different misclassification costs and this difference cannot be ignored. For this reason, studies on cost‐sensitive classification approaches have gained importance in recent years. The characteristics of telecommunications datasets such as high dimensionality and imbalance are making it difficult to achieve the desired performance for churn prediction. By taking this into consideration, we propose a multiobjective–cost‐sensitive ant colony optimization (MOC‐ACO‐Miner) approach which integrates the cost‐based nondominated sorted genetic algorithm feature selection and multiobjective ACO based cost‐sensitive learning. MOC‐ACO‐Miner is applied to one of Turkey's top 100 information technology companies for customer churn‐prediction. Finally, experiments find out that the model performs quite well with the area under receiver operating characteristic curve values of 0.9998 for predicting churners and therefore it can be beneficial for the highly competitive telecommunications sector.
Higher-Order Iteration Schemes for Solving Nonlinear Systems of Equations

We present a three-step family of iterative methods to solve systems of nonlinear equations. This family is a generalization of the well-known fourth-order King’s family to the multidimensional case. The convergence analysis of the methods is provided under mild conditions. The analytical discussion of the work is upheld by performing numerical experiments on some application oriented problems. Finally, numerical results demonstrate the validity and reliability of the suggested methods.