Search results for: state machine

Authors: Saheed A. Rufai

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Courses in Pedagogy and Curriculum Development expectedly occupy a core place in the professional education components of teacher education at Lagos, Lagos State, and Sokoto State Universities. This is in keeping with the National Teacher Education Policy statement that stipulates that for student teachers to learn effectively teacher education institutions must be equipped to prepare them adequately. However, there is a growing concern over the unfaithfulness of some of the dominant Nigerian models of teacher education, to this policy statement on teacher educators’ knowledge and skills. The purpose of this paper is to comparatively evaluate both the curricular provisions and the manpower for the pedagogy and curriculum development sub-components of the Lagos, Lagos State, and Sokoto State models of teacher preparation. The paper employs a combination of quantitative and qualitative methods. Preliminary analysis revealed a new trend in teacher educators’ pedagogical knowledge and understanding, with regard to the two intertwined sub-components. The significance of such a study lies in its potential to determine the degree of conformity of each of the three models to the stipulated standards. The paper’s contribution to scholarship lies in its correlation of deficiencies in teacher educators’ professional knowledge and skills and articulation of the implications of such deficiencies for the professional knowledge and skills of the prospective teachers, with a view to providing a framework for reforms.

Keywords: curriculum development, pedagogy, teacher education, dominant Nigerian teacher preparation models

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Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

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Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

Procedia PDF Downloads 63

Authors: Ehsan Vaziri Goodarzi, Mahmood Houshmand, Omid Fatahi Valilai, Vahidreza Ghezavati, Shahrooz Bamdad

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One important paradigm of industry 4.0 is Cloud Manufacturing (CM). In CM everything is considered as a service, therefore, the CM platform should consider all service provider's capabilities and tries to integrate services in an equilibrium state. This research develops a framework for implementing manufacturing cloud service composition in the equilibrium state. The developed framework using well-known tools called axiomatic design (AD) and game theory. The research has investigated the factors for forming equilibrium for measures of the manufacturing cloud service composition. Functional requirements (FRs) represent the measures of manufacturing cloud service composition in the equilibrium state. These FRs satisfied by related Design Parameters (DPs). The FRs and DPs are defined by considering the game theory, QoS, consumer needs, parallel and cooperative services. Ultimately, four FRs and DPs represent the framework. To insure the validity of the framework, the authors have used the first AD’s independent axiom.

Keywords: axiomatic design, manufacturing cloud service composition, cloud manufacturing, industry 4.0

Procedia PDF Downloads 170

Authors: Nacera Yassa, Abdelmalek Saidoune, Ghania Ouadfel, Hamza Houassine

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The rapid advancement in electrical technologies has underscored the increasing importance of multiphase machines across various industrial sectors. These machines offer significant advantages in terms of efficiency, compactness, and reliability compared to their single-phase counterparts. However, early detection and diagnosis of electrical faults remain critical challenges to ensure the durability and safety of these complex systems. This paper presents an advanced analytical model for multiphase machines, with a particular focus on dual stator asynchronous machines. The primary objective is to develop a robust diagnostic tool capable of effectively detecting and locating electrical faults in these machines, including short circuits, winding faults, and voltage imbalances. The proposed methodology relies on an analytical approach combining electrical machine theory, modeling of magnetic and electrical circuits, and advanced signal analysis techniques. By employing detailed analytical equations, the developed model accurately simulates the behavior of multiphase machines in the presence of electrical faults. The effectiveness of the proposed model is demonstrated through a series of case studies and numerical simulations. In particular, special attention is given to analyzing the dynamic behavior of machines under different types of faults, as well as optimizing diagnostic and recovery strategies. The obtained results pave the way for new advancements in the field of multiphase machine diagnostics, with potential applications in various sectors such as automotive, aerospace, and renewable energies. By providing precise and reliable tools for early fault detection, this research contributes to improving the reliability and durability of complex electrical systems while reducing maintenance and operation costs.

Keywords: faults, diagnosis, modelling, multiphase machine

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: deep learning, generative, knowledge, response generation, retrieval

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Authors: Xiaoman Dong

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The refugee problem has a long history, but the scale and severity of modern refugee crises demand us to consider if the progress of political history exacerbates the refugee problem. This paper argues that although sovereignty owes its legitimacy to the protection of human rights, the modern state system complicates the refugee problem by first introducing then blurring the line between human rights and civil rights, and making national identity indispensable to basic livelihood and dignity. This paper first explains the source of the modern state system’s legitimacy by putting it in the context of social contract theories and the politics of nation-building. It then discusses how states create the concept of statelessness, which leads to more violations on human rights. Using historical records of the League of Nations High Commission for Refugees and the United Nations High Commissioner for Refugees, this paper reveals that neither the refugee problem of the Cold-War period nor the current refugee crisis is collateral damage of war, but rather the consequence of intentional exclusionary policies produced out of political interests. Finally, it contends that if the modern state system is to sustain, it cannot prioritize the protection of civil rights of a particular group over the protection of basic human rights of all.

Keywords: burden sharing, human rights, legitimacy of state, positive externality, sovereignty

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Authors: Amrane Meriem, Oukid Salyha

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The presence of defects or bugs in software can lead to costly setbacks, operational inefficiencies, and compromised user experiences. The integration of Machine Learning(ML) techniques has emerged to predict and preemptively address software defects. ML represents a proactive strategy aimed at identifying potential anomalies, errors, or vulnerabilities within code before they manifest as operational issues. By analyzing historical data, such as code changes, feature im- plementations, and defect occurrences. This en- ables development teams to anticipate and mitigate these issues, thus enhancing software quality, reducing maintenance costs, and ensuring smoother user interactions. In this work, we used a recommendation system to improve the performance of ML models in terms of predicting the code severity and effort estimation.

Keywords: software engineering, machine learning, bugs detection, effort estimation

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Authors: Jozsef Katona, Attila Kovari, Tibor Ujbanyi, Gergely Sziladi

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By the development of IT systems, human-computer interaction is also developing even faster and newer communication methods become available in human-machine interaction. In this article, the application of a hand gesture controlled human-computer interface is being introduced through the example of a mobile robot. The control of the mobile robot is implemented in a realistic virtual environment that is advantageous regarding the aspect of different tests, parallel examinations, so the purchase of expensive equipment is unnecessary. The usability of the implemented hand gesture control has been evaluated by test subjects. According to the opinion of the testing subjects, the system can be well used, and its application would be recommended on other application fields too.

Keywords: human-machine interface (HCI), mobile robot, hand control, virtual environment

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Authors: Barenten Suciu

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In this paper, effectiveness of a machine design process is evaluated on the basis of the specific output calculus. Concretely, a screw-worm gear mechanical transmission is designed by using the classical and the 3D-CAD methods. Strength analysis and drawing of the designed parts is substantially aided by employing the SolidWorks software. Quality of the design process is assessed by manufacturing (printing) the parts, and by computing the efficiency, specific load, as well as the specific output (work) of the mechanical transmission. Influence of the stroke, travelling velocity and load on the mechanical output, is emphasized. Optimal design of the mechanical transmission becomes possible by the appropriate usage of the acquired results.

Keywords: mechanical transmission, design, screw, worm-gear, efficiency, specific output, 3D-printing

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Authors: Yuxi Zhu, Zhenqian Chen

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It is of great interest to investigate the thermal properties of non-precious metal catalysts for Proton exchange membrane fuel cell (PEMFC) based on the thermal management requirements. Due to the low symmetry of materials, to accurately obtain the thermal conductivity of materials, it is necessary to obtain the second and third-order force constants by combining density functional theory and machine learning interatomic potential and then further solve the Boltzmann transport equation. In this paper, the thermal transport properties of single metal atom catalysts are studied for the first time to our best knowledge by machine-learning interatomic potential (MLIP). Results show that the single metal atom catalysts exhibit anisotropic thermal conductivities and partially exhibit good thermal conductivity. The average lattice thermal conductivities of G-FeN₄, G-CoN₄ and G-NiN₄ at 300 K are 88.61 W/mK, 205.32 W/mK and 210.57 W/mK, respectively. While other single metal atom catalysts show low thermal conductivity due to their low phonon lifetime. The results also show that low-frequency phonons (0-10 THz) dominate thermal transport properties. The results provide theoretical insights into the application of single-metal atom catalysts in thermal management.

Keywords: proton exchange membrane fuel cell, single metal atom catalysts, density functional theory, thermal conductivity, machine-learning interatomic potential

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Authors: Wiji Wijaya

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The aim of this research is to examine the effect of internal control weaknesses and audit opinion on the state’s loss findings of audit compliance to the regulation in public sector. The samples of this research consisted of 175 local government financial statements in the area of Central Java Province at 2009 until 2013. Area sampling design was used to select the financial statements. This study using quantitative descriptive statistical analysis and regression was run for data analysis and hypothesis examination. Result of this study indicated that internal control weaknesses and audit opinion contributes a positive influence which is significant to the state’s loss findings of audit compliance to the regulation. The internal control weaknesses that affect the state's loss finding are weakness control system of accounting and reporting with the value of the critical ratio 0.010 p 2.613 ; weakness budget execution control system with critical ratio value of 3.421 p 0.001 and weaknesses internal control structure with critical ratio value of 2.246 p 0.026 . While the audit opinion with a critical ratio value of 4.401 p 0.000. The implications of this research so that policy makers at the local government should give more attention to the implementation and improvement of internal control system.

Keywords: audit compliance findings, state’s loss, audit opinion, internal control, local government

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Joselito Medina Marin, Norberto Hernández Romero, Juan Carlos Seck Tuoh Mora, Erick S. Martinez Gomez

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The Flow Shop Scheduling Problem (FSSP) is a typical problem that is faced by production planning managers in Flexible Manufacturing Systems (FMS). This problem consists in finding the optimal scheduling to carry out a set of jobs, which are processed in a set of machines or shared resources. Moreover, all the jobs are processed in the same machine sequence. As in all the scheduling problems, the makespan can be obtained by drawing the Gantt chart according to the operations order, among other alternatives. On this way, an FMS presenting the FSSP can be modeled by Petri nets (PNs), which are a powerful tool that has been used to model and analyze discrete event systems. Then, the makespan can be obtained by simulating the PN through the token game animation and incidence matrix. In this work, we present an adaptive PN to obtain the makespan of FSSP by applying PN analytical tools.

Keywords: flow-shop scheduling problem, makespan, Petri nets, state equation

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Authors: Alica Höpken, Hergen Pargmann

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The resource-efficient planning of the complex production planning processes in the meat industry and the reduction of food waste is a permanent challenge. The complexity of the production planning process occurs in every part of the supply chain, from agriculture to the end consumer. It arises from long and uncertain planning phases. Uncertainties such as stochastic yields, fluctuations in demand, and resource variability are part of this process. In the meat industry, waste mainly relates to incorrect storage, technical causes in production, or overproduction. The high amount of food waste along the complex supply chain in the meat industry could not be reduced by simple solutions until now. Therefore, resource-efficient production planning by conventional methods is currently only partially feasible. The realization of intelligent, automated production planning is basically possible through the application of machine learning algorithms, such as those of reinforcement learning. By applying the adapted design thinking method, machine learning methods (especially reinforcement learning algorithms) are used for the complex production planning process in the meat industry. This method represents a concretization to the application area. A resource-efficient production planning process is made available by adapting the design thinking method. In addition, the complex processes can be planned efficiently by using this method, since this standardized approach offers new possibilities in order to challenge the complexity and the high time consumption. It represents a tool to support the efficient production planning in the meat industry. This paper shows an elegant adaption of the design thinking method to apply the reinforcement learning method for a resource-efficient production planning process in the meat industry. Following, the steps that are necessary to introduce machine learning algorithms into the production planning of the food industry are determined. This is achieved based on a case study which is part of the research project ”REIF - Resource Efficient, Economic and Intelligent Food Chain” supported by the German Federal Ministry for Economic Affairs and Climate Action of Germany and the German Aerospace Center. Through this structured approach, significantly better planning results are achieved, which would be too complex or very time consuming using conventional methods.

Keywords: change management, design thinking method, machine learning, meat industry, reinforcement learning, resource-efficient production planning

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Authors: Isabel Moscol, Carlos J. Díaz, Ciro Rodríguez

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Hip replacement becomes necessary when a person suffers severe pain or considerable functional limitations and the best option to enhance their quality of life is through the replacement of the damaged joint. One of the main components in femoral prostheses is the stem which distributes the loads from the joint to the proximal femur. To preserve more bone stock and avoid weakening of the diaphysis, a short starting stem was selected, generated from the intramedullary morphology of the patient's femur. It ensures the implantability of the design and leads to geometric delimitation for personalized optimization with machine learning (ML) and metaheuristic algorithms. The present study attempts to design a cementless short stem to make the strain deviation before and after implantation close to zero, promoting its fixation and durability. Regression models developed to estimate the percentage change of maximum principal stresses were used as objective optimization functions by the metaheuristic algorithm. The latter evaluated different geometries of the short stem with the modification of certain parameters in oblique sections from the osteotomy plane. The optimized geometry reached a global stress shielding (SS) of 18.37% with a determination factor (R²) of 0.667. The predicted results favour implantability integration in the short stem optimization to effectively reduce SS in the proximal femur.

Keywords: machine learning techniques, metaheuristic algorithms, short-stem design, stress shielding, hip replacement

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Authors: M. A. Grigoryev, A. N. Shishkov, D. A. Sychev

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The article defines the necessity of choosing the optimal power circuits scheme of the electric drive with field regulated reluctance machine. The specific weighting factors are calculation, the linear regression dependence of specific losses in semiconductor frequency converters are presented depending on the values of the rated current. It is revealed that with increase of the carrier frequency PWM improves the output current waveform, but increases the loss, so you will need depending on the task in a certain way to choose from the carrier frequency. For task of optimization by criterion of the minimum electrical losses regression dependence of the electrical losses in the frequency converter circuit at a frequency of a PWM signal of 0 Hz. The surface optimization criterion is presented depending on the rated output torque of the motor and number of phases. In electric drives with field regulated reluctance machine with at low output power optimization criterion appears to be the worst for multiphase circuits. With increasing output power this trend hold true, but becomes insignificantly different optimal solutions for three-phase and multiphase circuits. This is explained to the linearity of the dependence of the electrical losses from the current.

Keywords: field regulated reluctance machine, the electrical losses, multiphase power circuit, the surface optimization criterion

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Hans Schumann, Colin Domnauer, Louis Rosenberg

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In this study, machine learning techniques were deployed on real-time human swarm data to forecast the likelihood of outcomes for English Premier League matches in the 2020/21 season. These techniques included ensemble models in combination with neural networks and were tested against an industry standard of Vegas Oddsmakers. Predictions made from the collective intelligence of human swarm participants managed to achieve a positive return on investment over a full season on matches, empirically proving the usefulness of a new artificial intelligence valuing human instinct and intelligence.

Keywords: artificial intelligence, data science, English Premier League, human swarming, machine learning, sports betting, swarm intelligence

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Authors: Jose Luis Solleiro, Rosario Castanon, Laura Elena Martinez

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The State of Mexico is an administrative entity of Mexico, and it is one of the most important territories due to its great economic and social impact for the whole country, especially since it contributes with more than eight of the national Gross Domestic Product (GDP). The State of Mexico has a population of over seventeen million people and host very important business and productive industries such as Automotive, Chemicals, Pharmaceutical, and Agri-food. In 2017, the State Development Plan (Plan Estatal de Desarrollo in Spanish) which is a policy document that rules State's economic actions and integrates the bases for sectoral and regional programs to achieve regional development), raised innovation as a key aspect to boost competitiveness and productivity of the State of Mexico. Therefore, in line with this proposal, in 2018 the Mexican Council for Science and Technology (COMECYT for its acronym in Spanish), an institution in charge of promoting public science and technology policies in the State of Mexico, took actions towards building up the State´s Innovation System. Hence, the main objective of this paper is to review and analyze the process to create RIS in the State of Mexico. We focus on the key elements of the process, the diverse actors that were involved in it, the activities that were carried out and the identification of the challenges, findings, successes, and failures of the intended exercise. The methodology used to analyze the structure of the Innovation System of the State of Mexico is based on two elements: the case study and the research-action approach. The main objective of the paper, the case study was based on semi-structured interviews with key actors who have participated in the process of launching the RIS of the State of Mexico. Additionally, we analyzed the information reports and other documents that were elaborated during the process of shaping the State's innovation system. Finally, the results obtained in the process were also examined. The relevance of this investigation fundamentally rests in two elements: 1) keeping documental record of the process of building a RIS in Mexico; and 2) carrying out the analysis of this case study recognizing the importance of knowledge extraction and dissemination, so that lessons on this matter may be useful for similar experiences in the future. We conclude that in Mexico, documentation and analysis efforts related to the formation of RIS and interaction processes between innovation ecosystem actors are scarce, so documents like are of great importance, especially since it generates a series of findings and recommendations for the building of RIS.

Keywords: regional innovation systems, innovation, development, competitiveness

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Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

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Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

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Authors: Roger-Claude Liwanga

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This article reflects on the normative aspects of the jurisprudence on the protection of human rights in times of war that the International Court of Justice (ICJ) developed in 2005 in the Case Concerning Armed Activities on the Territory of the Congo (Democratic Republic of Congo v. Uganda). The article focuses on theories raised in connection with the Democratic Republic of Congo (DRC)'s claim of the violation of human rights of its populations by Uganda as opposed to the violation of its territorial integrity claims. The article begins with a re-visitation of the doctrine of state extraterritorial responsibility for violations of human rights by suggesting that a state's accountability for the breach of its international obligations is not territorially confined but rather transcends the State's national borders. The article highlights the criteria of assessing the State's extraterritorial responsibility, including the circumstances: (1) where the concerned State has effective control over the territory of another State in the context of belligerent occupation, and (2) when the unlawful actions committed by the State's organs on the occupied territory can be attributable to that State. The article also analyzes the ICJ's opinions articulated in DRC v. Uganda with reference to the relationship between human rights law and humanitarian law, and it contends that the ICJ had revised the traditional interaction between these two bodies of law to the extent that human rights law can no longer be excluded from applying in times of war as both branches are complementary rather than exclusive. The article correspondingly looks at the issue of reparations for victims of human rights violations. It posits that reparations for victims of human rights violations should be integral (including restitution, compensation, rehabilitation, satisfaction, and guarantees of non-repetition). Yet, the article concludes by emphasizing that reparations for victims were not integral in DRC v. Uganda because: (1) the ICJ failed to set a reasonable timeframe for the negotiations between the DRC and Uganda on the amount of compensation, resulting in Uganda paying no financial reparation to the DRC since 2005; and (2) the ICJ did not request Uganda to domestically prosecute the perpetrators of human rights abuses.

Keywords: human rights law, humanitarian law, civilian protection, extraterritorial responsibility

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Walied Hanora

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This paper presents the problem of robust state feedback H∞ for discrete time nonlinear system represented by Takagi-Sugeno fuzzy systems. Based on fuzzy lyapunov function, the condition ,which is represented in the form of Liner Matrix Inequalities (LMI), guarantees the H∞ performance of the T-S fuzzy system with uncertainties. By comparison with recent literature, this approach will be more relaxed condition. Finally, an example is given to illustrate the proposed result.

Keywords: fuzzy lyapunov function, H∞ control , linear matrix inequalities, state feedback, T-S fuzzy systems

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

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Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

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Authors: J. M. Lee, W. R. Noh, C. Y. Kim, M. G. Lee

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Delamination of oxide scale has been often discovered at the interface between Fe carbon steel and oxide scale. Among several mechanisms of this delamination behavior, the normal tensile stress to the substrate-scale interface has been described as one of the main factors. The stress distribution at the interface is also known to be affected by thermal expansion mismatch between substrate and oxide scale, creep behavior during cooling and the geometry of the interface. In this study, stress states near the interface in a Fe carbon steel with oxide scale have been investigated using FE simulations. The thermal and mechanical properties of oxide scales are indicated in literature and Fe carbon steel is measured using tensile testing machine. In particular, the normal and shear stress components developed at the interface during bending are investigated. Preliminary numerical sensitivity analyses are provided to explain the effects of the interface geometry and oxide thickness on the delamination behavior.

Keywords: oxide scale, delamination, Fe analysis, roughness, thickness, stress state

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Authors: R. A. Moronfolu, I. M. Ndaks, O. E. Ifekoya

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This study investigated Gender Stereotypes in Leadership Behaviour and its consequent effect on Job Performance of Sports Council Personnel in Lagos State. The descriptive research method was adapted in conducting the study, while eighty sports personnel of Lagos State sports council, Lagos, Nigeria were drawn as respondents using the stratified random sampling technique. A self-structured questionnaire titled “ Gender- Leader Performance Questionnaire (GLPQ) ”was used for data collection. The GLPQ was face validated by three experts in sports management and was subjected to a pilot test using the test retest method for reliability. A total of eighty copies of the validated GLPQ were administered on selected respondents and retrieved on the spot. The descriptive statistics of frequency counts and percentages were used in describing the demographic data collected, while the inferential statistics of Chi-square (X2) and Analysis of Variance (ANOVA) were used in drawing inferences at a level of significance of 0.05. It was observed that gender stereotypes and behaviours of leaders in Lagos State Sports Council, significantly differ. In addition, gender stereotypes and leadership behavior were observed to significantly influence the job performance of sports council personnel in Lagos State.

Keywords: gender, leadership, stereotype, performance

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Authors: Majid Khalili, Hamed Tayebi

Abstract:

This paper considers a stochastic flexible job-shop scheduling (SFJSS) problem in the presence of production disruptions, and reactive scheduling is implemented in order to find the optimal solution under uncertainty. In this problem, there are two main disruptions including machine failure which influences operation time, and modification or cancellation of the order delivery date during production. In order to decrease the negative effects of these difficulties, two derived strategies from reactive scheduling are used; the first one is relevant to being able to allocate multiple machine to each job, and the other one is related to being able to select the best alternative process from other job while some disruptions would be created in the processes of a job. For this purpose, a Mixed Integer Linear Programming model is proposed.

Keywords: flexible job-shop scheduling, reactive scheduling, stochastic environment, mixed integer linear programming

Procedia PDF Downloads 351

Authors: Kishor T. Zingre, Seshadhri Srinivasan

Abstract:

Energy consumption by the building sector is increasing at an alarming rate throughout the world and leading to more building-related CO₂ emissions into the environment. In buildings, the main contributors to energy consumption are heating, ventilation, and air-conditioning (HVAC) systems, lighting, and electrical appliances. It is hypothesised that the energy efficiency in buildings can be achieved by implementing sustainable technologies such as i) enhancing the thermal resistance of fabric materials for reducing heat gain (in hotter climates) and heat loss (in colder climates), ii) enhancing daylight and lighting system, iii) HVAC system and iv) occupant localization. Energy performance of various sustainable technologies is highly dependent on climatic conditions. This paper investigated the use of machine learning techniques for accurate prediction of air-conditioning energy in seasonal climates. The data required to train the machine learning techniques is obtained using the computational simulations performed on a 3-story commercial building using EnergyPlus program plugged-in with OpenStudio and Google SketchUp. The EnergyPlus model was calibrated against experimental measurements of surface temperatures and heat flux prior to employing for the simulations. It has been observed from the simulations that the performance of sustainable fabric materials (for walls, roof, and windows) such as phase change materials, insulation, cool roof, etc. vary with the climate conditions. Various renewable technologies were also used for the building flat roofs in various climates to investigate the potential for electricity generation. It has been observed that the proposed technique overcomes the shortcomings of existing approaches, such as local linearization or over-simplifying assumptions. In addition, the proposed method can be used for real-time estimation of building air-conditioning energy.

Keywords: building energy efficiency, energyplus, machine learning techniques, seasonal climates

Procedia PDF Downloads 108