Search results for: human machine collaboration

Authors: Ivana Tadic, Snjezana Pivac

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Although human resources are recognized as the crucial companies’ resources and their positive influence on companies’ performances has been confirmed through different researches, scientists are still debating it. In order to contribute this debate, this paper firstly discusses the most important human resource management elements and practices and its influence on companies’ success. Afterwards it defines human resource “bundles” – interrelated and internally consistent human resource practices, complementary to each other, or the most important human resource practices and elements regarding Croatian companies and its human resource management activities. Finally, the paper provides empirical results; more precisely it reveals the relation of the level of development of human resource management function (“bundles”) and companies’ financial performances (using profitability ratios, liquidity ratios, solvency ratios and a group of additional ratios related to employees’ indicators).

Keywords: companies’ performances, human resource bundles, multivariate statistical analysis, marketing

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Authors: Taha Bensiradj, Samira Moussaoui

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Hybrid Sensors and Vehicular Networks (HSVN), represent a hybrid network, which uses several generations of Ad-Hoc networks. It is used especially in Intelligent Transport Systems (ITS). The HSVN allows making collaboration between the Wireless Sensors Network (WSN) deployed on the border of the road and the Vehicular Network (VANET). This collaboration is defined by messages exchanged between the two networks for the purpose to inform the drivers about the state of the road, provide road safety information and more information about traffic on the road. Moreover, this collaboration created by HSVN, also allows the use of a network and the advantage of improving another network. For example, the dissemination of information between the sensors quickly decreases its energy, and therefore, we can use vehicles that do not have energy constraint to disseminate the information between sensors. On the other hand, to solve the disconnection problem in VANET, the sensors can be used as gateways that allow sending the messages received by one vehicle to another. However, because of the short communication range of the sensor and its low capacity of storage and processing of data, it is difficult to ensure the exchange of road messages between it and the vehicle, which can be moving at high speed at the time of exchange. This represents the time where the vehicle is in communication range with the sensor. This work is the proposition of a communication protocol between the sensors and the vehicle used in HSVN. The latter has as the purpose to ensure the exchange of road messages in the available time of exchange.

Keywords: HSVN, ITS, VANET, WSN

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Le Jiang, Chua Jinxing

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Introduction: The recent outbreak of Ebola virus disease in the west Africa has drawn global concern. With a high fatality rate and direct human-to-human transmission, it has spread between countries and caused great damages for patients and family who are affected. Being the designated hospital to manage epidemic outbreak in Singapore, Tan Tock Seng Hospital (TTSH) is facing great challenges in preparation and managing of potential outbreak of emerging infectious disease such as Ebola virus disease. Aim: We conducted an infection control drill in TTSH emergency department to assess the readiness of healthcare and allied health workers in managing suspected Ebola patients. It also helps to review current Ebola clinical protocol and work instruction to ensure more smooth and safe practice in managing Ebola patients in TTSH emergency department. Result: General preparedness level of staffs involved in managing Ebola virus disease in TTSH emergency department is not adequate. Knowledge deficits of staffs on Ebola personal protective equipment gowning and degowning process increase the risk of potential cross contamination in patient care. Loopholes are also found in current clinical protocol, such as unclear instructions and inaccurate information, which need to be revised to promote better staff performance in patient management. Logistic issues such as equipment dysfunction and inadequate supplies can lead to ineffective communication among teams and causing harm to patients in emergency situation. Conclusion: The infection control drill identified the need for more well-structured and clear clinical protocols to be in place to promote participants performance. In addition to quality protocols and guidelines, systemic training and annual refresher for all staffs in the emergency department are essential to prepare staffs for the outbreak of Ebola virus disease. Collaboration and communication with allied health staffs are also crucial for smooth delivery of patient care and minimising the potential human suffering, properties loss or injuries caused by disease. Therefore, more clinical drills with collaboration among various departments involved are recommended to be conducted in the future to monitor and assess readiness of TTSH emergency department in managing Ebola virus disease.

Keywords: ebola, emergency department, infection control drill, Tan Tock Seng Hospital

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Authors: Evangelos Koumparoudis

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The definition of death is a deep philosophical question, and no single meaning can be ascribed to it. This essay focuses on the ontological, epistemological, and ethical aspects of death and dying in view of technological progress in information technology and biomedicine. It starts with the ad hoc 1968 Harvard committee that proposed that the criterion for the definition of death be irreversible coma and then refers to the debate over the whole brain death formula, emphasizing the integrated function of the organism and higher brain formula, taking consciousness and personality as essential human characteristics. It follows with the contribution of information technology in personalized and precision medicine and anti-aging measures aimed at life prolongation. It also touches on the possibility of the creation of human-machine hybrids and how this raises ontological and ethical issues that concern the “cyborgization” of human beings and the conception of the organism and personhood based on a post/transhumanist essence, and, furthermore, if sentient AI capable of autonomous decision-making that might even surpass human intelligence (singularity, superintelligence) deserves moral or legal personhood. Finally, there is the question as to whether death and dying should be redefined at a transcendent level, which is reinforced by already-existing technologies of “virtual after-” life and the possibility of uploading human minds. In the last section, I refer to the current (and future) applications of nanomedicine in diagnostics, therapeutics, implants, and tissue engineering as well as the aspiration to “immortality” by cryonics. The definition of death is reformulated since age and disease elimination may be realized, and the criterion of irreversibility may be challenged.

Keywords: death, posthumanism, infomedicine, nanomedicine, cryonics

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Authors: Adel Fathy Sadek Abdalla

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The issue of respect for human rights in Southeast Asia has become a major concern and is attracting the attention of the international community. Basically, the Association of Southeast Asian Nations (ASEAN) made human rights one of its main issues and in the ASEAN Charter in 2008. Subsequently, the Intergovernmental Commission on Human Rights ASEAN Human Rights (AICHR) was established. AICHR is the Southeast Asia Human Rights Enforcement Commission charged with the responsibilities, functions and powers to promote and protect human rights. However, at the end of 2016, the protective function assigned to the AICHR was not yet fulfilled. This is shown by several cases of human rights violations that are still ongoing and have not yet been solved. One case that has recently come to light is human rights violations against the Rohingya people in Myanmar. Using a legal-normative approach, the study examines the urgency of establishing a human rights tribunal in Southeast Asia capable of making a decision binding on ASEAN members or guilty parties. Data shows ASEAN needs regional courts to deal with human rights abuses in the ASEAN region. In addition, the study also highlights three important factors that ASEAN should consider when establishing a human rights tribunal, namely: Volume. a significant difference in terms of democracy and human rights development among the members, a consistent implementation of the principle of non-interference and the financial issue of the continuation of the court.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

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Authors: Tolulope A. Olukunle

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Plastic waste management has emerged as one of the greatest challenges facing developing countries. This paper describes the design of various components of a plastic shredder. This machine is widely used in industries and recycling plants. The introduction of plastic shredder machine will promote reduction of post-consumer plastic waste accumulation and serves as a system for wealth creation and empowerment through conversion of waste into economically viable products. In this design research, a 10 kW electric motor with a rotational speed of 500 rpm was chosen to drive the shredder. A pulley size of 400 mm is mounted on the electric motor at a distance of 1000 mm away from the shredder pulley. The shredder rotational speed is 300 rpm.

Keywords: design, machine, plastic waste, recycling

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Authors: Saleh Elawgali

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Current spectrums of a four pole-pair, 400 kW induction machine were calculated for the cases of full symmetry and static eccentricity. The calculations involve integration of 93 electrical plus four mechanical ordinary differential equations. Electrical equations account for variable inductances affected by slotting and eccentricities. The calculations were followed by Fourier analysis of the stator currents in steady state operation. Zooms of the current spectrums, around the 50 Hz fundamental harmonic as well as of the main slot harmonic zone, were included. The spectrums included refer to both calculated and measured currents.

Keywords: diagnostic, harmonic, induction machine, spectrum

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Authors: G. Götz, P. Stich, J. Backhaus, G. Reinhart

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The rising demand for format-flexible packaging machines is caused by current market changes. Increasing the formatflexibility is a new goal for the packaging machine manufacturers’ product development process. There are no methodical or designorientated tools for a comprehensive consideration of this target. This paper defines the term format-flexibility in the context of packaging machines and shows the state-of-the-art for improving the changeover of production machines. The requirements for a new approach and the concept itself will be introduced, and the method elements will be explained. Finally, the use of the concept and the result of the development of a format-flexible packaging machine will be shown.

Keywords: packaging machine, format-flexibility, changeover, design method

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Dulanjali Ranasinghe, Isuru Supasan, Kaushalya Premachandra, Ranjan Dissanayake, Ajith Rajapaksha, Eustace Fernando

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Proteomics studies of organisms are considered to be significantly information-rich compared to their genomic counterparts because proteomes of organisms represent the expressed state of all proteins of an organism at a given time. In modern top-down and bottom-up proteomics workflows, the primary analysis methods employed are gel–based methods such as two-dimensional (2D) electrophoresis and mass spectrometry based methods. Machine learning (ML) and artificial intelligence (AI) have been used increasingly in modern biological data analyses. In particular, the fields of genomics, DNA sequencing, and bioinformatics have seen an incremental trend in the usage of ML and AI techniques in recent years. The use of aforesaid techniques in the field of proteomics studies is only beginning to be materialised now. Although there is a wealth of information available in the scientific literature pertaining to proteomics workflows, no comprehensive review addresses various aspects of the combined use of proteomics and machine learning. The objective of this review is to provide a comprehensive outlook on the application of machine learning into the known proteomics workflows in order to extract more meaningful information that could be useful in a plethora of applications such as medicine, agriculture, and biotechnology.

Keywords: proteomics, machine learning, gel-based proteomics, mass spectrometry

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Authors: Tareq M. Aziz Elven

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The issue of Human Rights enforcement in Southeast Asia has become the serious problem and attract the attention of international community. Principally, Association of Southeast Asian Nations (ASEAN) has mentioned the Human Rights as one of the focus and be a part of the ASEAN Charter in 2008. It was followed by the establishment of ASEAN Inter-Governmental Commission on Human Rights (AICHR). AICHR is the commission of Human Rights enforcement in Southeast Asia which has a duty, function, and an authority to conduct dissemination and protection of Human Rights. In the end of 2016, however, the function of protection mandated to AICHR have not achieved yet. It can be proved by several cases of Human Rights violation which still exist and have not settled yet. One of case which attracts the public attention recently is human rights violation towards Rohingya in Myanmar. Using the juridical-normative method, the research aims to examine the urgency of Human Rights court establishment in Southeast Asia region which able to issue the decision that binds the ASEAN members or the violating parties. The data shows that ASEAN needs to establish a regional court which intended to settle the Human Rights violations in ASEAN region. Furthermore, the research also highlights three strong factors should be settled by ASEAN for establishing human rights court i.e. the significant distinction of democracy and human rights development among the members, the strong implementation of non-intervention principle, and the financial matter to sustain the court.

Keywords: AICHR, ASEAN, human rights, human rights court

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Authors: Richu J. Babu, Sony Mathew, Sharon Rony Jacob, Soney C. George, Jibin C. Jacob

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Human hair is a non-biodegradable waste available in plenty throughout the world but is rarely explored for applications in engineering fields. Tensile strength of human hair ranges from 170 to 220 MPa. This property of human hair can be made use in the field of making bio-composites[1]. The composite is prepared by commixing the human hair and natural rubber in a two roll mill along with additives followed by vulcanization. Here the concentration of the human hair is varied by fine-tuning the fiber length as 20 mm and sundry tests like tensile, abrasion, tear and hardness were conducted. While incrementing the fiber length up to a certain range the mechanical properties shows superior amendments.

Keywords: human hair, natural rubber, composite, vulcanization, fiber loading

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Authors: Stephanie Lehmann, Esther Ruf, Sabina Misoch

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Background: Due to demographic change and shortage of skilled nursing staff, assistive robots are built to support older adults at home and nursing staff in care institutions. When assistive robots facilitate tasks that are usually performed by humans, user acceptance is essential. Even though they are an important aspect of acceptance, emotions towards different assistive robots and different situations of robot-use have so far not been examined in detail. The appearance of assistive robots can trigger emotions that affect their acceptance. Acceptance of robots is assumed to be greater when they look more human-like; however, too much human similarity can be counterproductive. Regarding different groups, it is assumed that older adults have a more negative attitude towards robots than younger adults. Within the framework of a simulated robot study, the aim was to investigate emotions of older adults compared to students towards robots with different appearances and in different situations and so contribute to a deeper view of the emotions influencing acceptance. Methods: In a questionnaire study, vignettes were used to assess emotions toward robots in different situations and of different appearance. The vignettes were composed of two situations (service and care) shown by video and four pictures of robots varying in human similarity (machine-like to android). The combination of the vignettes was randomly distributed to the participants. One hundred forty-two older adults and 35 bachelor students of nursing participated. They filled out a questionnaire that surveyed 30 positive and 30 negative emotions. For each group, older adults and students, a sum score of “positive emotions” and a sum score of “negative emotions” was calculated. Mean value, standard deviation, or n for sample size and % for frequencies, according to the scale level, were calculated. For differences in the scores of positive and negative emotions for different situations, t-tests were calculated. Results: Overall, older adults reported significantly more positive emotions than students towards robots in general. Students reported significantly more negative emotions than older adults. Regarding the two different situations, the results were similar for the care situation, with older adults reporting more positive emotions than students and less negative emotions than students. In the service situation, older adults reported significantly more positive emotions; negative emotions did not differ significantly from the students. Regarding the appearance of the robot, there were no significant differences in emotions reported towards the machine-like, the mechanical-human-like and the human-like appearance. Regarding the android robot, students reported significantly more negative emotions than older adults. Conclusion: There were differences in the emotions reported by older adults compared to students. Older adults reported more positive emotions, and students reported more negative emotions towards robots in different situations and with different appearances. It can be assumed that older adults have a different attitude towards the use of robots than younger people, especially young adults in the health sector. Therefore, the use of robots in the service or care sector should not be rejected rashly based on the attitudes of younger persons, without considering the attitudes of older adults equally.

Keywords: emotions, robots, seniors, young adults

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Elena Puica

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This paper aims to investigate the impact of Supply Chain Management (SCM) on the environment by applying a Machine Learning model while pointing out the efficiency of the technology used. The Machine Learning model was used to derive the efficiency and optimization of technology used in SCM and the environmental impact of SCM processes. The model applied is a predictive classification model and was trained firstly to determine which stage of the SCM has more outputs and secondly to demonstrate the efficiency of using advanced technology in SCM instead of recuring to traditional SCM. The outputs are the emissions generated in the environment, the consumption from different steps in the life cycle, the resulting pollutants/wastes emitted, and all the releases to air, land, and water. This manuscript presents an innovative approach to applying advanced technology in SCM and simultaneously studies the efficiency of technology and the SCM's impact on the environment. Identifying the conceptual relationships between SCM practices and their impact on the environment is a new contribution to the research. The authors can take a forward step in developing recent studies in SCM and its effects on the environment by applying technology.

Keywords: machine-learning model in SCM, SCM processes, SCM and the environmental impact, technology in SCM

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Authors: O. Gubnitsyna, O. Zakoretskaya, O. Russova

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It is generally recognized that the main task of modern society is human development. The quality of human capital has been identified as a key driver of economic development in the region. In this article, considered the quality of human capital as one of the main types of social and economic potential for the region’s development. The phenomenon of human capital represents both material and intellectual components of human activity. It is show that the necessary population characterized by certain quantitative and qualitative indicators (qualification and professional structure, education or social general condition and others) and is an necessary resource for the development of the regional economy. The connection of the regional goals with the quality of human capital is discussed in the article and a number of recommendations on its improvement were given. Solving the tasks stated in the article, the authors used analytical and statistical methods of research, scientific publications of domestic and foreign scientists on this issue. The results can be used in this implementation of the concept of regional development.

Keywords: human capital, the quality of human capital, economic development, social general condition

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Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

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Authors: Parameshwaran Nair

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Filling and packaging lines for bottling of beverages into glass, PET or aluminum containers require specialized expertise and a different configuration of equipment like – Filler, Warmer, Labeller, Crater/Recrater, Shrink Packer, Carton Erector, Carton Sealer, Date Coder, Palletizer, etc. Over the period of time, the packaging industry has evolved from manually operated single station machines to highly automized high-speed lines. Human factor and ergonomics have gained significant consideration in this course of transformation. A pre-requisite for such bottling lines, irrespective of the container type and size, is to be suitable for multi-format applications. It should also be able to handle format changeovers with minimal adjustment. It should have variable capacity and speeds, for providing great flexibility of use in managing accumulation times as a function of production characteristics. In terms of layout as well, it should demonstrate flexibility for operator movement and access to machine areas for maintenance. Packaging technology during the past few decades has risen to these challenges by a series of major breakthroughs interspersed with periods of refinement and improvement. The milestones are many and varied and are described briefly in this paper. In order to have a brief understanding of the human factor and ergonomics in the modern packaging lines, this paper, highlights the various technologies, design considerations and statutory requirements in packaging equipment for different types of containers used in India.

Keywords: human-factor, ergonomics, bottling lines, automized high-speed lines

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Authors: Farshad Badie

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The central focus of this article is on the fact that knowledge is constructed from an interaction between humans’ experiences and over their conceptions of constructed concepts. Logical characterisation of ‘human inductive learning over human’s constructed concepts’ within terminological systems and providing a logical background for theorising over the Human Concept Learning Problem (HCLP) in terminological systems are the main contributions of this research. This research connects with the topics ‘human learning’, ‘epistemology’, ‘cognitive modelling’, ‘knowledge representation’ and ‘ontological reasoning’.

Keywords: human concept learning, concept construction, knowledge construction, terminological systems

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Authors: Nonso Ochinanwata, Patrick Oseloka Ezepue

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This paper explores the process of creating and capturing born-global firm opportunities. It reviews the key constructs that underpin the establishment of born-global firms in sub-Saharan Africa. These include entrepreneurial orientation, resources and capabilities, collaboration, and contextual influences. The paper discusses how individuals and entrepreneurs in sub-Saharan Africa can establish home-based born-global firms that seek early international markets from inception. The paper suggests that sub-Saharan African governments should make a favourable microeconomics policy that will enable entrepreneurs and firms to acquire some certain minimal resources and capabilities, in order to develop global products and services.

Keywords: born global-firms, collaboration, internationalisation, dynamic capabilities, entrepreneurship, sub-Saharan Africa

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Authors: J. K. Alhassan, B. Attah, S. Misra

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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: artificial neural network, classification, decision tree algorithms, diabetes mellitus

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Authors: Orestis Κ. Efthymiou, Stavros T. Ponis

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In the last decade, a new industrial revolution seems to be emerging, supported -once again- by the rapid advancements of Information Technology in the areas of Machine-to-Machine (M2M) communication permitting large numbers of intelligent devices, e.g. sensors to communicate with each other and take decisions without any or minimum indirect human intervention. The advent of these technologies have triggered the emergence of a new category of hybrid (cyber-physical) manufacturing systems, combining advanced manufacturing techniques with innovative M2M applications based on the Internet of Things (IoT), under the umbrella term Industry 4.0. Even though the topic of Industry 4.0 has attracted much attention during the last few years, the attempts of providing a systematic literature review of the subject are scarce. In this paper, we present the authors’ initial study of the field with a special focus on the use and applications of Industry 4.0 principles in material handling automations and in-house logistics. Research shows that despite the vivid discussion and attractiveness of the subject, there are still many challenges and issues that have to be addressed before Industry 4.0 becomes standardized and widely applicable.

Keywords: Industry 4.0, internet of things, manufacturing systems, material handling, logistics

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Authors: Robin Lok-Wang Ma

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The Center/Unit for Industry Engagement and Collaboration, as well as Internship, play a significant role at university. In general, the Center serves as the official interface between the industry and the School or Department to cultivate students’ early exposure to professional experience. The missions of the Center are not limited to provide a communication channel and collaborative platform for the industries and the university but also to assist students to build up their career paths early while still in the university. In recent years, a cooperative education experience (commonly known as a co-op) has been strongly advocated for students to make the school-to-work transition. The nature of the co-op program is not only consistent with the internships/final year design projects, but it is also more industrial-oriented with academic support from faculty at the university. The purpose of this paper is to describe an approach to how cooperative education experience can be integrated into Engineering Curriculum. It provides a mutual understanding and exchange of ideas for the approach between the university and the industry. A suggested format in terms of timeline, duration, selection of candidates, students, and companies’ expectations for the co-op program is described. Also, feedbacks from employers/industries show that a longer-term co-op program is well suited for students compared with a short-term internship. To this end, it provides a new insight into collaboration and/or partnership between the university and the industries to prepare professional work-ready graduates.

Keywords: cooperative education, industry, engagement, collaboration

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Authors: Jian Wang, Lu Yang, Jiong Peng

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Internal circulation two-platen clamping system for injection molding machine (IMM) has many potential advantages on energy-saving. In order to estimate its properties, experiments in this paper were carried out. Displacement and pressure of the components were measured. In comparison, the model of hydraulic clamping system was established by using AMESim. The related parameters as well as the energy consumption could be calculated. According to the analysis, the hydraulic system was optimized in order to reduce the energy consumption.

Keywords: AMESim, energy-saving, injection molding machine, internal circulation

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Authors: Kang-Mo Jung

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Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers.

Keywords: least absolute deviation, quadratic programming, robustness, support vector machine, weight

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Authors: Ratun Rahman, Rafid Islam, Akin Ahmed, Kamrul Hasan, Hasan Mahmud

Abstract:

Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and TensorFlow Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. The test set consists of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications), each consisting of 948 features (permissions). Using the permission-based dataset, the machine learning algorithms then produce accuracy rates above 80%, except the Naive Bayes Algorithm with 65% accuracy. Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.

Keywords: android malware detection, machine learning, malware, malware analysis

Procedia PDF Downloads 128

Authors: Saba Ebrahimi, Hedieh Ashrafi

Abstract:

Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

Procedia PDF Downloads 184

Authors: Nicholas Milburn, Yu Liang, Dalei Wu

Abstract:

Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

Procedia PDF Downloads 122