Search results for: threat intelligence

Authors: Taju Abdela Mohammed

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Introduction: There has been a rise in awareness of violence against young women and girls, particularly when it occurs in educational environments. Gender-based violence in schools is a significant barrier. Therefore, it would be a threat to the achievement of the sustainable development goals, strive for gender equality in all our programs, right from the planning stages, to make sure we are as equitable as possible. Research on the causes, attitudes, and perceptions of gender-based violence was scant. Furthermore, there aren't many studies done on female students attending private colleges. Thus, the purpose of this study is to evaluate the frequency of gender-based violence and related variables among female students attending private colleges in Harar City, Ethiopia. Methodology: A facility-based mixed method concurrent triangulation study design was conducted among 500 randomly selected Private college female students in Harar City. Self-administered questionnaire and an in-depth interview were used to collect the data. The collected data were cleaned and analyzed using a statistical package for social science. Descriptive statistics were conducted and the results were reported using frequency, and percentile. Bivariate logistic regression was performed to identify associated factors. Adjusted odds ratios with 95% confidence intervals and p values < 0.05 were used to explain statistically significant associations. Thematic analysis was used to manually translate, transcribe, and analyze qualitative data. Result: The study showed the prevalence of gender-based violence was 338 (67.6%) (CI 0.432–0.721) Private college female students in Harar city Administration. Age less than 20 years and 20–24 years, [AOR = 0.21, 95% CI (0.03–0.81)] and [AOR = 0.12, 95% CI (0.03–0.51)], tight family control, [AOR = 5.12, 95% CI (1.43–6.9)], Witnessed father abuse mother at childhood; [AOR = 4.04, 95% CI (1.36–12.1)], had drunkenness female or boyfriend; [AOR = 2.12, 95% CI (1.60–14.05)] had significant association with gender-based violence. Conclusion: Our study shows the prevalence of gender-based violence among Private college female students is significant. This is due to the fact that gender-based violence, such as school dropout, unintended pregnancy, abortion, STDs, and psychological disorders, is abandoning young girls' lives and lowering their productivity.

Keywords: female students, gender-violence, harar, Ethiopia

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Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

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The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

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Authors: Chun Chen Yea, Wen Huei Chou

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Many chemical changes in the atmosphere and the ocean are invisible to the naked eye, but they have profound impacts. These changes not only confirm the phenomenon of global carbon pollution, but also forewarn that more changes are coming. The carbon dioxide gases emitted from the burning of fossil fuels dissolve into the ocean and chemically react with seawater to form carbonic acid, which increases the acidity of the originally alkaline seawater. This gradual acidification is occurring at an unprecedented rate and will affect the effective formation of carapace of some marine organisms such as corals and crustaceans, which are almost entirely composed of calcium carbonate. The carapace of these organisms will become more dissoluble. Acidified seawater not only threatens the survival of marine life, but also negatively impacts the global ecosystem via the food chain. Faced with the threat of ocean acidification, all humans are duty-bound. The industrial sector outputs the highest level of carbon dioxide emissions in Taiwan, and the petrochemical industry is the major contributor. Ever since the construction of Formosa Plastics Group's No. 6 Naphtha Cracker Plant in Yunlin County, there have been many environmental concerns such as air pollution and carbon dioxide emission. The marine life along the coast of Yunlin is directly affected by ocean acidification arising from the carbon emissions. Societal change demands our willingness to act, which is what social advocacy promotes. This study uses digital storytelling for social advocacy and ocean acidification as the subject of a visual narrative in visualization to demonstrate the subsequent promotion of social advocacy. Storytelling can transform dull knowledge into an engaging narrative of the crisis faced by marine life. Digital dissemination is an effective social-work practice. The visualization promoting awareness on ocean acidification disseminated via social media platforms, such as Facebook and Instagram. Social media enables users to compose their own messages and share information across different platforms, which helps disseminate the core message of social advocacy.

Keywords: digital storytelling, visualization, ocean acidification, social advocacy

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Authors: Aarnav Singh, Jatin Moolchandani

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The proposed research work aims to build a query-based AI chatbot that can answer any question related to any topic. A chatbot is software that converses with users via text messages. In the proposed system, we aim to build a chatbot that generates a response based on the user’s query. For this, we use natural language processing to analyze the query and some set of texts to form a concise answer. The texts are obtained through web-scrapping and filtering all the credible sources from a web search. The objective of this project is to provide a chatbot that is able to provide simple and accurate answers without the user having to read through a large number of articles and websites. Creating an AI chatbot that can answer a variety of user questions on a variety of topics is the goal of the proposed research project. This chatbot uses natural language processing to comprehend user inquiries and provides succinct responses by examining a collection of writings that were scraped from the internet. The texts are carefully selected from reliable websites that are found via internet searches. This project aims to provide users with a chatbot that provides clear and precise responses, removing the need to go through several articles and web pages in great detail. In addition to exploring the reasons for their broad acceptance and their usefulness across many industries, this article offers an overview of the interest in chatbots throughout the world.

Keywords: Chatbot, Artificial Intelligence, natural language processing, web scrapping

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Authors: Jaeseo Lim, Jooyong Park

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Although there is ample evidence that student involvement enhances learning, college education is still mainly centered on lectures. However, in recent years, the effectiveness of discussions and the use of collective intelligence have attracted considerable attention. This study intends to examine the empirical effects of discussions on learning outcomes in various conditions. Eighty eight college students participated in the study and were randomly assigned to three groups. Group 1 was told to review material after a lecture, as in a traditional lecture-centered class. Students were given time to review the material for themselves after watching the lecture in a video clip. Group 2 participated in a discussion in groups of three or four after watching the lecture. Group 3 participated in a discussion after studying on their own. Unlike the previous two groups, students in Group 3 did not watch the lecture. The participants in the three groups were tested after studying. The test questions consisted of memorization problems, comprehension problems, and application problems. The results showed that the groups where students participated in discussions had significantly higher test scores. Moreover, the group where students studied on their own did better than that where students watched a lecture. Thus discussions are shown to be effective for enhancing learning. In particular, discussions seem to play a role in preparing students to solve application problems. This is a preliminary study and other age groups and various academic subjects need to be examined in order to generalize these findings. We also plan to investigate what kind of support is needed to facilitate discussions.

Keywords: discussions, education, learning, lecture, test

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Antoifi Abdoulhalik, Ashraf Ahmed

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Saltwater intrusion (SWI) in coastal aquifers has become a growing threat for many countries around the world. While various control measures have been suggested to mitigate SWI, the construction of subsurface physical barriers remains one of the most effective solutions for this problem. In this work, we used laboratory experiments and numerical simulations to investigate the effectiveness of subsurface dams in heterogeneous layered coastal aquifer with different layering patterns. Four different cases were investigated, including a homogeneous (case H), and three heterogeneous cases in which a low permeability (K) layer was set in the top part of the system (case LH), in the middle part of the system (case HLH) and the bottom part of the system (case HL). Automated image analysis technique was implemented to quantify the main SWI parameters under high spatial and temporal resolution. The method also provides transient salt concentration maps, allowing for the first time clear visualization of the spillage of saline water over the dam (advancing wedge condition) as well as the flushing of residual saline water from the freshwater area (receding wedge condition). The SEAWAT code was adopted for the numerical simulations. The results show that the presence of an overlying layer of low permeability enhanced the ability of the dam to retain the saline water. In such conditions, the rate of saline water spillage and inland extension may considerably be reduced. Conversely, the presence of an underlying low K layer led to a faster increase of saltwater volume on the seaward side of the wall, therefore considerably facilitating the spillage. The results showed that a complete removal of the residual saline water eventually occurred in all the investigated scenarios, with a rate of removal strongly affected by the hydraulic conductivity of the lower part of the aquifer. The data showed that the addition of the underlying low K layer in case HL caused the complete flushing to be almost twice longer than in the homogeneous scenario.

Keywords: heterogeneous coastal aquifers, laboratory experiments, physical barriers, seawater intrusion control

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Authors: Tugce Duygu Koksal

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In recent years, given the fact that the acts of terrorism and the threat of the latter are taking place without any border and distinction, it has led the states to deal with the terrorism as a priority issue. More recently, as seen in different countries during state of emergency, the adoption of anti-terrorism measures motivated by the sole need of the prevention of terrorism targets directly the fundamental rights of individuals. Therefore, a contribution to the understanding of the value of the principle of legality is becoming more and more important nowadays. This paper aims to reflect the probable effects of the adoption of anti-terrorism measures regardless of the principle of legality, on the fundamental rights. In this respect, this paper will first discuss the margin of appreciation of the national authorities by countering terrorism, and then, the importance of the respect of the legality of the anti-terrorism measures will be examined in the light of actual examples. Indeed, one of the major findings of this study is the fact that the anti-terrorism laws and measures were taken in this framework must be subject to close scrutiny in democracies, which adopted the principle of the rule of law and respect human rights. Although the state's margin of appreciation in the field of counter-terrorism is broad, these measures which are based on the legitimate aim of a democracies’ legitimate right to protect itself against the activities of terrorist organizations should have the legal basis and be strictly required by the exigencies of the fight against terrorism. While combating terrorism, the legal basis shall only be achieved if the legal consequences of an individuals’ actions related to terrorism shall be clear and foreseeable by the individuals of a society. On the other hand, particularly during the state of emergency, the ambiguity of the law might be used to include a wide range of actions under acts of terrorism. This is becoming more dangerous where freedom of expression, freedom of the press, freedom of association and the right to information is in the substance of these actions. Disregarding the principle of legality is susceptible to create a chilling effect on the exercise of human rights, and therefore, the fight against terrorism can be transformed into a repressive regime on opponents. As a result, the efforts to counter terrorism of the national authorities irrespective of the principle of legality are susceptible to cause a transformation of the rule of law to a state of law which cannot be appreciated in a democratic society.

Keywords: anti-terrorism measures, chilling effect, predictability, the principle of legality, state of emergency

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Authors: Reza Dinarvand, Mahdi Sadeghian, Somaye Sadeghian

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Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.

Keywords: artificial neural network, clayey soil, imperialist competition algorithm, lateral bearing capacity, short pile

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Authors: Gaurav Kumar Sinha

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The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

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Authors: Zhaohan Xie, Yining Yu, Mingliang Chen

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As research has shown a convergent tendency for aversion to AI recommendation, it is imperative to find a way to promote AI usage and better harness the technology. In the context of e-commerce, this study has found evidence that people show less avoidance of algorithms when recommending search products compared to experience products. This is due to people’s different attribution of mind to AI versus humans, as suggested by mind perception theory. While people hold the belief that an algorithm owns sufficient capability to think and calculate, which makes it competent to evaluate search product attributes that can be obtained before actual use, they doubt its capability to sense and feel, which is essential for evaluating experience product attributes that must be assessed after experience in person. The result of the behavioral investigation (Study 1, N=112) validated that consumers show low purchase intention to experience products recommended by AI. Further consumer neuroscience study (Study 2, N=26) using Event-related potential (ERP) showed that consumers have a higher level of cognitive conflict when faced with AI recommended experience product as reflected by larger N2 component, while the effect disappears for search product. This research has implications for the effective employment of AI recommenders, and it extends the literature on e-commerce and marketing communication.

Keywords: algorithm recommendation, consumer behavior, e-commerce, event-related potential, experience product, search product

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Authors: Morsli Mourad, Tahiri Mohamed, Samdi Azzeddine

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Casablanca is the biggest city in Morocco. It concentrates more than 60% of the economic and industrial activity of the kingdom. Its building and public works (BTP) sector is the leading source of inert waste scattered in open areas. This inert waste is a major challenge for the city of Casablanca, as it is not properly managed, thus causing a significant nuisance for the environment and the health of the population. Hence the vision of our project is to recycle and valorize concrete waste. In this work, we present concrete results in the exploitation of this abundant and permanent deposit. Typical wastes are concrete, clay and concrete bricks, ceramic tiles, marble panels, gypsum, scrap metal, wood . The work performed included: geolocation with a combination of artificial intelligence and Google Earth, estimation of the amount of waste per site, sorting, crushing, grinding, and physicochemical characterization of the samples. Then, we proceeded to the exploitation of the types of substrates to be developed: light cement, coating, and glue for ceramics... The said products were tested and characterized by X-ray fluorescence, specific surface, resistance to bending and crushing, etc. We will present in detail the main results of our research work and also describe the specific properties of each material developed.

Keywords: déchets de démolition et des chantiers de construction, logiciels de combinaison SIG, valorisation de déchets inertes, enduits, ciment leger, casablanca

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

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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: John G. Vongas, Raghid Al Hajj

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In recent years, there has been a surge in research on empathy across disciplines ranging from management and psychology to philosophy and neuroscience. In organizational behavior, in particular, scholars have become interested in leader empathy given the rise of workplace diversity and the growing perception of leaders as managers of group emotions. It would appear that the current zeitgeist in behavioral and philosophical science is that empathy is a cornerstone of morality and that our world would be better off if only more people – and by extension, more leaders – were empathic. In spite of these claims, however, researchers have used different terminologies to explore empathy, confusing it at times with other related constructs such as emotional intelligence and compassion. Second, extant research that specifies what empathic leaders do and how their behavior affects organizational stakeholders, including themselves, does not devolve from a unifying theoretical framework. These problems plague knowledge development in this important research domain. Therefore, to the authors' best knowledge, this paper provides the first comprehensive review and synthesis of the literature on leader empathy by drawing on disparate yet complementary fields of inquiry. It clarifies empathy from other constructs and presents a theoretical model that elucidates the mechanisms by which a leader’s empathy translates into behaviors that could be either beneficial or harmful to the leaders themselves, as well as to their followers and groups. And third, it specifies the boundary conditions under which a leader’s empathy will become manifest. Finally, it suggests ways in which training could be implemented to improve empathy in practice while also remaining skeptical of its conceptualization as a moral or even effective guide in human affairs.

Keywords: compassion, empathy, leadership, group outcomes

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Authors: Zhao Xinlei

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In general, interstate relations are diverse and include economic, political, military, and diplomatic. Therefore, by analyzing the development of the relationship between Malaysia and China, we can better verify how the Hirschman effect works between small countries and great powers. This paper mainly adopts qualitative research methods and uses Malaysia's 2020-2022 political crisis as a specific case to verify the practice of the Hirschman effect between small and large countries. In particular, the interest groups in small countries that are closely related to trade with extraordinary abilities, as the primary beneficiaries in the development of trade between the two countries, although they may use their resources to a certain extent to influence the decisions of small countries towards great powers, they do not fundamentally determine the small countries' response to large countries. In this process, the relative power asymmetry between states plays a dominant role, as small states lack trust and suspicion in political diplomacy towards large states based on the perception of threat arising from the relative power asymmetry. When developing bilateral relations with large countries, small states seek practical cooperation to promote economic and trade development but become more cautious in their political ties to avoid being caught in power struggles between large states or being controlled by them. The case of Malaysia-China relations also illustrates that despite the ongoing political crisis in Malaysia, which saw the country go through the transition from (Perikatan Nasional) PN to (Barisan Nasional) BN, different governments have maintained a pragmatic and proactive economic policy towards China to reduce suspicion and mistrust between the two countries in political and diplomatic affairs, thereby enhancing cooperation and interactions between the two countries. At the same time, the Malaysian government is developing multi-dimensional foreign relations and actively participating in multilateral, regional organizations and platforms, such as those organized by the United States, to maintain a relative balance in the influence of the US and China on Malaysia.

Keywords: Hirschman effect, interest groups, Malaysia, China, bilateral relations

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Authors: Javed Sidiqi, Stephen Baezinger, Stephen Wegulo

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Wheat (Triticum aestivum L.) production continues to be challenged by foliar fungal diseases although significant improvement has been made to manage the diseases through developing resistant varieties and the fungicide use to ensure sufficient wheat is produced to meet the growing population’s need. Significant crop losses have been recorded in the history of grain production and yield losses due to fungal diseases, and the trend continues to threat food security in the world and particularly in the less developed countries. The impact of individual fungal diseases on grain yield has been studied extensively to determine crop losses. However, there is limited research available to find out the combined effects of fungal diseases on grain yield and the ways to effectively manage the diseases. Therefore, the objectives of this research were to study the effect of fungal pathogens on grain yield of pre-released winter wheat genotypes in fungicide treated and untreated plots, and to determine whether S7b gene was present in ‘Gage’ wheat as previously hypothesized. Sixty winter wheat genotypes in fungicide treated and untreated plots were studied across four environments. There was a significant effect of fungicide on grain yield consistently across four environments in three years. Fungicide treated wheat lines demonstrated (4,496 kg/ ha-1) grain yield compared to (3,147 kg/ ha-1) grain yield in untreated wheat lines indicating 43% increased grain yield due to severity of foliar fungal diseases. Furthermore, fungicide application also caused an increase in protein concentration from 153 (g kg-1) to 164 (g kg-1) in treated plots in along with test weight from 73 to 77 (kg hL-1) respectively. Gage wheat variety and ISr7b-Ra were crossed to determine presence of Sr7b in Gage. The F2 and F2:3 segregating families were screened and evaluated for stem rust resistance. The segregation of families fell within 15:1 ratio for two separate resistance genes suggesting that Sr7b segregates independently from an unknown resistance gene in Gage that needs to be characterized for its use in the future wheat breeding program to develop resistant wheat varieties.

Keywords: funicide, genetics, foliar diseases, grain

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Authors: Mostafa Mjahed

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Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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Authors: G. S. Thyagaraju, U. P. Kulkarni

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Context-aware computing refers to a general class of computing systems that can sense their physical environment, and adapt their behaviour accordingly. Context aware computing makes systems aware of situations of interest, enhances services to users, automates systems and personalizes applications. Context-aware services have been introduced into mobile devices, such as PDA and mobile phones. In this paper we are presenting a novel approaches used to realize the context aware mobile. The context aware mobile phone (CAMP) proposed in this paper senses the users situation automatically and provides user context required services. The proposed system is developed by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rough sets theory based decision table. Bayesian Network to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the decision table based rules for service recommendation. To exemplify and demonstrate the effectiveness of the proposed methods, the context aware mobile phone is tested for college campus scenario including different locations like library, class room, meeting room, administrative building and college canteen.

Keywords: context aware mobile, fuzzy logic, decision table, Bayesian probability

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Authors: Lifar M. S., Tereshchenko A. A., Bulgakov A. N., Guda S. A., Guda A. A., Soldatov A. V.

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Metal nanoparticles are widely used as heterogeneous catalysts to activate adsorbed molecules and reduce the energy barrier of the reaction. Reaction product yield depends on the interplay between elementary processes - adsorption, activation, reaction, and desorption. These processes, in turn, depend on the inlet feed concentrations, temperature, and pressure. At stationary conditions, the active surface sites may be poisoned by reaction byproducts or blocked by thermodynamically adsorbed gaseous reagents. Thus, the yield of reaction products can significantly drop. On the contrary, the dynamic control accounts for the changes in the surface properties and adjusts reaction parameters accordingly. Therefore dynamic control may be more efficient than stationary control. In this work, a reinforcement learning algorithm has been applied to control the simulation of CO oxidation on a catalyst. The policy gradient algorithm is learned to maximize the CO₂ production rate based on the CO and O₂ flows at a given time step. Nonstationary solutions were found for the regime with surface deactivation. The maximal product yield was achieved for periodic variations of the gas flows, ensuring a balance between available adsorption sites and the concentration of activated intermediates. This methodology opens a perspective for the optimization of catalytic reactions under nonstationary conditions.

Keywords: artificial intelligence, catalyst, co oxidation, reinforcement learning, dynamic control

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Authors: Yu-Lin Chen, Kimberly Koester, Marissa Raymond-Flesh, Anika Thapar, Jay Thapar

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Purpose: Effectively communicating adolescent health topics to teens and their parents is crucial. This study emphasizes critically evaluating the optimal use of artificial intelligence tools (AI), which are increasingly prevalent in disseminating health information. By fostering a deeper understanding of AI voice preference in the context of health, the research aspires to have a ripple effect, enhancing the collective health literacy and decision-making capabilities of both teenagers and their parents. This study explores AI voices' potential within health learning modules for annual well-child visits. We aim to identify preferred voice characteristics and understand factors influencing perceived trustworthiness, ultimately aiming to improve health literacy and decision-making in both demographics. Methods: A cross-sectional study assessed preferences and trust perceptions of AI voices in learning modules among teens (11-18) and their parents/guardians in Northern California. The study involved the development of four distinct learning modules covering various adolescent health-related topics, including general communication, sexual and reproductive health communication, parental monitoring, and well-child check-ups. Participants were asked to evaluate eight AI voices across the modules, considering a set of six factors such as intelligibility, naturalness, prosody, social impression, trustworthiness, and overall appeal, using Likert scales ranging from 1 to 10 (the higher, the better). They were also asked to select their preferred choice of voice for each module. Descriptive statistics summarized participant demographics. Chi-square/t-tests explored differences in voice preferences between groups. Regression models identified factors impacting the perceived trustworthiness of the top-selected voice per module. Results: Data from 104 participants (teen=63; adult guardian = 41) were included in the analysis. The mean age is 14.9 for teens (54% male) and 41.9 for the parent/guardian (12% male). At the same time, similar voice quality ratings were observed across groups, and preferences varied by topic. For instance, in general communication, teens leaned towards young female voices, while parents preferred mature female tones. Interestingly, this trend reversed for parental monitoring, with teens favoring mature male voices and parents opting for mature female ones. Both groups, however, converged on mature female voices for sexual and reproductive health topics. Beyond preferences, the study delved into factors influencing perceived trustworthiness. Interestingly, social impression and sound appeal emerged as the most significant contributors across all modules, jointly explaining 71-75% of the variance in trustworthiness ratings. Conclusion: The study emphasizes the importance of catering AI voices to specific audiences and topics. Social impression and sound appeal emerged as critical factors influencing perceived trustworthiness across all modules. These findings highlight the need to tailor AI voices by age and the specific health information being delivered. Ensuring AI voices resonate with both teens and their parents can foster their engagement and trust, ultimately leading to improved health literacy and decision-making for both groups. Limitations and future research: This study lays the groundwork for understanding AI voice preferences for teenagers and their parents in healthcare settings. However, limitations exist. The sample represents a specific geographic location, and cultural variations might influence preferences. Additionally, the modules focused on topics related to well-child visits, and preferences might differ for more sensitive health topics. Future research should explore these limitations and investigate the long-term impact of AI voice on user engagement, health outcomes, and health behaviors.

Keywords: artificial intelligence, trustworthiness, voice, adolescent

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Arth Bohra, Marwa Mahmoud

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In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

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Authors: Hanadi Mubarak Al-Mubaraki, Michael Busler

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Innovation is the powerful tools for economic growth and diversification, which lead to smart growth. The objective of this paper is to identify the opportunities and challenges of innovation programs discuss and analyse the implementation of the innovation program in the United States (US) and United Kingdom (UK). To achieve the objectives, the research used a mixed methods approach, quantitative (survey), and qualitative (multi-case study) to examine innovation best practices in developed countries. In addition, the selection of 4 interview case studies of innovation organisations based on the best practices and successful implementation worldwide. The research findings indicated the two challenges such as 1) innovation required business ecosystem support to deliver innovation outcomes such as new product and new services, and 2) foster the climate of innovation &entrepreneurship for economic growth and diversification. Although the two opportunities such as 1) sustainability of the innovation events which lead smart growth, and 2) establish the for fostering the artificial intelligence hub entrepreneurship networking at multi-levels. The research adds value to academicians and practitioners such as government, funded organizations, institutions, and policymakers. The authors aim to conduct future research a comparative study of innovation case studies between developed and developing countries for policy implications worldwide. The Originality of This study contributes to current literature about the innovation best practice in developed and developing countries.

Keywords: economic development, technology transfer, entrepreneurship, innovation program

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Authors: Olumuyiwa Ojo, Masengo Ilunga

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Artificial neural network (ANN) has broken the bounds of the convention programming, which is actually a function of garbage in garbage out by its ability to mimic the human brain. Its ability to adopt, adapt, adjust, evaluate, learn and recognize the relationship, behavior, and pattern of a series of data set administered to it, is tailored after the human reasoning and learning mechanism. Thus, the study aimed at modeling wastewater treatment process in order to accurately diagnose water control problems for effective treatment. For this study, a stage ANN model development and evaluation methodology were employed. The source data analysis stage involved a statistical analysis of the data used in modeling in the model development stage, candidate ANN architecture development and then evaluated using a historical data set. The model was developed using historical data obtained from Daspoort Wastewater Treatment plant South Africa. The resultant designed dimensions and model for wastewater treatment plant provided good results. Parameters considered were temperature, pH value, colour, turbidity, amount of solids and acidity. Others are total hardness, Ca hardness, Mg hardness, and chloride. This enables the ANN to handle and represent more complex problems that conventional programming is incapable of performing.

Keywords: ANN, artificial neural network, wastewater treatment, model, development

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Authors: Abdul Mannan Akhtar

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In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: Rajendra Kumar Pandey

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Forest genetic resources not only represent regional biodiversity but also have immense value as the wealth for securing livelihood of poor people. These are vulnerable to ecological due to depletion/deforestation and /or impact of climate change. These resources of various plant categories are vulnerable on the floor of natural tropical forests, and leading to the threat on the growth and development of future forests. More than 170 species, including NTFPs, are in critical condition for their survival in natural tropical forests of Central India. Forest degradation, commensurate with biodiversity loss, is now pervasive, disproportionately affecting the rural poor who directly depend on forests for their subsistence. Looking ahead the interaction between forest and water, soil, precipitation, climate change, etc. and its impact on biodiversity of tropical forests, it is inevitable to develop co-operation policies and programmes to address new emerging realities. Forests ecosystem also known as the 'wealth of poor' providing goods and ecosystem services on a sustainable basis, are now recognized as a stepping stone to move poor people beyond subsistence. Poverty alleviation is the prime objective of the Millennium Development Goals (MDGs). However, environmental sustainability including other MDGs, is essential to ensure successful elimination of poverty and well being of human society. Loss and degradation of ecosystem are the most serious threats to achieving development goals worldwide. Millennium Ecosystem Assessment (MEA, 2005) was an attempt to identify provisioning and regulating cultural and supporting ecosystem services to provide livelihood security of human beings. Climate change may have a substantial impact on ecological structure and function of forests, provisioning, regulations and management of resources which can affect sustainable flow of ecosystem services. To overcome these limitations, policy guidelines with respect to planning and consistent research strategy need to be framed for conservation and sustainable development of forest genetic resources.

Keywords: climate change, forest ecosystem services, sustainable forest management, biodiversity conservation

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Authors: Bisola Stella Sonde

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Digital innovation has emerged as a pivotal driver of business transformation in the contemporary landscape. This case study research explores the dynamic interplay between digital innovation and the profound metamorphosis of businesses across industries. It delves into the multifaceted dimensions of digital innovation, elucidating its impact on organizational structures, customer experiences, and operational paradigms. The study investigates real-world instances of businesses harnessing digital technologies to enhance their competitiveness, agility, and sustainability. It scrutinizes the strategic adoption of digital platforms, data analytics, artificial intelligence, and emerging technologies as catalysts for transformative change. The cases encompass a diverse spectrum of industries, spanning from traditional enterprises to disruptive startups, offering insights into the universal relevance of digital innovation. Moreover, the research scrutinizes the challenges and opportunities posed by the digital era, shedding light on the intricacies of managing cultural shifts, data privacy, and cybersecurity concerns in the pursuit of innovation. It unveils the strategies that organizations employ to adapt, thrive, and lead in the era of digital disruption. In summary, this case study research underscores the imperative of embracing digital innovation as a cornerstone of business transformation. It offers a comprehensive exploration of the contemporary digital landscape, offering valuable lessons for organizations striving to navigate the ever-evolving terrain of the digital age.

Keywords: business transformation, digital innovation, emerging technologies, organizational structures

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