Search results for: artificial animal intelligence

Authors: Shital Patil, Surendra Bhosale

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Cardiac diseases are the leading causes of mortality and morbidity in the world, from recent few decades accounting for a large number of deaths have emerged as the most life-threatening disorder globally. Machine learning and Artificial intelligence have been playing key role in predicting the heart diseases. A relevant set of feature can be very helpful in predicting the disease accurately. In this study, we proposed a comparative analysis of 4 different features selection methods and evaluated their performance with both raw (Unbalanced dataset) and sampled (Balanced) dataset. The publicly available Z-Alizadeh Sani dataset have been used for this study. Four feature selection methods: Data Analysis, minimum Redundancy maximum Relevance (mRMR), Recursive Feature Elimination (RFE), Chi-squared are used in this study. These methods are tested with 8 different classification models to get the best accuracy possible. Using balanced and unbalanced dataset, the study shows promising results in terms of various performance metrics in accurately predicting heart disease. Experimental results obtained by the proposed method with the raw data obtains maximum AUC of 100%, maximum F1 score of 94%, maximum Recall of 98%, maximum Precision of 93%. While with the balanced dataset obtained results are, maximum AUC of 100%, F1-score 95%, maximum Recall of 95%, maximum Precision of 97%.

Keywords: cardio vascular diseases, machine learning, feature selection, SMOTE

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Authors: Pawit Monkolprasit, Proud Arunrangsiwed

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It was important to understand the impact of media in young adolescents. The animated film, Khun Tong Dang the Inspirations (2015), was purposefully created for teaching young children to have a positive personal trait. The current study used this film as the case study. The objective is to understand the relationship between the good characteristic of movie audiences and their perception of the good characteristic of a movie character. One-hundred students from various age ranges responded to quantitative questionnaires. The questions included their age, gender, perception about their own personal traits, perception about their experiences with others, and perception about the bravery, intelligence, and gratefulness of the character. It was found that a good personal trait has a strong relationship with the perception of bravery, intelligence, and gratefulness of the character.

Keywords: impact of media, children, personal trait, prosocial content

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Authors: Habtemariam Alemu

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It has been known that the performance of position and force control is highly affected by both robot dynamic and environment stiffness uncertainties. In this paper, joint space hybrid force and position control strategy with self-selecting matrix using artificial neural network compensator is proposed. The objective of the work is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. Simulation results for a 6 degree of freedom (6-DoF) manipulator and different types of environments showed the effectiveness of the suggested approach. 6-DoF Puma 560 family robot manipulator is chosen as industrial robot and its efficient dynamic model is designed using Matlab/SimMechanics library.

Keywords: robot manipulator, force/position control, artificial neural network, Matlab/Simulink

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Authors: P. Karimi, A. H. Khedmati Bazkiaei

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The main goal of this study is to test differential neural network as a controller of smart structure and is to enumerate its advantages and disadvantages in comparison with other controllers. In this study, the smart structure has been considered as a Euler Bernoulli cantilever beam and it has been tried that it be under control with the use of vibration neural network resulting from movement. Also, a linear observer has been considered as a reference controller and has been compared its results. The considered vibration charts and the controlled state have been recounted in the final part of this text. The obtained result show that neural observer has better performance in comparison to the implemented linear observer.

Keywords: smart material, on-line differential artificial neural network, active control, finite element method

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Authors: Sheila S. Silang

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This research seeks to make an impact analysis on multiple intelligence as the basis for differentiated classroom instruction in TLE. It will also address the felt need of how TLE subject could be taught effectively exhausting all the possible means.This study seek the effect of giving different instruction according to the ability of the students in the following objectives: 1. student’s technological skills enhancement, 2. learning potential improvements 3. having better linkage between school and community in a need for soliciting different learning devices and materials for the learner’s academic progress. General Luna, Quezon is composed of twenty seven barangays. There are only two public high schools. We are aware that K-12 curriculum is focused on providing sufficient time for mastery of concepts and skills, develop lifelong learners, and prepare graduates for tertiary education, middle-level skills development, employment, and entrepreneurship. The challenge is with TLE offerring a vast area of specializations, how would Multiple Intelligence play its vital role as basis in classroom instruction in acquiring the requirement of the said curriculum? 1.To what extent do the respondent students manifest the following types of intelligences: Visual-Spatial, Body-Kinesthetic, Musical, Interpersonal, Intrapersonal, Verbal-Linguistic, Logical-Mathematical and Naturalistic. What media should be used appropriate to the student’s learning style? Visual, Printed Words, Sound, Motion, Color or Realia 3. What is the impact of multiple intelligence as basis for differentiated instruction in T.L.E. based on the following student’s ability? Learning Characteristic and Reading Ability and Performance 3. To what extent do the intelligences of the student relate with their academic performance? The following were the findings derived from the study: In consideration of the vast areas of study of TLE, and the importance it plays in the school curriculum coinciding with the expectation of turning students to technologically competent contributing members of the society, either in the field of Technical/Vocational Expertise or Entrepreneurial based competencies, as well as the government’s concern for it, we visualize TLE classroom teachers making use of multiple intelligence as basis for differentiated classroom instruction in teaching the subject .Somehow, multiple intelligence sample such as Linguistic, Logical-Mathematical, Bodily-Kinesthetic, Interpersonal, Intrapersonal, and Spatial abilities that an individual student may have or may not have, can be a basis for a TLE teacher’s instructional method or design.

Keywords: education, multiple, differentiated classroom instruction, impact analysis

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Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

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The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.

Keywords: melanoma, convolutional neural network, momentum, batch hyperparameter

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Authors: Ali Abdelnabi, Nermeen Hamza

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Coral calcium is a boasting natural product and dietary supplement which is considered a source of alkaline calcium carbonate, this study is a comparative study, comparing the remineralization effect of the new product of coral calcium with that of nano-hydroxyapatite. Methodology: a total of 35 extracted molars were collected, examined and sectioned to obtain 70 sound enamel discs, all discs were numbered and examined by scanning electron microscope coupled with Energy Dispersive Analysis of X-rays(EDAX) for mineral content, subjected to artificial caries, and mineral content was re-measured, discs were divided into seven groups according to the remineralizing agent used, where groups 1 to 3 used 10%, 20%, 30% nanohydroxyapatite gel respectively, groups 4 to 6 used 10%, 20%, 30% coral calcium gel and group 7 with no remineralizing agent (control group). All groups were re-examined by EDAX after remineralization; data were calculated and tabulated. Results: All groups showed a statistically significant drop in calcium level after artificial caries; all groups showed a statistically significant rise in calcium content after remineralization except for the control group; groups 1 and 5 showed the highest increase in calcium level after remineralization. Conclusion: coral calcium can be considered a comparative product to nano-hydroxyapatite regarding the remineralization of enamel initial carious lesions.

Keywords: artificial caries, coral calcium, nanohydroxyapatite, re-mineralization

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Authors: Ibrahim Makram Ibrahim Salib

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Agriculture plays an essential role in providing food for the world's population. It also offers numerous benefits to countries, including non-food products, transportation, and environmental balance. Precision agriculture, which employs advanced tools to monitor variability and manage inputs, can help achieve these benefits. The increasing demand for food security puts pressure on decision-makers to ensure sufficient food production worldwide. To support sustainable agriculture, unmanned aerial vehicles (UAVs) can be utilized to manage farms and increase yields. This paper aims to provide an understanding of UAV usage and its applications in agriculture. The objective is to review the various applications of UAVs in agriculture. Based on a comprehensive review of existing research, it was found that different sensors provide varying analyses for agriculture applications. Therefore, the purpose of the project must be determined before using UAV technology for better data quality and analysis. In conclusion, identifying a suitable sensor and UAV is crucial to gather accurate data and precise analysis when using UAVs in agriculture.

Keywords: agriculture land, agriculture land loss, Kabul city, urban land expansion, urbanization agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models drone, precision agriculture, farmer income

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Authors: Aurea C. L. Lacerda, Paulo R. H. Moreno, Bruna M. P. Vianna, Cristina H. R. Serra, Airton Martin, André R. Baby, Vladi O. Consiglieri, Telma M. Kaneko

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The modified Franz cell is the most widely used model for in vitro permeation studies, however it still presents some disadvantages. Thus, some alternative methods have been developed such as Skin PAMPA, which is a bio- artificial membrane that has been applied for skin penetration estimation of xenobiotics based on HT permeability model consisting. Skin PAMPA greatest advantage is to carry out more tests, in a fast and inexpensive way. The membrane system mimics the stratum corneum characteristics, which is the primary skin barrier. The barrier properties are given by corneocytes embedded in a multilamellar lipid matrix. This layer is the main penetration route through the paracellular permeation pathway and it consists of a mixture of cholesterol, ceramides, and fatty acids as the dominant components. However, there is no consensus on the membrane composition. The objective of this work was to compare the performance among different bio-artificial membranes for studying the permeation in skin PAMPA system. Material and methods: In order to mimetize the lipid composition`s present in the human stratum corneum six membranes were developed. The membrane composition was equimolar mixture of cholesterol, ceramides 1-O-C18:1, C22, and C20, plus fatty acids C20 and C24. The membrane integrity assay was based on the transport of Brilliant Cresyl Blue, which has a low permeability; and Lucifer Yellow with very poor permeability and should effectively be completely rejected. The membrane characterization was performed using Confocal Laser Raman Spectroscopy, using stabilized laser at 785 nm with 10 second integration time and 2 accumulations. The membrane behaviour results on the PAMPA system were statistically evaluated and all of the compositions have shown integrity and permeability. The confocal Raman spectra were obtained in the region of 800-1200 cm-1 that is associated with the C-C stretches of the carbon scaffold from the stratum corneum lipids showed similar pattern for all the membranes. The ceramides, long chain fatty acids and cholesterol in equimolar ratio permitted to obtain lipid mixtures with self-organization capability, similar to that occurring into the stratum corneum. Conclusion: The artificial biological membranes studied for Skin PAMPA showed to be similar and with comparable properties to the stratum corneum.

Keywords: bio-artificial membranes, comparison, confocal Raman, skin PAMPA

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Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

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Authors: Mirna Febriani

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The use of stainless steel wires in the field of dentistry is widely used, especially for orthodontic and prosthodontic treatment using stainless steel wire. The oral cavity is the ideal environment for corrosion, which can be caused by saliva. Prevention of corrosion on stainless steel wires can be done by using an organic or non-organic corrosion inhibitor. One of the organic inhibitors that can be used to prevent corrosion is the leaves of breadfruit. The method used for this research using Atomic Absorption Spectrophotometric test. The results showed that the difference of chromium ion releases on soaking in saliva and breadfruit leaf extracts on days 1, 3, 7 and 14. Statically calculation with independent T-test with p < 0,05 showed the significant difference. The conclusion of this study shows that breadfruit leaf extract can inhibit the corrosion rate of stainless steel wires.

Keywords: chromium ion, stainless steel, artificial saliva, breadfruit leaf

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Authors: Niamh Higgins, Dawn Howard

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The Irish agri-food sector is of major importance to Ireland’s manufacturing sector and to the Irish economy through employment and the exporting of animal products worldwide. Infectious diseases and parasites have an impact on farm animal health causing profitability and productivity to be affected. For the sustainability of Irish dairy farming, there must be the highest standard of animal health. There can be a lack of information in accounting for > 1% of complete microbial diversity in an environment. There is the tendency of culture-based methods of microbial identification to overestimate the prevalence of species which grow easily on an agar surface. There is a need for new technologies to address these issues to assist with animal health. Metagenomic approaches provide information on both the whole genome and transcriptome present through DNA sequencing of total DNA from environmental samples producing high determination of functional and taxonomic information. Nanopore Next Generation Technologies have the ability to be powerful sequencing technologies. They provide high throughput, low material requirements and produce ultra-long reads, simplifying the experimental process. The aim of this study is to use a metagenomics approach to analyze dairy cattle faecal samples using the Oxford Nanopore MinION Next Generation Sequencer and to establish an in-house pipeline for metagenomic characterization of complex samples. Faecal samples will be obtained from Irish dairy farms, DNA extracted and the MinION will be used for sequencing, followed by bioinformatics analysis. Of particular interest, will be the parasite Buxtonella sulcata, which there has been little research on and which there is no research on its presence on Irish dairy farms. Preliminary results have shown the ability of the MinION to produce hundreds of reads in a relatively short time frame of eight hours. The faecal samples were obtained from 90 dairy cows on a Galway farm. The results from Oxford Nanopore ‘What’s in my pot’ (WIMP) using the Epi2me workflow, show that from a total of 926 classified reads, 87% were from the Kingdom Bacteria, 10% were from the Kingdom Eukaryota, 3% were from the Kingdom Archaea and < 1% were from the Kingdom Viruses. The most prevalent bacteria were those from the Genus Acholeplasma (71 reads), Bacteroides (35 reads), Clostridium (33 reads), Acinetobacter (20 reads). The most prevalent species present were those from the Genus Acholeplasma and included Acholeplasma laidlawii (39 reads) and Acholeplasma brassicae (26 reads). The preliminary results show the ability of the MinION for the identification of microorganisms to species level coming from a complex sample. With ongoing optimization of the pipe-line, the number of classified reads are likely to increase. Metagenomics has the potential in animal health for diagnostics of microorganisms present on farms. This would support wprevention rather than a cure approach as is outlined in the DAFMs National Farmed Animal Health Strategy 2017-2022.

Keywords: animal health, buxtonella sulcata, infectious disease, irish dairy cattle, metagenomics, minION, next generation sequencing

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Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of machine learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. With the application of learning methods in such diverse domains, artificial intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been on developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and three defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt machine learning techniques in security-critical areas such as the nuclear industry without rigorous testing since they may be vulnerable to adversarial attacks. While common defence methods can effectively defend against different attacks, none of the three considered can provide protection against all five adversarial attacks analysed.

Keywords: adversarial machine learning, attacks, defences, nuclear industry, crack detection

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Authors: Seyed Sadegh Naseralavi, Najmeh Bemani

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In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.

Keywords: adaptive neuro fuzzy inference system, anticipate method, artificial neural network, concrete design code, multi-variable regression

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Authors: M. Meddi, A. Boufekane

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The Mitidja coastal groundwater which extends over an area of 1450 km2 is a strategic resource in the Algiers region. The high dependence of the regional economy on the use of this groundwater forces us to have recourse to its artificial recharge from the Wadi El Harrach in its upstream part. This system of artificial recharge has shown its effectiveness in the development of water resource mentioned in the succeeding works in several regions of the world. The objective of this study is to: Increase the reserves of water inputs by infiltration, raise the water level and its good quality in wells and boreholes, reduce losses to the sea, and address seawater intrusion by maintaining balance in the freshwater-saltwater interface in the downstream part of the groundwater basin. After analyzing the situation, it was noticed that a qualitative monitoring of the Wadi water for the groundwater recharge has to be done. For this purpose, we proceeded during three successive years (2010, 2011, and 2012) to the monthly sampling of water in the upstream part of the Wadi El Harrach for chemical analysis. The variation of the sediment transport concentration will be also measured. This monitoring aims to characterize the water quality and avoid clogging in the proposed recharge area. The results of these analyses showed the good chemical quality according to the analyses we performed in the laboratory during the three years, but they are too loaded with suspended matters. We noticed that these fine particles come from the grinding of limestone of sandpit located upstream of the area of the proposed recharge system. This problem can be solved by a water supply upstream of sandpit. For the recharge, we propose the method of using two wells for dual use, which means that it can be used for water supply and extraction. This solution is inexpensive in our case and could easily be used as wells are already drilled in the upstream part. This solution increases over time the piezometric level and also reduce groundwater contamination by saltwater in the downstream part.

Keywords: water quality, artificial groundwater recharge, Mitidja, North Algeria

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Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: C. Fernanda da S. Sampaio, M. Gabriela Sadith Perez Paredes, V. Antonio de O. Martins

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Today the transport sector is undergoing a revolution, with the rise of Advanced Driver Assistance Systems (ADAS), industry and society itself will undergo a major transformation. However, the technological development of these applications is a challenge that requires new techniques and great machine learning and artificial intelligence. The study proposes to develop a vehicular perception system called Veibra, which consists of two front cameras for day/night viewing and an embedded device capable of working with Yolov2 image processing algorithms with low computational cost. The strategic version for the market is to assist the driver on the road with the detection of day/night objects, such as road signs, pedestrians, and animals that will be viewed through the screen of the phone or tablet through an application. The system has the ability to perform real-time driver detection and recognition to identify muscle movements and pupils to determine if the driver is tired or inattentive, analyzing the student's characteristic change and following the subtle movements of the whole face and issuing alerts through beta waves to ensure the concentration and attention of the driver. The system will also be able to perform tracking and monitoring through GSM (Global System for Mobile Communications) technology and the cameras installed in the vehicle.

Keywords: advanced driver assistance systems, tracking, traffic signal detection, vehicle perception system

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: Yooseo Pang, Jongwoong Choi, Yong Cho, Yongchae Jeong

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Climate crisis, such as environmental problems related to energy supply, is getting emerged issues, so the use of renewable energy is essentially required to solve these problems, which are mainly managed by the Paris Agreement, the international treaty on climate change. The government of the Republic of Korea announced that the key long-term goal for a low-carbon strategy is “Carbon neutrality by 2050”. It is focused on the role of the internet data centers (IDC) in which large amounts of data, such as artificial intelligence (AI) and big data as an impact of the 4th industrial revolution, are managed. The demand for the cooling system market for IDC was about 9 billion US dollars in 2020, and 15.6% growth a year is expected in Korea. It is important to control the temperature in IDC with an efficient air conditioning system, so hydrothermal energy is one of the best options for saving energy in the cooling system. In order to save energy and optimize the operating conditions, it has been considered to apply ‘the dam deep water air conditioning system. Deep water at a specific level from the dam can supply constant water temperature year-round. It will be tested & analyzed the amount of energy saving with a pilot plant that has 100RT cooling capacity. Also, a target of this project is 1.2 PUE (Power Usage Effectiveness) which is the key parameter to check the efficiency of the cooling system.

Keywords: hydrothermal energy, HVAC, internet data center, free-cooling

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Authors: Zhi-Jun Lu, Qi Lu, Meng Wu, Qian Xiang, Jun Gu

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The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.

Keywords: artificial neural network (ANN), finite element method (FEM), perforated sections, thin-walled Steel, ultimate load

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Authors: Georges Bou Ghantous

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This literature review examines the transformative impact of Artificial Intelligence (AI) on higher education, highlighting both the potential benefits and challenges associated with its adoption. The review reveals that AI significantly enhances personalized learning by tailoring educational experiences to individual student needs, thereby boosting engagement and learning outcomes. Automated grading systems streamline assessment processes, allowing educators to focus on improving instructional quality and student interaction. AI's data-driven insights provide valuable analytics, helping educators identify trends in at-risk students and refine teaching strategies. Moreover, AI promotes enhanced instructional innovation through the adoption of advanced teaching methods and technologies, enriching the educational environment. Administrative efficiency is also improved as AI automates routine tasks, freeing up time for educators to engage in research and curriculum development. However, the review also addresses the challenges that accompany AI integration, such as data privacy concerns, algorithmic bias, dependency on technology, reduced human interaction, and ethical dilemmas. This balanced exploration underscores the need for careful consideration of both the advantages and potential hurdles in the implementation of AI in higher education.

Keywords: administrative efficiency, data-driven insights, data privacy, ethical dilemmas, higher education, personalized learning

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Authors: Piret Pernik

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Based on strategic, operational, and technical analysis of the ongoing armed conflict in Ukraine, this paper will examine the opportunities and risks of using small commercial drones (dual-use unmanned aerial vehicles, UAV) for military purposes. The paper discusses the opportunities and risks in the information domain, encompassing both cyber and electromagnetic interference and attacks. The paper will draw conclusions on a possible strategic impact to the battlefield outcomes in the modern armed conflicts by the widespread use of dual-use UAVs. This article will contribute to filling the gap in the literature by examining based on empirical data cyberattacks and electromagnetic interference. Today, more than one hundred states and non-state actors possess UAVs ranging from low cost commodity models, widely are dual-use, available and affordable to anyone, to high-cost combat UAVs (UCAV) with lethal kinetic strike capabilities, which can be enhanced with Artificial Intelligence (AI) and Machine Learning (ML). Dual-use UAVs have been used by various actors for intelligence, reconnaissance, surveillance, situational awareness, geolocation, and kinetic targeting. Thus they function as force multipliers enabling kinetic and electronic warfare attacks and provide comparative and asymmetric operational and tactical advances. Some go as far as argue that automated (or semi-automated) systems can change the character of warfare, while others observe that the use of small drones has not changed the balance of power or battlefield outcomes. UAVs give considerable opportunities for commanders, for example, because they can be operated without GPS navigation, makes them less vulnerable and dependent on satellite communications. They can and have been used to conduct cyberattacks, electromagnetic interference, and kinetic attacks. However, they are highly vulnerable to those attacks themselves. So far, strategic studies, literature, and expert commentary have overlooked cybersecurity and electronic interference dimension of the use of dual use UAVs. The studies that link technical analysis of opportunities and risks with strategic battlefield outcomes is missing. It is expected that dual use commercial UAV proliferation in armed and hybrid conflicts will continue and accelerate in the future. Therefore, it is important to understand specific opportunities and risks related to the crowdsourced use of dual-use UAVs, which can have kinetic effects. Technical countermeasures to protect UAVs differ depending on a type of UAV (small, midsize, large, stealth combat), and this paper will offer a unique analysis of small UAVs both from the view of opportunities and risks for commanders and other actors in armed conflict.

Keywords: dual-use technology, cyber attacks, electromagnetic warfare, case studies of cyberattacks in armed conflicts

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Authors: Z. Zerdoumi, D. Benatia, , D. Chicouche

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This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart.

Keywords: Artificial Neural Network, signal restoration, Nonlinear Channel equalization, equalization

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Authors: Pedro G. Rodrigues, Maria J. Bárrios, Sara A. Ambrósio

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The digitalisation of health is a profoundly transformative economic, political, and social process. As is often the case, such processes need to be carefully managed if misunderstandings, policy misalignments, or outright conflicts between the government and a wide gamut of stakeholders with competing interests are to be avoided. Thus, ensuring open lines of communication where all parties know what each other’s concerns are is key to good governance, as well as efficient and effective policymaking. This project aims to make a small but still significant contribution in this regard in that we seek to determine the extent to which health managers’ perceptions of what is a priority for digital health in Portugal are aligned with official policy perspectives. By applying state-of-the-art artificial intelligence technology first to the indexed literature on digital health and then to a set of official policy documents on the same topic, followed by a survey directed at health managers working in public and private hospitals in Portugal, we obtain two priority rankings that, when compared, will allow us to produce a synthesis and toolkit on digital health policy in Portugal, with a view to identifying areas of policy convergence and divergence. This project is also particularly peculiar in the sense that sophisticated digital methods related to text analytics are employed to study good governance aspects of digitalisation applied to health care.

Keywords: digital health, health informatics, text analytics, governance, natural language understanding

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

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The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: Wasan Ismail Khalil, Hisham Khalid Ahmed, Zainab Ali

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Structural Lightweight Aggregate Concrete (SLWAC) has been developed in recent years because it reduces the dead load, cost, thermal conductivity and coefficient of thermal expansion of the structure. So SLWAC has the advantage of being a relatively green building material. Lightweight Aggregate (LWA) is either occurs as natural material such as pumice, scoria, etc. or as artificial material produced from different raw materials such as expanded shale, clay, slate, etc. The use of SLWAC in Iraq is limited due to the lack in natural LWA. The existence of Iraqi clay deposit with different types and characteristics leads to the idea of producing artificial expanded clay aggregate. The main aim in this work is to present of the properties of artificial LWA produced in the laboratory. Available local bentonite clay which occurs in the Western region of Iraq was used as raw material to produce the LWA. Sodium silicate as liquid industrial waste material from glass plant was mixed with bentonite clay in mix proportion 1:1 by weight. The manufacturing method of the lightweight aggregate including, preparation and mixing of clay and sodium silicate, burning of the mixture in the furnace at the temperature between 750-800˚C for two hours, and finally gradually cooling process. The produced LWA was then crushed to small pieces then screened on standard sieve series and prepared with grading which conforms to the specifications of LWA. The maximum aggregate size used in this investigation is 10 mm. The chemical composition and the physical properties of the produced LWA are investigated. The results indicate that the specific gravity of the produced LWA is 1.5 with the density of 543kg/m3 and water absorption of 20.7% which is in conformity with the international standard of LWA. Many trail mixes were carried out in order to produce LWAC containing the artificial LWA produced in this research. The selected mix proportion is 1:1.5:2 (cement: sand: aggregate) by weight with water to cement ratio of 0.45. The experimental results show that LWAC has oven dry density of 1720 kg/m3, water absorption of 8.5%, the thermal conductivity of 0.723 W/m.K and compressive strength of 23 N/mm2. The SLWAC produced in this research can be used in the construction of different thermal insulated buildings and masonry units. It can be concluded that the SLWA produced in this study contributes to sustainable development by, using industrial waste materials, conserving energy, enhancing the thermal and structural efficiency of concrete.

Keywords: expanded clay, lightweight aggregate, structural lightweight aggregate concrete, sustainable

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Authors: Franco Ceasar Agbalog, Shintaro Yukawa

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Mindfulness is increasingly being used as a method for resolving conflict. However, less is known about how its positive outcome develops. To better understand the underlying effects of mindfulness on conflict resolution strategies, this study examines the potential mediating factors between them. The researchers hypothesized that Emotional Intelligence (EI) mediates the effects of mindfulness on conflict resolution strategies due to its similar components to the benefits of mindfulness, such as awareness and control of one’s emotions, awareness and understanding of other’s emotions, and cultivation of compassion and empathy. Using a random sampling, 157 participants completed three questionnaires: Five Facet Mindfulness Questionnaire (FFMQ), Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), and Rahim Organizational Conflict Inventory-II (ROCI-II). Utilizing the SPSS Process, results showed a significant relationship between mindfulness and EI. However, among the five approaches to conflict resolution, only the integrating style was significantly related to EI. Following the principle of Mediation Analysis, mindfulness has an indirect effect on integrating style. Moreover, mindfulness and conflict resolution strategies were not significantly related. This is a rather surprising result because research literature has always indicated a positive relationship between the two variables. These findings imply that although integrating style is generally considered the best approach in handling conflict, each style may be appropriate depending on the situation. Mindfulness allows practitioners to have a holistic view of the conflict situation and choose the approach they think best for that specific situation. This could explain why statistically, there is no direct effect of mindfulness on conflict resolution strategies. This work provides basis for the necessity to investigate the factors of conflict instead of the conflict resolution strategies; factors that can be manipulated and may be directly influenced by mindfulness.

Keywords: conflict resolution strategies, emotional intelligence, mindfulness and conflict, ROCI-II integrating style

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: Angela Oktavia Suryani, Bernadeth Gloria, Edwin Sutamto, Jessica Kristianty, Ni Made Rai Sapitri, Patricia Catherine Agla, Sitti Arlinda Rochiadi

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It was found that some blogs or personal websites in Indonesia sell standardized intelligence tests (for example, Progressive Matrices (PM), Intelligence Structure Test (IST), and Culture Fair Intelligence Test (CFIT)) and other psychological tests, together with the manual and the key answers for public. Individuals can buy and prepare themselves for selection or recruitment with the real test. This action drives people to lie to the institution (education or company) and also to themselves. It was also found that those tests are old. Some items are not relevant with the current context, for example a question about a diameter of a certain coin that does not exist anymore. These problems motivate us to develop a new intelligence battery test, namely of Multiple Aptitude Battery (MAB). The battery test was built by using Thurstone’s Primary Mental Abilities theory and intended to be used by high schools students, university students, and worker applicants. The battery tests consist of 9 subtests. In the current study we examine six subtests, namely Reading Comprehension, Verbal Analogies, Numerical Inductive Reasoning, Numerical Deductive Reasoning, Mechanical Ability, and Two Dimensional Spatial Reasoning for university students. The study included 1424 data from students recruited by convenience sampling from eight faculties at Atma Jaya Catholic University of Indonesia. Classical and modern test approaches (Item Response Theory) were carried out to identify the item difficulties of the items and confirmatory factor analysis was applied to examine their internal validities. The validity of each subtest was inspected by using convergent–discriminant method, whereas the reliability was examined by implementing Kuder–Richardson formula. The result showed that the majority of the subtests were difficult in medium level, and there was only one subtest categorized as easy, namely Verbal Analogies. The items were found homogenous and valid measuring their constructs; however at the level of subtests, the construct validity examined by convergent-discriminant method indicated that the subtests were not unidimensional. It means they were not only measuring their own constructs but also other construct. Three of the subtests were able to predict academic performance with small effect size, namely Reading Comprehension, Numerical Inductive Reasoning, and Two Dimensional Spatial Reasoning. GPAs in intermediate level (GPAs at third semester and above) were considered as a factor for predictive invalidity. The Kuder-Richardson formula showed that the reliability coefficients for both numerical reasoning subtests and spatial reasoning were superior, in the range 0.84 – 0.87, whereas the reliability coefficient for the other three subtests were relatively below standard for ability test, in the range of 0.65 – 0.71. It can be concluded that some of the subtests are ready to be used, whereas some others are still need some revisions. This study also demonstrated that the convergent-discrimination method is useful to identify the general intelligence of human.

Keywords: intelligence, psychometric examination, multiple aptitude battery, university students

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Authors: Mateusz Janas

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Innovative implementations in the micro, small, and medium-sized enterprises (MSME) sector are among the essential activities considering the current market realities, technological advancements, and digitization trends. MSMEs play a crucial role and significantly influence the economic conditions of countries, as their competitiveness directly impacts the global economy. Business development and investment in innovation and technology are integral parts of every modern enterprise's strategy, seeking to maintain and achieve a desired competitive position. The instability of the socio-economic environment, along with contemporary changes in artificial intelligence implementation and digitization, requires businesses to adopt increasingly newer solutions and actions. Enterprises must strive to survive in the global market and build competitive positions, especially in uncertain conditions. Being aware of the significance of innovative actions is crucial for MSMEs as it enables them to enhance their operations and expand their scope. It is essential for managers and executives of MSMEs to be focused on development and innovation, as their approach will also impact their employees, emphasizing results and maximizing the company's value. Managers of MSMEs must be aware of various threats, costs, opportunities, and gains that can arise from implementing new technical and organizational solutions. Businesses must view development as an integral part of their strategy and continuously strive for improvement.

Keywords: innovation, SME, develop, management

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