Search results for: motor intelligence

Authors: Shinoy Vengaramkode Bhaskaran, Kaushik Sathupadi, Sandesh Achar

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Human locomotion recognition is important in a variety of sectors, such as robotics, security, healthcare, fitness tracking and cloud computing. With the increasing pervasiveness of peripheral devices, particularly Inertial Measurement Units (IMUs) sensors, researchers have attempted to exploit these advancements in order to precisely and efficiently identify and categorize human activities. This research paper introduces a state-of-the-art methodology for the recognition of human locomotion patterns in a cloud environment. The methodology is based on a publicly available benchmark dataset. The investigation implements a denoising and windowing strategy to deal with the unprocessed data. Next, feature extraction is adopted to abstract the main cues from the data. The SelectKBest strategy is used to abstract optimal features from the data. Furthermore, state-of-the-art ML classifiers are used to evaluate the performance of the system, including logistic regression, random forest, gradient boosting and SVM have been investigated to accomplish precise locomotion classification. Finally, a detailed comparative analysis of results is presented to reveal the performance of recognition models.

Keywords: artificial intelligence, cloud computing, IoT, human locomotion, gradient boosting, random forest, neural networks, body-worn sensors

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Authors: Jarmila Štěpánová, Martin Kudláček, Lukáš Jakubec

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The people with spinal cord injury are one of the least sport active members of our society. Their hypoactivity is determined by primary injury, i.e., the loss of motor function, the injured part of the body is connected with health complications and social handicap. Study performs one part of the standardization process of semi-structured interview PARA-SCI.CZ (Czech version of the Physical Activity Recall Assessment for People with Spinal Cord Injury), which measures the type, frequency, duration, and intensity of physical activity of people with spinal cord injury. The study focused on persons with paraplegia who use a wheelchair as their primary mode of mobility. The aim of this study was to perform a process to determine the criterion validity of PARA-SCI.CZ. The actual physical activity of wheelchair users was monitored during three days by using accelerometers Actigraph GT3X fixed on the non-dominant wrist, and semi-structured interview PARA-SCI.CZ. During the PARA-SCI.CZ interview, participants were asked to recall activities they had done over the past 3 days, starting with the previous day. PARA-SCI.CZ captured frequency, duration, and intensity (low, moderate, and heavy) of two categories of physical activity (leisure time physical activity and activities of a usual day). Accelerometer Actigraph GT3X captured duration and intensity (low and moderate + heavy) of physical activity during three days and nights. The study presented three potential recalculations of measured data. Standardization process of PARA-SCI.CZ is essential to critically approach issues of health and active lifestyle of persons with spinal cord injury in the Czech Republic. Standardized PARA-SCI.CZ can be used in practice by physiotherapists and sports pedagogues from the field of adapted physical activities.

Keywords: physical activity, lifestyle, paraplegia, semi-structure interview, accelerometer

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Authors: Karam Aziz Hamdy Fahmy

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Although human rights protection in the industrial sector has increased, human rights violations continue to occur. Although the government has passed human rights laws, labor laws, and an international treaty ratified by the United States, human rights crimes continue to occur and go undetected. The growing number of textile companies in Bekasi is also leading to an increase in human rights violations as the government has no obligation to protect them. The United States government and business leaders should respect, protect and defend the human rights of workers. The article discusses the human rights violations faced by garment factory workers in the context of the law, as well as ideas for improving the protection of workers' rights. The connection between development and human rights has long been the subject of academic debate. Therefore, to understand the dynamics between these two concepts, a number of principles have been adopted, ranging from the right to development to a human rights-based approach to development. Despite these attempts, the precise connection between development and human rights is not yet fully understood. However, the inherent interdependence between these two concepts and the idea that development efforts must respect human rights guarantees has gained momentum in recent years. It will then be examined whether the right to sustainable development is recognized.

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

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Authors: Yong Min You

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The study on the torque ripple of Permanent Magnet Synchronous Motors (PMSMs) has been rapidly progressed, which effects on the noise and vibration of the electric vehicle. There are several ways to reduce torque ripple, which are the increase in the number of slots and poles, the notch of the rotor and stator teeth, and the skew of the rotor and stator. However, the conventional methods have the disadvantage in terms of material cost and productivity. The demagnetization characteristic of PMSMs must be attained for electric vehicle application. Due to rare earth supply issue, the demand for Dy-free permanent magnet has been increasing, which can be applied to PMSMs for the electric vehicle. Dy-free permanent magnet has lower the coercivity; the demagnetization characteristic has become more significant. To improve the torque ripple as well as the demagnetization characteristics, which are significant parameters for electric vehicle application, an unequal air-gap model is proposed for a PMSM. A shape optimization is performed to optimize the design variables of an unequal air-gap model. Optimal design variables are the shape of an unequal air-gap and the angle between V-shape magnets. An optimization process is performed by Latin Hypercube Sampling (LHS), Kriging Method, and Genetic Algorithm (GA). Finite element analysis (FEA) is also utilized to analyze the torque and demagnetization characteristics. The torque ripple and the demagnetization temperature of the initial model of 45kW PMSM with unequal air-gap are 10 % and 146.8 degrees, respectively, which are reaching a critical level for electric vehicle application. Therefore, the unequal air-gap model is proposed, and then an optimization process is conducted. Compared to the initial model, the torque ripple of the optimized unequal air-gap model was reduced by 7.7 %. In addition, the demagnetization temperature of the optimized model was also increased by 1.8 % while maintaining the efficiency. From these results, a shape optimized unequal air-gap PMSM has shown the usefulness of an improvement in the torque ripple and demagnetization temperature for the electric vehicle.

Keywords: permanent magnet synchronous motor, optimal design, finite element method, torque ripple

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Authors: Jamal Almatawah, Hamad Matar, Abdulsalam Altemeemi

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The World Health Organization (WHO) has recognized environmental noise as harmful pollution that causes adverse psychosocial and physiologic effects on human health. The motor vehicle is considered to be one of the main source of noise pollution. It is a universal phenomenon, and it has grown to the point that it has become a major concern for both the public and policymakers. The aim of this paper, therefore, is to investigate the Traffic noise levels and the contributing factors that affect its level, such as traffic volume, heavy-vehicle Speed and other metrological factors in Ishbiliyah as a sample of a residential area in Kuwait. Three types of roads were selected in Ishbiliyah expressway, major arterial and collector street. The other source of noise that interferes the traffic noise has also been considered in this study. Traffic noise level is measured and analyzed using the Bruel & Kjaer outdoor sound level meter 2250-L (2250 Light). The Count-Cam2 Video Camera has been used to collect the peak and off-peak traffic count. Ambient Weather WM-5 Handheld Weather Station is used for metrological factors such as temperature, humidity and wind speed. Also, the spot speed was obtained using the radar speed: Decatur Genesis model GHD-KPH. All the measurement has been detected at the same time (simultaneously). The results showed that the traffic noise level is over the allowable limit on all types of roads. The average equivalent noise level (LAeq) for the Expressway, Major arterial and Collector Street was 74.3 dB(A), 70.47 dB(A) and 60.84 dB(A), respectively. In addition, a Positive Correlation coefficient between the traffic noise versus traffic volume and between traffic noise versus 85th percentile speed was obtained. However, there was no significant relation and Metrological factors. Abnormal vehicle noise due to poor maintenance or user-enhanced exhaust noise was found to be one of the highest factors that affected the overall traffic noise reading.

Keywords: traffic noise, residential area, pollution, vehicle noise

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Authors: Kanwar Varinder Pal Singh

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Art is one of the five secondary sciences next to the social sciences. As per the single essential concept in garment design, it is the coexistence and co-creation of two aspects of reality: Ultimate reality and apparent or conventional reality. All phenomena possess two natures: That which is revealed by correct perception and that which is induced by deceptive perception. The object of correct perception is the ultimate reality, the object of deceptive perception is conventional reality. The same phenomenon, therefore, may be perceived according to its ultimate nature or its apparent nature. Ultimate reality is also called ‘emptiness’. Emptiness does not mean that all phenomena are nothing but do not exist in themselves. Although phenomena, the universe, thoughts, beings, time, and so on, seem very real in themselves, ultimately, they are not. Each one of us can perceive the changing and unpredictable nature of existence. This transitory nature of phenomena, impermanence, is the first sign of emptiness. Sometimes, the interdependence of phenomena leads to ultimate reality, which is nothing but emptiness, e.g., a rainbow, which is an effect due to the function of ‘sun rays,’ ‘rain,’ and ‘time.’ In light of the above, to achieve decision-making for the global desirability of garment design, the coexistence of artistic and technical creativity must achieve an object of correct perception, i.e., ultimate reality. This paper mentions the decision-making technique as semiotic engineering, both subjective and objective.

Keywords: global desirability, social desirability, comfort desirability, handle desirability, overall desirability

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Authors: Abanoub Rady Shaker Saleb

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Nowadays, design and architecture are being affected and underwent change with the rapid advancements in technology, economics, politics, society and culture. Architecture has been transforming with the latest developments after the inclusion of computers into design. Integration of design into the computational environment has revolutionized the architecture and new perspectives in architecture have been gained. The history of architecture shows the various technological developments and changes in which the architecture has transformed with time. Therefore, the analysis of integration between technology and the history of the architectural process makes it possible to build a consensus on the idea of how architecture is to proceed. In this study, each period that occurs with the integration of technology into architecture is addressed within historical process. At the same time, changes in architecture via technology are identified as important milestones and predictions with regards to the future of architecture have been determined. Developments and changes in technology and the use of technology in architecture within years are analyzed in charts and graphs comparatively. The historical process of architecture and its transformation via technology are supported with detailed literature review and they are consolidated with the examination of focal points of 20th-century architecture under the titles; parametric design, genetic architecture, simulation, and biomimicry. It is concluded that with the historical research between past and present; the developments in architecture cannot keep up with the advancements in technology and recent developments in technology overshadow the architecture, even the technology decides the direction of architecture. As a result, a scenario is presented with regards to the reach of technology in the future of architecture and the role of the architect.

Keywords: design and development the information technology architecture, enterprise architecture, enterprise architecture design result, TOGAF architecture development method (ADM)

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Authors: D. Anderlini, G. Wallis

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Background: The number of people experiencing stroke is steadily increasing due to changes in diet and lifestyle, to longer life expectancy resulting in older population, to higher survival rates as a consequence of improvements during the acute phase. This study considers what risk factors might contribute to delayed entry to hospital for treatment. Methods: We analyzed data from 2472 patients admitted to the Stroke Unit of the Royal Brisbane Women's Hospital, Australia, between 2002 to 2011. Results: Previous studies have reported that factors which can contribute to delay include the patient’s age, the time of day, physical location, visit the GP instead of going to the emergency, means of transport, severity of symptoms and type of stroke. Contrary to findings of other studies, we found a strong correlation between side of lesion and delay in admission: patients with right hemisphere lesions had an average delay of 3.78 days, while patients with left hemisphere lesions had an average delay of 1.49 days. Damage to the right hemisphere generally ends in motor impairment in the non-dominant hand and no speech impediment. In contrast, left hemisphere lesions can result in deficit to; dominant hand function and aphasia which will be noticed even if their impact on performance is relatively minor. A finding which goes against many previous studies, is the fact that women get to the hospital much sooner than men, showing an average delay of 0.92 days in women vs. 3.36 days in men. Conclusion: Acute surgical-pharmacological therapies are most effective if applied immediately after stroke. Hence delays to admission can be crucial to the degree of recovery. The tendency of patients to overlook symptoms of right hemisphere lesion should be the target of information campaigns both for the general public and GPs. Why do men go to hospital so late? We don't know yet! Nevertheless an awareness plan specifically direct to male population should be on the agenda of Health Departments.

Keywords: gender, admission delay, stroke location, bioinformatics, biomedicine

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Authors: Tesfaye Mengistu

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This thesis aims to investigate the effectiveness of Reinforcement Learning (RL) for Autonomous Energy Management solutions. The study explores the potential of Model Free RL approaches, such as Monte Carlo RL and Q-learning, to improve energy management by autonomously adjusting energy management strategies to maximize efficiency. The research investigates the implementation of RL algorithms for optimizing energy consumption in a single-agent environment. The focus is on developing a framework for the implementation of RL algorithms, highlighting the importance of RL for enabling autonomous systems to adapt quickly to changing conditions and make decisions based on previous experiences. Moreover, the paper proposes RL as a novel energy management solution to address nations' CO2 emission goals. Reinforcement learning algorithms are well-suited to solving problems with sequential decision-making patterns and can provide accurate and immediate outputs to ease the planning and decision-making process. This research provides insights into the challenges and opportunities of using RL for energy management solutions and recommends further studies to explore its full potential. In conclusion, this study provides valuable insights into how RL can be used to improve the efficiency of energy management systems and supports the use of RL as a promising approach for developing autonomous energy management solutions in residential buildings.

Keywords: artificial intelligence, reinforcement learning, monte carlo, energy management, CO2 emission

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Authors: Sruthi Ramagiri, Rajeev T.

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Free radical damage has been entailed as the major culprit in the ischemic stroke contributing for oxidative damage. Recent investigations on Hydroxy Safflower Yellow A (HSYA) suggested its role in cerebral ischemia and various neurodegenerative disorders with unidentified molecular mechanisms. The current study was designed to investigate putative therapeutic role and possible molecular mechanisms of HSYA administration during the onset of reperfusion in cerebral ischemia-reperfusion (I/R) injury in cerebral stroke. Cerebral stroke was achieved by focal ischemic model. HSYA (10 mg/kg) was injected intravenously via the tail vein 5 minutes before reperfusion. Losses of sensorimotor abilities were evaluated by neurological scoring, spontaneous locomotor activity, and rotarod performance. Extent of oxidative stress was evaluated by biochemical parameters i.e., malondialdehyde (MDA), Glutathione (GSH), Super Oxide Dismutase (SOD) and catalase levels. The infarct volume of brain was assessed by 2,3,5-triphenyl tetrazolium chloride (TTC) staining technique. Increased cerebral injury (I/R) was evidenced by motor impairment, increased infarct volume and elevation of MDA levels along with significant reduction in antioxidant i.e.,MDA levels along with significant reduction in antioxidant i.e., GSH, SOD and catalase levels when compared to sham control. However, post conditioning with HSYA (10 mg/kg, i.v.) at the onset of reperfusion has significantly ameliorated sensorimotor abilities, attenuated MDA levels and reduced the infarct volume as compared with vehicle treated I/R injury group. Moreover, HSYA treatments improved antioxidant enzyme levels as compared with vehicle treated I/R-injury group. In conclusion, it may be suggested that HSYA post conditioning could be novel therapeutic approach against I/R injury in cerebral stroke possibly through its anti-oxidant mechanism.

Keywords: HSYA, Ischemia reperfusion injury, oxidative stress, stroke

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Authors: Catalina Oana Dumitrescu, Andreea L. Drugau-constantin

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In the digital age, public administration is facing profound change, fueled by technological advances and the growing demands of citizens for efficient, accessible and transparent services. This paper explores how new digital technologies – including artificial intelligence, blockchain, big data and e-governance solutions – are reshaping the functioning of public administrations globally. In addition to the obvious opportunities to streamline and optimize processes, digital transformation brings with it major challenges, such as cyber security, personal data protection, resistance to change and the need to develop new skills for employees. The paper aims to provide a discussion platform for public administration experts, policy makers and technology innovators to consider how governments can balance the benefits and risks of digital transformation. Topics such as the reconfiguration of administrative processes, the creation of interoperable government systems, the involvement of citizens in public decisions through digital platforms, and solutions for reducing the digital gap between developed and developing regions will be addressed. In conclusion, the digital transformation of public administration is not only an opportunity for modernization, but also a necessity to respond to the new demands and challenges of contemporary society. This paper will provide new insights into the role of technology in improving the quality of governance and public services.

Keywords: public administration, digital ERA, technology, government systems, global

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Authors: Joud Basyouni, Rama Zagzoog, Mashael Al Faleh, Jana Alireza

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AI is changing many fields and speeding up tasks that used to take a long time. It used to take too long to edit photos. However, many AI-powered apps make photo editing, automatic effects, and animations much easier than other manual editing apps with no AI. The mobile app Photoleap edits photos and creates digital art using AI. Editing photos with text prompts is also becoming a standard these days with the help of apps like Photoleap. Now, users can change backgrounds, add animations, turn text into images, and create scenes with AI. This project report discusses the photo editing app's history and popularity. Photoleap resembles Photoshop, Canva, Photos, and Pixlr. The report includes survey questions to assess Photoleap user satisfaction. The report describes Photoleap's features and functions with screenshots. Photoleap uses AI well. Charts and graphs show Photoleap user ratings and comments from the survey. This project found that most Photoleap users liked how well it worked, was made, and was easy to use. People liked changing photos and adding backgrounds. Users can create stunning photo animations. A few users dislike the app's animations, AI art, and photo effects. The project report discusses the app's pros and cons and offers improvements.

Keywords: artificial intelligence, photoleap, images, background, photo editing

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Authors: Annu Panghal, Bimla Dhanda

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This research paper aims to determine the genetic-environment influences on the cognitive abilities of twins. Using the 100 pairs of twins from two districts, namely: Bhiwani (N = 90) and Hisar (N = 110) of Haryana State, genetic and environmental influences were assessed in twin study design. The cognitive abilities of twins were measured using the Wechsler Intelligence Scale for Children (WISC-R). Home Observation for Measurement of the Environment (HOME) Inventory was taken to examine the home environment of twins. Heritability estimate was used to analyze the genes contributing to shape the cognitive abilities of twins. The heritability estimates for cognitive abilities of 6-7 years old twins in Hisar district were 74% and in Bhiwani District 76%. Further the heritability estimates were 64% in the twins of Hisar district and 60 in Bhiwani district % in the age group of 7-8 years. The remaining variations in the cognitive abilities of twins were due to environmental factors namely: provision for Active Stimulation, paternal involvement, safe physical environment. The findings provide robust evidence that the cognitive abilities were more influenced by genes than the environmental factors and also revealed that the influence of genetic was more in the age group 6-7 years than the age group 7-8 years. The conclusion of the heritability estimates indicates that the genetic influence was more in the age group of 6-7 years than the age group of 7-8 years. As the age increases the genetic influence decreases and environment influence increases. Mother education was strongly associated with the cognitive abilities of twins.

Keywords: genetics, heritability, twins, environment, cognitive abilities

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Authors: Frankie Burgos, Emely Munar, Conrado Basa

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This paper aimed at developing an artificial neural network (ANN) model specifically for the Philippine Stock Exchange index closing value. The inputs to the ANN are US Dollar and Philippine Peso(USD-PHP) exchange rate, GDP growth of the country, quarterly inflation rate, 10-year bond yield, credit rating of the country, previous open, high, low, close values and volume of trade of the Philippine Stock Exchange Index (PSEi), gold price of the previous day, National Association of Securities Dealers Automated Quotations (NASDAQ), Standard and Poor’s 500 (S & P 500) and the iShares MSCI Philippines ETF (EPHE) previous closing value. The target is composed of the closing value of the PSEi during the 627 trading days from November 3, 2011, to May 30, 2014. MATLAB’s Neural Network toolbox was employed to create, train and simulate the network using multi-layer feed forward neural network with back-propagation algorithm. The results satisfactorily show that the neural network developed has the ability to model the PSEi, which is affected by both internal and external economic factors. It was found out that the inputs used are the main factors that influence the movement of the PSEi closing value.

Keywords: artificial neural networks, artificial intelligence, philippine stocks exchange index, stocks trading

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Authors: Nafe Moradkhani, Frederick Benaben, Benoit Montreuil, Ali Vatankhah Barenji, Dima Nazzal

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In the context of the global pandemic, the practical management of the 2020 presidential election in the USA was a strong concern. To anticipate and prepare for this election accurately, one of the main challenges was to confront (i) forecasts of voter turnout, (ii) capacities of the facilities and, (iii) potential configuration options of resources. The approach chosen to conduct this anticipative study consists of collecting data about forecasts and using simulation models to work simultaneously on resource allocation and facility configuration of polling places in Fulton County, Georgia’s largest county. A polling place is a dedicated facility where voters cast their ballots in elections using different devices. This article presents the results of the simulations of such places facing pre-identified potential risks. These results are oriented towards the efficiency of these places according to different criteria (health, trust, comfort). Then a dynamic framework is introduced to describe risks as physical forces perturbing the efficiency of the observed system. Finally, the main benefits and contributions resulting from this simulation campaign are presented.

Keywords: performance, decision support, simulation, artificial intelligence, risk management, election, pandemics, information system

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Authors: Felix Olajide Ibikunle

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Introductory Statement: Sustainable participation of teenagers in sports requires deliberate and concerted plans and managerial policy rooted in the “philosophy of catch them young.” At risk, teenagers need proper integration into societal aspiration: This direction will go a long way to streamline them into security breaches and attractive nuisance free lifestyles. Basic Methodology: The population consists of children between 13-19 years old. A proportionate sampling size technique of 60% was adopted to select seven zones out of 11 geo-political zones in the Ibadan metropolis. Qualitative information and interview were used to collect needed information. The majority of the teenagers were out of school, street hawkers, motor pack touts and unserious vocation apprentices. These groups have the potential for security breaches in the metropolis and beyond. Five hundred and thirty-four (534) respondents were used for the study. They were drawn from Ojoo, Akingbile and Moniya axis = 72; Agbowo, Ajibode and Apete axis = 74; Akobo, Basorun and Idi-ape axis 79; Wofun, Monatan and Iyana-Church axis = 78; Molete, Oke-ado and Oke-Bola axis = 75; Beere, Odinjo, Elekuro axis = 77; Eleyele, Ologuneru and Alesinloye axis = 79. Major Findings: Multiple regression was used to analyze the independent variables and percentages. The respondents' average age was 15.6 years old, and 100% were male. The instrument (questionnaire) used yielded; sport preference (r = 0.72), intervention (r = 0.68), and sustainable participation (r = 0.70). The relative contributions of sport preference on the participation of at risk teenagers was (F-ratio = 1.067); Intervention contribution of sport on the participation of at risk teenagers = produced (F-ratio of 12.095) was significant while, sustainable participation of at risk teenagers produced (F-ratio = 1.062) was significant. Closing Statement: The respondents’ sport preference stimulated their participation in sports. The intervention exposed at risk-teenagers to coaching, which activated their interest and participation in sports. At the same time, sustainable participation contributed positively to evolving at risk teenagers' participation in their preferred sport.

Keywords: sport, preference, intervention, teenagers, sustainable, participation and risk teenagers

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Authors: Walid Moudani

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The Crew Reserve Assignment Problem (CRAP) considers the assignment of the crew members to a set of reserve activities covering all the scheduled flights in order to ensure a continuous plan so that operations costs are minimized while its solution must meet hard constraints resulting from the safety regulations of Civil Aviation as well as from the airlines internal agreements. The problem considered in this study is of highest interest for airlines and may have important consequences on the service quality and on the economic return of the operations. In this communication, a new mathematical formulation for the CRAP is proposed which takes into account the regulations and the internal agreements. While current solutions make use of Artificial Intelligence techniques run on main frame computers, a low cost approach is proposed to provide on-line efficient solutions to face perturbed operating conditions. The proposed solution method uses a dynamic programming approach for the duties scheduling problem and when applied to the case of a medium airline while providing efficient solutions, shows good potential acceptability by the operations staff. This optimization scheme can then be considered as the core of an on-line Decision Support System for crew reserve assignment operations management.

Keywords: airlines operations management, combinatorial optimization, dynamic programming, crew scheduling

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Authors: Rorlinda Yusof, Siti Aishah Hassan, Afifah Mohamad Radzi, Mohd Hakimie Zainal Abidin, Amran Rasli, Inderbir Sandhu

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The purpose of this study is to examine the self-identities of gifted and talented students and the relationship between self-identity and academic accomplishment. A random sample of 300 students enrolled in a secondary education programme at the Pusat GENIUS@pintar Negara was chosen as respondents of a 151-item holistic-identity component development tool. The validity of the instrument was assessed using Principal Components Analysis and Factor Analysis via an inter-Item Correlation Matrix (Loading values 0.44 to 0.86), which resulted in the formation of eight dimensions. The Cronbach's Alpha was calculated to determine the instrument's reliability (the overall result was 0.98). The results showed that students' holistic-identity profiles were relatively high (mean=4.09, standard deviation=0.449). In addition, spiritual identity received the greatest mean score (4.34) out of the eight components of identity investigated, while leadership identity received the lowest mean score (3.88). A conceptual framework for Islamic school leadership is recommended to implement spiritual values without differentiation to harmonize spiritual and intellectual intelligence among all the students. Some benchmarking studies with other centres for gifted and talented students are recommended for further research.

Keywords: holistic self-identity, academic achievement, self-development programme, counselling services, gifted and talented students

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Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

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Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: Jay Ananth

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The drug discovery process currently requires numerous years of clinical testing as well as money just for a single drug to earn FDA approval. For drugs that even make it this far in the process, there is a very slim chance of receiving FDA approval, resulting in detrimental hurdles to drug accessibility. To minimize these inefficiencies, numerous studies have implemented computational methods, although few computational investigations have focused on a crucial feature of drugs: lipophilicity. Lipophilicity is a physical attribute of a compound that measures its solubility in lipids and is a determinant of drug efficacy. This project leverages Artificial Intelligence to predict the impact of a drug’s lipophilicity on its performance by accounting for factors such as binding affinity and toxicity. The model predicted lipophilicity and binding affinity in the validation set with very high R² scores of 0.921 and 0.788, respectively, while also being applicable to a variety of target receptors. The results expressed a strong positive correlation between lipophilicity and both binding affinity and toxicity. The model helps in both drug development and discovery, providing every pharmaceutical company with recommended lipophilicity levels for drug candidates as well as a rapid assessment of early-stage drugs prior to any testing, eliminating significant amounts of time and resources currently restricting drug accessibility.

Keywords: drug discovery, lipophilicity, ligand-receptor interactions, machine learning, drug development

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Authors: Joanna Cabaj, Sylwia Baluta, Karol Malecha, Kamila Drzozga

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Catecholamines are the principal neurotransmitters that mediate a variety of the central nervous system functions, such as motor control, cognition, emotion, memory processing, and endocrine modulation. Dysfunctions in catecholamine neurotransmission are induced in some neurologic and neuropsychiatric diseases. Changeable neurotransmitters level in biological fluids can be a marker of several neurological disorders. Because of its significance in analytical techniques and diagnostics, sensitive and selective detection of neurotransmitters is increasingly attracting a lot of attention in different areas of bio-analysis or biomedical research. Recently, fluorescent techniques for detection of catecholamines have attracted interests due to their reasonable cost, convenient control, as well as maneuverability in biological environments. Nevertheless, with the observed need for a sensitive and selective catecholamines sensor, the development of a convenient method for this neurotransmitter is still at its basic level. The manipulation of nanostructured materials in conjunction with biological molecules has led to the development of a new class of hybrid modified biosensors in which both enhancement of charge transport and biological activity preservation may be obtained. Immobilization of biomaterials on electrode surfaces is the crucial step in fabricating electrochemical as well as optical biosensors and bioelectronic devices. Continuing systematic investigation in the manufacturing of enzyme–conducting sensitive systems, here is presented a convenient fluorescence sensing strategy for catecholamines detection based on FRET (fluorescence resonance energy transfer) phenomena observed for, i.e., complexes of Fe²⁺ and epinephrine. The biosensor was constructed using low temperature co-fired ceramics technology (LTCC). This sensing system used the catalytical oxidation of catecholamines and quench of the strong luminescence of obtained complexes due to FRET. The detection process was based on the oxidation of substrate in the presence of the enzyme–laccase/tyrosinase.

Keywords: biosensor, conducting polymer, enzyme, FRET, LTCC

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Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

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Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model.

Keywords: probability-based damage detection (PBDD), Kriging, surrogate modeling, uncertainty quantification, artificial intelligence, enhanced ideal gas molecular movement (EIGMM)

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Authors: Dimitrios I. Tselentis, Simon P. Washington

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Considerable literature has been focused over the last few decades on forecasting the consumer demand of Energy Efficient Vehicles (EEVs). These methodological issues range from how to capture recent purchase decisions in revealed choice studies and how to set up experiments in stated preference (SP) studies, and choice of analysis method for analyzing such data. This paper reviews the plethora of published studies on the field of forecasting demand of EEVs since 1980, and provides a review and annotated bibliography of that literature as it pertains to this particular demand forecasting problem. This detailed review addresses the literature not only to Transportation studies, but specifically to the problem and methodologies around forecasting to the time horizons of planning studies which may represent 10 to 20 year forecasts. The objectives of the paper are to identify where existing gaps in literature exist and to articulate where promising methodologies might guide longer term forecasting. One of the key findings of this review is that there are many common techniques used both in the field of new product demand forecasting and the field of predicting future demand for EEV. Apart from SP and RP methods, some of these new techniques that have emerged in the literature in the last few decades are survey related approaches, product diffusion models, time-series modelling, computational intelligence models and other holistic approaches.

Keywords: demand forecasting, Energy Efficient Vehicles (EEVs), forecasting methodologies review, methodological approaches

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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: Yao-Hong Tsai

Abstract:

Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

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Authors: Maryam Aloshan, Imam Mohammad Ibn Saud, Ahmad Khudair

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This study assesses the data governance maturity of nonprofit organizations in Riyadh, Saudi Arabia, using the National Data Maturity Index (NDI) framework developed by the Saudi Data and Artificial Intelligence Authority (SDAIA). Employing a survey designed around the NDI model, data maturity levels were evaluated across 14 dimensions using a 5-point Likert scale. The results reveal a spectrum of maturity levels among the organizations surveyed: while some medium-sized associations reached the ‘Defined’ stage, others, including large associations, fell within the ‘Absence of Capabilities’ or ‘Building’ phases, with no organizations achieving the advanced ‘Established’ or ‘Pioneering’ levels. This variation suggests an emerging recognition of data governance but underscores the need for targeted interventions to bridge the maturity gap. The findings point to a significant opportunity to elevate data governance capabilities in Saudi nonprofits through customized capacity-building initiatives, including training, mentorship, and best practice sharing. This study contributes valuable insights into the digital transformation journey of the Saudi nonprofit sector, aligning with national goals for data-driven governance and organizational efficiency.

Keywords: nonprofit organizations-national data maturity index (NDI), Saudi Arabia- SDAIA, data governance, data maturity

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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: Andrii Shalaginov, Katrin Franke, Xiongwei Huang

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One of the leading problems in Cyber Security today is the emergence of targeted attacks conducted by adversaries with access to sophisticated tools. These attacks usually steal senior level employee system privileges, in order to gain unauthorized access to confidential knowledge and valuable intellectual property. Malware used for initial compromise of the systems are sophisticated and may target zero-day vulnerabilities. In this work we utilize common behaviour of malware called ”beacon”, which implies that infected hosts communicate to Command and Control servers at regular intervals that have relatively small time variations. By analysing such beacon activity through passive network monitoring, it is possible to detect potential malware infections. So, we focus on time gaps as indicators of possible C2 activity in targeted enterprise networks. We represent DNS log files as a graph, whose vertices are destination domains and edges are timestamps. Then by using four periodicity detection algorithms for each pair of internal-external communications, we check timestamp sequences to identify the beacon activities. Finally, based on the graph structure, we infer the existence of other infected hosts and malicious domains enrolled in the attack activities.

Keywords: malware detection, network security, targeted attack, computational intelligence

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Authors: Milad Pirali, Sohrab Keyhani, Mohhamad Ali Sanjari, Ali Ashraf Jamshidi

Abstract:

Purpose: The effect of hamstring antagonist activity on the knee extensors torque of the Anterior Cruciate Ligament reconstruction (ACLR) is not clear and persistent muscle weakness is common after ACLR. Hamstring activation when acting as antagonist is considered very important for knee strengths. Therefore the purpose of this study was to examine hamstring antagonist coactivation during maximal effort of the isokinetic knee extension in ACLR athletes with hamstring autograft. Materials and Methods: We enrolled 20 professional athletes who underwent primary ACLR (hamstring tendon autograft)with 6-24 months postoperative and 20 healthy subjects as control group. Each subjects performed maximal effort isokinetic knee extension and flexion in 60/˚ s and 180/˚ s velocities for the involved and uninvolved limb. Synchronously, surface electromyography (EMG) was recorded of vastus medialis (VM), vastus lateralis (VL), rectus femoris (RF) and biceps femoris (BF). The antagonist integrated EMG (IEMG) values were normalized to the IEMG of the same muscle during maximal isokinetic eccentric effort at the same velocities and ROM. Results: A one-way analysis of variance designs shows significantly greater IEMG coactivation of hamstring and decreased activation of Vm in ACLR when compared to uninvolved and control group leg in 60/˚ s and 180/˚ s velocities. Likewise peak torque to body weight was decreased in ACLR compared to uninvolved and control group during knee extension in both velocities (p < 0.05). Conclusions: Decreased extensors moment caused by decreased quadriceps inhibition and increased hamstring coactivation. In addition, these result indicated to decrease of motor unit recruitment in the VM (as a kinesiologicmonitore of the knee). It is appearing that strengthening of the quadriceps to be an important for rehabilitation program after ACLR for preparation in athletes endeavors. Therefore, we suggest that having more emphasis and focus on quadriceps strength and less emphasis on hamstring following ACLR.

Keywords: ACLR-coactivation, dynamometry, electromyography, isokinetic

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Authors: Ibrahim Makram Ibrahim Salib

Abstract:

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

Procedia PDF Downloads 74