Search results for: artifical intelligence

Authors: Hang Ju, Changmin Zhu

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The development level of the BeiDou Navigation Satellite System (BDS) can strongly reflect national defense strength as an important spatial information infrastructure. BDS can be not only used for military purposes, such as intelligence gathering, nuclear explosion monitoring, emergency communications, but also for location services, transportation, mapping, precision agriculture. In order to ensure the national defense security and the wide application of BDS in civil and military areas, BDS must be autonomous and controllable. As a complex system of knowledge-intensive, knowledge transformation runs through the whole process of research and development, production, operation, and maintenance of BDS. Based on the perspective of knowledge transformation, this paper expounds on the meaning of socialization, externalization, combination, and internalization of knowledge transformation, and the coupling relationship of autonomy and control on the basis of analyzing the status quo and problems of the autonomy and control of BDS. The autonomous and controllable framework of BDS based on knowledge transformation is constructed from six dimensions of management capability, R&D capability, technical capability, manufacturing capability, service support capability, and application capability. It can provide support for the smooth implementation of information security policy, provide a reference for the autonomy and control of the upstream and downstream industrial chains in Beidou, and provide a reference for the autonomous and controllable research of aerospace components, military measurement test equipment, and other related industries.

Keywords: knowledge transformation, BeiDou Navigation Satellite System, autonomy and control, framework

Procedia PDF Downloads 184

Authors: Kapil Khanna, Swee Kuik, Joowon Ban

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The current linear economic system is unsustainable due to its dependence on the uncontrolled exploitation of diminishing natural resources. The integration of business innovation and supply chain management has brought about the redesign of business processes through the implementation of a closed-loop approach. The circular economy (CE) offers a sustainable solution to improve business opportunities in the near future by following the principles of rejuvenation and reuse inspired by nature. Those business owners start to rethink and consider using waste as raw material to make new products for consumers. The implementation of CE helps organisations to incorporate new strategic plans for decreasing the use of virgin materials and nature resources. Supply chain partners that are geographically dispersed rely heavily on innovative approaches to support supply chain management. Presently, numerous studies have attempted to establish the concept of supply chain management (SCM) by integrating CE principles, which are commonly denoted as circular SCM. While many scholars have recognised the challenges of transitioning to CE, there is still a lack of consensus on business best practices that can facilitate companies in embracing CE across the supply chain. Hence, this paper strives to scrutinize the SCM practices utilised for CE, identify the obstacles, and recommend best practices that can enhance a company's ability to incorporate CE principles toward business innovation and supply chain performance. Further, the paper proposes future research in the field of using specific technologies such as artificial intelligence, Internet of Things, and blockchain as business innovation tools for supply chain management and CE adoption.

Keywords: business innovation, challenges, circular supply chain, supply chain management, technology

Procedia PDF Downloads 98

Authors: G. M. Hamada, A. A. Al-Gathe, A. M. Al-Khudafi

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Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles.

Keywords: water saturation, Archie’s parameters, artificial intelligence, PSONN, sandstone reservoir

Procedia PDF Downloads 128

Authors: Jasmin Cowin

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Augmented reality digital technologies, big data, and the need for a teacher workforce able to meet the demands of a knowledge-based society are poised to lead to major changes in the field of education. This paper explores applications and educational use cases of augmented reality digital technologies for educational organizations during the Fourth Industrial Revolution. The Fourth Industrial Revolution requires vision, flexibility, and innovative educational conduits by governments and educational institutions to remain competitive in a global economy. Educational organizations will need to focus on teaching in and for a digital age to continue offering academic knowledge relevant to 21st-century markets and changing labor force needs. Implementation of contemporary disciplines will need to be embodied through learners’ active knowledge-making experiences while embracing ubiquitous accessibility. The power of distributed ledger technology promises major streamlining for educational record-keeping, degree conferrals, and authenticity guarantees. Augmented reality digital technologies hold the potential to restructure educational philosophies and their underpinning pedagogies thereby transforming modes of delivery. Structural changes in education and governmental planning are already increasing through intelligent systems and big data. Reimagining and redesigning education on a broad scale is required to plan and implement governmental and institutional changes to harness innovative technologies while moving away from the big schooling machine.

Keywords: fourth industrial revolution, artificial intelligence, big data, education, augmented reality digital technologies, distributed ledger technology

Procedia PDF Downloads 277

Authors: Luis N. Pereira, Marco P. Carrasco

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Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

Procedia PDF Downloads 234

Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: Nawal Yacoub Halim Abdelmasih

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The intersection between development and human rights has been the factor of scholarly debate for a long term. therefore, some of standards, which enlarge from the proper to development to the human rights-based totally method to development, had been adopted to apprehend the dynamics among the two standards. no matter these attempts, the exact relationship among improvement and human rights has not been completely determined but. however, the inevitable interdependence between the two notions and the idea that improvement efforts ought to be undertaken with the aid of giving due regard to human rights ensures has won momentum in recent years. then again, the emergence of sustainable development as a extensively common technique in development dreams and policies makes this unsettled convergence even extra complicated. The vicinity of sustainable improvement in human rights regulation discourse and the function of the latter in making sure the sustainability of development applications name for a scientific observe. as a result, this newsletter seeks to discover the relationship among development and human rights, particularly focusing at the location given to sustainable development principles in international human proper regulation. it'll similarly quest whether or not there is a proper to sustainable improvement diagnosed therein. as a result, the item asserts that the ideas of sustainable improvement are immediately or circuitously diagnosed in diverse human rights contraptions, which affords an affirmative response to the question raised hereinabove. This paintings, therefore, will make expeditions via international and regional human rights devices in addition to case legal guidelines and interpretative hints of human rights bodies to show this speculation.

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: Shivaram P. Arunachalam, Mustafa Y. Sir, Andy Boggust, David M. Nestler, Thomas R. Hellmich, Kalyan S. Pasupathy

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Emergency departments (EDs) provide urgent care to patients throughout the day in a complex and chaotic environment. Real-time location systems (RTLS) are increasingly being utilized in healthcare settings, and have shown to improve safety, reduce cost, and increase patient satisfaction. Radio Frequency Identification Device (RFID) data in an ED has been shown to compute variables such as patient-provider contact time, which is associated with patient outcomes such as 30-day hospitalization. These variables can provide avenues for improving ED operational efficiency. A major challenge with ED financial operations is under-coding of critical care services due to physicians’ difficulty reporting accurate times for critical care provided under Current Procedural Terminology (CPT) codes 99291 and 99292. In this work, the authors propose a framework to optimize ED critical care billing using RFID data. RFID estimated physician-patient contact times could accurately quantify direct critical care services which will help model a data-driven approach for ED critical care billing. This paper will describe the framework and provide insights into opportunities to prevent under coding as well as over coding to avoid insurance audits. Future work will focus on data analytics to demonstrate the feasibility of the framework described.

Keywords: critical care billing, CPT codes, emergency department, RFID

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Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Prakash Singh, Piet Maphodisa Kgohlo

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Knowledge-worker productivity is one of the biggest leadership challenges facing all organizations in the twenty-first century. It cannot be denied that knowledge-worker productivity affects all organizations. The work and the workforce are both undergoing greater changes currently than at any time, since the beginning of the industrial revolution two centuries ago. Employees welcome collegial teaming (CT) as an innovative way to develop their work-integrated learning competencies. Human resource development policies must evoke the symbiotic relationship between CT and work-integrated learning, seeing that employees need to be endowed with the competence to move from one skill to another, as each one becomes obsolete, and to simultaneously develop their cognitive and emotional intelligence. The outcome of this relationship must culminate in the development of highly productive knowledge-workers. While this study focuses on teachers, the conceptual framework and the findings of this research can be beneficial for any organization, public or private sector, business or non-business. Therefore, in this quantitative study, the benefits of CT are considered in developing human resources to sustain knowledge-worker productivity. The ANOVA p-values reveal that the majority of teachers agree that CT can empower them to overcome the challenges of managing curriculum change. CT can equip them with continuous and sustained learning, growth and improvement, necessary for knowledge-worker productivity. This study, therefore, confirms that CT benefits all workers, immaterial of their age, gender or experience. Hence, this exploratory research provides a new perspective of CT in addressing knowledge-worker productivity when organizational change alters the vision of the organization.

Keywords: collegial teaming, human resource development, knowledge-worker productivity, work-integrated learning

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Authors: Ali Riza Perçin, Eser Bingül

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Internet technologies have created opportunities with which people share their ideologies, thoughts and products. This virtual world, named social media has given the chance of gathering individual users and people from the world's remote locations and establishing an interaction between them. However, to an increasingly higher degree terrorist organizations today use the internet and most notably social-network media to create the effects they desire through a series of on-line activities. These activities, designed to support their activities, include information collection (intelligence), target selection, propaganda, fundraising and recruitment to name a few. Meanwhile, these have been used as the most important tool for recruitment especially from the different region of the world, especially disenfranchised youth, in the West in order to mobilize support and recruit “foreign fighters.” The recruits have obtained the statue, which is not accessible in their society and have preferred the style of life that is offered by the terrorist organizations instead of their current life. Like other terrorist groups, for a while now the terrorist organization Islamic State (IS) in Iraq and Syria has employed a social-media strategy in order to advance their strategic objectives. At the moment, however, IS seems to be more successful in their on-line activities than other similar organizations. IS uses social media strategically as part of its armed activities and for the sustainability of their military presence in Syria and Iraq. In this context, “Generation Y”, which could exist at the critical position and undertake active role, has been examined. Additionally, the explained characteristics of “Generation Y” have been put forward and the duties of families and society have been stated as well.

Keywords: social media, "generation Y", terrorist organization, islamic state IS

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Richard Molyneux, Christopher Parrott, Kirill Horoshenkov

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This paper aims to demonstrate how various algorithms can be implemented within swarms of autonomous robots to provide continuous inspection within underground pipeline networks. Current methods of fault detection within pipes are costly, time consuming and inefficient. As such, solutions tend toward a more reactive approach, repairing faults, as opposed to proactively seeking leaks and blockages. The paper presents an efficient inspection method, showing that autonomous swarm robotics is a viable way of monitoring underground infrastructure. Tailored adaptations of various Vehicle Routing Problems (VRP) and path-planning algorithms provide a customised inspection procedure for complicated networks of underground pipes. The performance of multiple algorithms is compared to determine their effectiveness and feasibility. Notable inspirations come from ant colonies and stigmergy, graph theory, the k-Chinese Postman Problem ( -CPP) and traffic theory. Unlike most swarm behaviours which rely on fast communication between agents, underground pipe networks are a highly challenging communication environment with extremely limited communication ranges. This is due to the extreme variability in the pipe conditions and relatively high attenuation of acoustic and radio waves with which robots would usually communicate. This paper illustrates how to optimise the inspection process and how to increase the frequency with which the robots pass each other, without compromising the routes they are able to take to cover the whole network.

Keywords: autonomous inspection, buried pipes, stigmergy, swarm intelligence, vehicle routing problem

Procedia PDF Downloads 166

Authors: Bilge B. Calik, Ummuhan B. Aslan, Suat Erel, Sehmus Aslan

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Objective: Intellectual disability (ID) is characterized by limitations in both intellectual functioning and adaptive behavior, which covers many everyday social and practical skills. The aim of this study was to evaluate the balance and coordination performance determined between mild and moderate ID adolescents who regularly play sport. Methods: The study comprised a total of 179 participants, of which 135 were male adolescents with mild and moderate-level ID who regularly play sports (16.52 ± 2.17 years) and 44 age-matched male adolescents with typical development without ID who do not do any sports (16.52 ± 0.99 years). The participants with ID were students of Special Education Schools for the mentally disabled and had been diagnosed with ID at a Ministry of Health Hospital. The adolescents with mild and moderate ID had been playing football in their school teams at least 2 days a week, for at least one year. Balance and coordination of adolescents were assessed by Bilateral coordination and balance subtests of Short Form Bruininks-Oseretsky Test of Motor Proficiency (BOT-2 SF). Results: As a result of the evaluations comparing coordination and balance scores significant differences were determined between all three groups in favor of the peers without ID (p<0.05). Conclusions: It was observed that balance and coordination levels of adolescents with mild ID were better than those of adolescents with moderate-level ID but lower than those of peers without ID. These results indicate a relationship between IQ level and motor performance. Further comparative studies are needed on individuals with ID who play and do not play sports in order to examine the impact of participation in sports on the motor skills of individuals with ID.

Keywords: balance, coordination, intellectual disability, motor skills, sport

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Authors: Christodoulou Christos, Politis Anastasios

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The utilization of Wireless Body Area Network (WBAN) is experiencing a notable surge in popularity as a result of its widespread implementation in the field of smart health. WBANs utilize small sensors implanted within the human body to monitor and record physiological indicators. These sensors transmit the collected data to hospitals and healthcare facilities through designated access points. Bio-sensors exhibit a diverse array of shapes and sizes, and their deployment can be tailored to the condition of the individual. Multiple sensors may be strategically placed within, on, or around the human body to effectively observe, record, and transmit essential physiological indicators. These measurements serve as a basis for subsequent analysis, evaluation, and therapeutic interventions. In conjunction with physical health concerns, numerous smartwatches are engineered to employ artificial intelligence techniques for the purpose of detecting mental health conditions such as depression and anxiety. The utilization of smartwatches serves as a secure and cost-effective solution for monitoring mental health. Physiological signals are widely regarded as a highly dependable method for the recognition of emotions due to the inherent inability of individuals to deliberately influence them over extended periods of time. The techniques that WBANs employ to recognize emotions are thoroughly examined in this article.

Keywords: emotion recognition, wireless body area network, WBAN, ERC, wearable devices, psychological signals, emotion, smart-watch, prediction

Procedia PDF Downloads 50

Authors: R. S. Quintal, J. L. Tesch Santos, M. D. Davis, E. C. de Santana, M. de F. Bandeira dos Santos

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The objective is to identify the contributions from the introduction of the computerized system deal within the Accounting Department of Brazilian Navy Financial Directorship and its possible effects on the budgetary and financial harvest of Brazilian Navy. The relevance lies in the fact that the management process is responsible for the continuous improvement of organizational performance through higher levels of quality in their activities. Improvements in organizational processes have direct effects on crops cost, quality, reliability, flexibility and speed. The method of study of this research is the case study. The choice of case study attended, among other demands, a need for greater flexibility to study processes related to a computerized system. The sources of evidence were used literature, documentary and direct observation. Direct observation was made by monitoring the implementation of the computerized system in the Division of Management Analysis. The main findings of the study point to the fact that the computerized system may contribute significantly to the standardization of information. There was improvement of internal processes in the division of management analysis, made possible the consolidation of a standard management and performance analysis that contribute to global homogeneity in the treatment of information essential to the process of decision making. This study has limitations related to the fact the search result be subject exclusively to the case studied, and it is impossible to generalize to other organs of government.

Keywords: process management, management control, business intelligence, Brazilian Navy

Procedia PDF Downloads 238

Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

Procedia PDF Downloads 131

Authors: Nathaniel Carpenter

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Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Daudi S. Simbeye, Mbazingwa E. Mkiramweni

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Plant diseases have the potential to drastically diminish the quantity and quality of agricultural products. Guava (Psidium guajava), sometimes known as the apple of the tropics, is one of the most widely cultivated fruits in tropical regions. Monitoring plant health and diagnosing illnesses is an essential matter for sustainable agriculture, requiring the inspection of visually evident patterns on plant leaves and fruits. Due to minor variations in the symptoms of various guava illnesses, a professional opinion is required for disease diagnosis. Due to improper pesticide application by farmers, erroneous diagnoses may result in economic losses. This study proposes a method that uses artificial intelligence (AI) to detect and classify the most widespread guava plant by comparing images of its leaves and fruits to datasets. ESP32 CAM is responsible for data collection, which includes images of guava leaves and fruits. By comparing the datasets, these image formats are used as datasets to help in the diagnosis of plant diseases through the leaves and fruits, which is vital for the development of an effective automated agricultural system. The system test yielded the most accurate identification findings (99 percent accuracy in differentiating four guava fruit diseases (Canker, Mummification, Dot, and Rust) from healthy fruit). The proposed model has been interfaced with a mobile application to be used by smartphones to make a quick and responsible judgment, which can help the farmers instantly detect and prevent future production losses by enabling them to take precautions beforehand.

Keywords: early identification, guava plants, fruit diseases, deep learning

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

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The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

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Authors: José María Agugusto-landa, Inmaculada García-Martínez, Lara Checa Domene, Óscar Gavín Chocano

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Self-concept is the perception that a person has of himself, of his abilities, skills, traits, and values. Self-concept is composed of different dimensions, such as academic self-concept, physical self-concept, social self-concept, emotional self-concept, and family self-concept. The relationship between the dimensions of self-concept and mental health and academic performance among future teachers is a topic of interest for educational psychology. Some studies have found that: (i) There is a positive relationship between general self-concept, academic self-concept and academic performance, that is, students who have a more positive image of themselves tend to get better grades and be more motivated to learn. (ii) There is a positive relationship between emotional intelligence, physical self-concept and healthy habits, that is, students who regulate and understand their emotions better have a higher satisfaction with their physical appearance and follow a more balanced diet and a higher physical activity. As for gender differences in the dimensions of self-concept among future teachers, some studies have found that: (i) Girls tend to have a higher self-concept in the social, family and verbal dimensions, that is, they perceive themselves as more capable of relating to others, communicating effectively and receiving support from their family. (ii) Boys tend to have a higher self-concept in the physical, emotional and mathematical dimensions, that is, they perceive themselves as more capable of performing physical activities, controlling their emotions and solving mathematical problems. (iii) There are no significant differences between general self-concept and academic self-concept according to gender, that is, both girls and boys have a similar perception of their global worth and academic competence.

Keywords: preservice teachers, self-concept, academic performance, mental wellbeing

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: Goudjil kamel, Boukhatem Ghania, Jlailia Djihene

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This paper delves into the development of a sophisticated desktop application designed to calculate soil bearing capacity and predict limit pressure. Drawing from an extensive review of existing methodologies, the study meticulously examines various approaches employed in soil bearing capacity calculations, elucidating their theoretical foundations and practical applications. Furthermore, the study explores the burgeoning intersection of artificial intelligence (AI) and geotechnical engineering, underscoring the transformative potential of AI- driven solutions in enhancing predictive accuracy and efficiency.Central to the research is the utilization of cutting-edge machine learning techniques, including Artificial Neural Networks (ANN), XGBoost, and Random Forest, for predictive modeling. Through comprehensive experimentation and rigorous analysis, the efficacy and performance of each method are rigorously evaluated, with XGBoost emerging as the preeminent algorithm, showcasing superior predictive capabilities compared to its counterparts. The study culminates in a nuanced understanding of the intricate dynamics at play in geotechnical analysis, offering valuable insights into optimizing soil bearing capacity calculations and limit pressure predictions. By harnessing the power of advanced computational techniques and AI-driven algorithms, the paper presents a paradigm shift in the realm of geotechnical engineering, promising enhanced precision and reliability in civil engineering projects.

Keywords: limit pressure of soil, xgboost, random forest, bearing capacity

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Authors: Sang Ho Lee, Yountaik Leem

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This paper reviews Korean cases of smart cities through the analysis framework of strategic foci, characteristics and effects. Firstly, national strategies including c(cyber), e(electronic), u(ubiquitous) and s(smart) Korea strategies were considered from strategic angles. Secondly, the characteristics of smart cities in Korea were looked through the smart cities examples such as Seoul, Busan, Songdo and Sejong cities etc. from the views on the by STIM (Service, Technology, Infrastructure and Management) analysis. Finally, the effects of smart cities on socio-economies were investigated from industrial perspective using the input-output model and structural path analysis. Korean smart city strategies revealed that there were different kinds of strategic foci. c-Korea strategy focused on information and communications network building and user IT literacy. e-Korea strategy encouraged e-government and e-business through utilizing high-speed information and communications network. u-Korea strategy made ubiquitous service as well as integrated information and communication operations center. s-Korea strategy is propelling 4th industrial platform. Smart cities in Korea showed their own features and trends such as eco-intelligence, high efficiency and low cost oriented IoT, citizen sensored city, big data city. Smart city progress made new production chains fostering ICTs (Information Communication Technologies) and knowledge intermediate inputs to industries.

Keywords: Korean smart cities, Korean smart city strategies, STIM, smart service, infrastructure, technologies, management, effect of smart city

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Authors: Evangelos Koumparoudis

Abstract:

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

Keywords: death, posthumanism, infomedicine, nanomedicine, cryonics

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Authors: Ang Chia Hong, Julian Khoo Xubin, Burra Venkata Durga Kumar

Abstract:

Distributed systems offer many benefits to the healthcare industry. From big data analysis to business intelligence, the increased computational power and efficiency from distributed systems serve as an invaluable resource in the healthcare sector to utilize. However, as the usage of these distributed systems increases, many issues arise. The main focus of this paper will be on security issues. Many security issues stem from distributed systems in the healthcare industry, particularly information security. The data of people is especially sensitive in the healthcare industry. If important information gets leaked (Eg. IC, credit card number, address, etc.), a person’s identity, financial status, and safety might get compromised. This results in the responsible organization losing a lot of money in compensating these people and even more resources expended trying to fix the fault. Therefore, a framework for a blockchain-based healthcare data management system for healthcare was proposed. In this framework, the usage of a blockchain network is explored to store the encryption key of the patient’s data. As for the actual data, it is encrypted and its encrypted data, called ciphertext, is stored in a cloud storage platform. Furthermore, there are some issues that have to be emphasized and tackled for future improvements, such as a multi-user scheme that could be proposed, authentication issues that have to be tackled or migrating the backend processes into the blockchain network. Due to the nature of blockchain technology, the data will be tamper-proof, and its read-only function can only be accessed by authorized users such as doctors and nurses. This guarantees the confidentiality and immutability of the patient’s data.

Keywords: distributed, healthcare, efficiency, security, blockchain, confidentiality and immutability

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Authors: Mizuno Norihito

Abstract:

This study attempts to examine Japanese views of Thailand primarily in the 1920s and 1930s through the analysis of documents published by the Office of Governor-General of Taiwan (Taiwan Sotokufu) and its affiliated organizations. Japan regarded Taiwan, under its control since 1895, as a foothold to making inroads into the South, and The governor-general office was active in investigations and intelligence gathering in Southeast Asia, as well as the southern part of the Chinese continent. Documents published by the governor-general office and its related organizations, especially those in a couple of decades following the First World War, reveal that the Japanese paid attention to the presence of the-Thai-Chinese during the time period. It would not be surprising that the desiring to penetrating into the Thai market, as well as the markets of the rest of Southeast Asia, the Japanese could not ignore the presence of the Thai-Chinese because of their local economic influences. The increased Japanese concern about the Thai-Chinese toward the end of the 1920s and throughout the 1930s was, moreover, intertwined with the increased tension between China and Japan. In other words, Thailand, as well as the rest of Southeast Asia, became another arena of Sino-Japanese confrontation. The rise of nationalism against Japan in China infected the Thai-Chinese communities and threatened Japanese economic activities in the country. However, the Japanese simultaneously found out that Thai-Chinese concert with anti-Japanese in China did not necessarily match their business interests and that the Thai government’s efforts to assimilate the Thai-Chinese into the Thais society and strategic approach to Japan in the late 1930s hampered their anti-Japanese actions.

Keywords: Japanese-Thai Relations, Sino-Japanese relations, Thai Chinese, Overseas Japanese

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Authors: R. O. Osaseri, A. R. Usiobaifo

Abstract:

The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Omar Farshad Jeelani, Makaire Njie, Viktoriia M. Korzhuk

Abstract:

Recently, the application of AI-powered algorithms in healthcare continues to flourish. Particularly, access to healthcare information, including patient health history, diagnostic data, and PII (Personally Identifiable Information) is paramount in the delivery of efficient patient outcomes. However, as the exchange of healthcare information between patients and healthcare providers through AI-powered solutions increases, protecting a person’s information and their privacy has become even more important. Arguably, the increased adoption of healthcare AI has resulted in a significant concentration on the security risks and protection measures to the security and privacy of healthcare data, leading to escalated analyses and enforcement. Since these challenges are brought by the use of AI-based healthcare solutions to manage healthcare data, AI-based data protection measures are used to resolve the underlying problems. Consequently, this project proposes AI-powered safeguards and policies/laws to protect the privacy of healthcare data. The project presents the best-in-school techniques used to preserve the data privacy of AI-powered healthcare applications. Popular privacy-protecting methods like Federated learning, cryptographic techniques, differential privacy methods, and hybrid methods are discussed together with potential cyber threats, data security concerns, and prospects. Also, the project discusses some of the relevant data security acts/laws that govern the collection, storage, and processing of healthcare data to guarantee owners’ privacy is preserved. This inquiry discusses various gaps and uncertainties associated with healthcare AI data collection procedures and identifies potential correction/mitigation measures.

Keywords: data privacy, artificial intelligence (AI), healthcare AI, data sharing, healthcare organizations (HCOs)

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Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

Abstract:

Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

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