Search results for: potential intelligence

Authors: Ahmed Atef, Mohamed Alhasheem, Eman Beshr

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In this paper, an enhanced DC microgrid design is proposed using the DAB converter as a conversion unit in order to harvest the maximum power from the PV array. Each connected DAB converter is controlled with an enhanced control strategy. The controller is based on the artificial intelligence (AI) technique to regulate the terminal PV voltage through the phase shift angle of each DAB converter. In this manner, no need for a Maximum Power Point Tracking (MPPT) unit to set the reference of the PV terminal voltage. This strategy overcomes the stability issues of the DC microgrid as the response of converters is superior compared to the conventional strategies. The proposed PV interface system is modelled and simulated using MATLAB/SIMULINK. The simulation results reveal an accurate and fast response of the proposed design in case of irradiance changes.

Keywords: DC microgrid, DAB converter, parallel operation, artificial intelligence, fast response

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Authors: M. N. A. Abdullah, Pritha Roy, R. R. Shil, D. R. Sarker

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Alpha-nucleus interaction is obscured because it produces enhanced cross-sections at large scattering angles known as anomaly in large angle scattering (ALAS). ALAS is prominent in the elastic scattering of α-particles as well as in non-elastic processes involving α-particles for incident energies up to 50 MeV and for targets of mass A ≤ 50. The Woods-Saxon type of optical model potential fails to describe the processes in a consistent manner. Folded potential is a good candidate and often used to construct the potential which is derived from the microscopic as well as semi-microscopic folding calculations. The present work reports the analyses of the elastic scattering of α-particles from ²⁴Mg and ²⁸Si at Eα=22-100 MeV and 14.4-120 MeV incident energies respectively in terms of the modified single-folded (MSF) potential. To derive the MSF potential, we take the view that the nucleons in the target nuclei ²⁴Mg and ²⁸Si are primarily in α-like clusters and the rest of the time in unclustered nucleonic configuration. The MSF potential, found in this study, does not need any renormalization over the whole range of incident α energies, and the renormalization factor has been found to be exactly 1 for both the targets. The best-fit parameters yield 4Aα = 21 and AN = 3 for α-²⁴Mg potential, and 4Aα = 26 and AN = 2 for α-²⁸Si potential in time-average pictures. The root-mean-square radii of both ²⁴Mg and ²⁸Si are also deduced, and the results obtained from this work agree well with the outcomes of other studies.

Keywords: elastic scattering, optical model, folded potential, renormalization

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Hae-Won Seo, Yong-Gu Lee

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Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: connected-car, data modeling, route planning, navigation system

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Authors: Kate Benedicta Amenador, Dianjian Wang, Bright Nkrumah

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This extensive bibliometric study explores the dynamic influence of artificial intelligence in the field of English as a Foreign Language (EFL) between 2012 and 2024. The study, which examined 4,500 articles from Google Scholar, Modern Language Association Linguistics Abstracts, Web of Science, Scopus, Researchgate, and library genesis databases, indicates that AI integration in EFL is on the rise. This notable increase is ascribed to a variety of transformative events, including increased academic funding for higher education and the COVID-19 epidemic. The results of the study identify leading contributors, prominent authors, publishers and sources, with the United States, China and the United Kingdom emerging as key contributors. The co-occurrence analysis of key terms reveals five clusters highlighting patterns in AI-enhanced language instruction and learning, including evaluation strategies, educational technology, learning motivation, EFL teaching aspects, and learner feedback. The study also discusses the impact of various AIs in enhancing EFL writing skills with software such as Grammarly, Quilbot, and Chatgpt. The current study recognizes limitations in database selection and linguistic constraints. Nevertheless, the results provide useful insights for educators, researchers and policymakers, inspiring and guiding a cross-disciplinary collaboration and creative pedagogical techniques and approaches to teaching and learning in the future.

Keywords: artificial intelligence, bibliometrics study, VOSviewer visualization, English as a foreign language

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Authors: Holly Sandberg, Christina McCoy, Khaled Mansy

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Eucalyptus Cloeziana, commonly known as Gympie Messmate, is a fast-growing hardwood native to Australia. Its quick growth makes it advantageous for carbon sequestering, while its strength class lends itself to structural applications. Market research shows that the demand for timber is growing, especially mass timber. An environmental product declaration, or EPD, for eucalyptus Cloeziana in the Australian market has been evaluated and compared to the EPD’s of steel and Douglas fir of the same region. An EPD follows a product throughout its life cycle, stating values for global warming potential, ozone depletion potential, acidification potential, eutrophication potential, photochemical ozone creation potential, and abiotic depletion potential. This paper highlights the market potential, as well as the environmental benefits and challenges to using Gympie Messmate as a structural building material. In addition, a case study is performed to compare steel, Douglas fir, and eucalyptus in terms of embodied carbon and structural weight within a single structural bay. Comparisons among the three materials highlight both the differences in structural capabilities as well as environmental impact.

Keywords: eucalyptus, timber, construction, structural, material

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Authors: Juanita Morris

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The purpose of this study will examine the mediating relationships between the theories of intelligence, mathematics anxiety, gender stereotype threat, meta-cognition and math performance through the use of eye tracking technology, affecting student perception and problem-solving abilities. The participants will consist of (N=80) female graduate students. Test administered were the Abbreviated Math Anxiety Scale, Tobii Eye Tracking software, gender stereotype threat through Google images, and they will be asked to describe their problem-solving approach allowed to measure metacognition. Participants will be administered mathematics problems while having gender stereotype threat shown to them through online images while being directed to look at the eye tracking software Tobii. We will explore this by asking ‘Is mathematics anxiety associated with the theories of intelligence and gender stereotype threat and how does metacognition and math performance place a role in mediating those perspectives?’. It is hypothesized that math-anxious students are more likely affected by the gender stereotype threat and that may play a role in their performance? Furthermore, we also want to explore whether math anxious students are more likely to be an entity theorist than incremental theorist and whether those who are math anxious will be more likely to be fixated on variables associated with coefficients? Path analysis and independent samples t-test will be used to generate results for this study. We hope to conclude that both the theories of intelligence and metacognition mediate the relationship between mathematics anxiety and gender stereotype threat.

Keywords: math anxiety, emotions, affective domains fo learning, cognitive underlinings

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Authors: Haider Javed Uppal, Javed Yunas Uppal

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In today's globalized world, it is necessary to develop a form of music that is able to evoke equal emotional responses among people from diverse cultural backgrounds. Indigenous cultures throughout history have developed their own music cognition, specifically in terms of the connections between music and mood. With the advancements in artificial intelligence technologies, it has become possible to analyze and categorize music features such as timbre, harmony, melody, and rhythm and relate them to the resulting mood effects experienced by listeners. This paper presents a model that utilizes a screenshot translator to convert music from different origins into waveforms, which are then analyzed using machine learning and information retrieval techniques. By connecting these waveforms with Thayer's matrix of moods, a mood classifier has been developed using fuzzy logic algorithms to determine the emotional impact of different types of music on listeners from various cultures.

Keywords: cognition, world music, artificial intelligence, Thayer’s matrix

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Authors: Anwesha Pathak

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With the increasing number of satellites and space debris, space traffic management (STM) becomes crucial. AI can aid in STM by predicting and preventing potential collisions, optimizing satellite trajectories, and managing orbital slots. Governance frameworks need to address the integration of AI algorithms in STM to ensure safe and sustainable satellite activities. AI and governance play significant roles in the context of satellite activities in space. Artificial intelligence (AI) technologies, such as machine learning and computer vision, can be utilized to process vast amounts of data received from satellites. AI algorithms can analyse satellite imagery, detect patterns, and extract valuable information for applications like weather forecasting, urban planning, agriculture, disaster management, and environmental monitoring. AI can assist in automating and optimizing satellite operations. Autonomous decision-making systems can be developed using AI to handle routine tasks like orbit control, collision avoidance, and antenna pointing. These systems can improve efficiency, reduce human error, and enable real-time responsiveness in satellite operations. AI technologies can be leveraged to enhance the security of satellite systems. AI algorithms can analyze satellite telemetry data to detect anomalies, identify potential cyber threats, and mitigate vulnerabilities. Governance frameworks should encompass regulations and standards for securing satellite systems against cyberattacks and ensuring data privacy. AI can optimize resource allocation and utilization in satellite constellations. By analyzing user demands, traffic patterns, and satellite performance data, AI algorithms can dynamically adjust the deployment and routing of satellites to maximize coverage and minimize latency. Governance frameworks need to address fair and efficient resource allocation among satellite operators to avoid monopolistic practices. Satellite activities involve multiple countries and organizations. Governance frameworks should encourage international cooperation, information sharing, and standardization to address common challenges, ensure interoperability, and prevent conflicts. AI can facilitate cross-border collaborations by providing data analytics and decision support tools for shared satellite missions and data sharing initiatives. AI and governance are critical aspects of satellite activities in space. They enable efficient and secure operations, ensure responsible and ethical use of AI technologies, and promote international cooperation for the benefit of all stakeholders involved in the satellite industry.

Keywords: satellite, space debris, traffic, threats, cyber security.

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Authors: Edlira Kalemi, Sule Yildirim-Yayilgan

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Online Social Networks (OSNs) are nowadays being used widely and intensively for crime investigation and prevention activities. As they provide a lot of information they are used by the law enforcement and intelligence. An extensive review on existing solutions and models for collecting intelligence from this source of information and making use of it for solving crimes has been presented in this article. The main focus is on smart solutions and models where ontologies have been used as the main approach for representing criminal domain knowledge. A framework for a prototype ontology named SC-Ont will be described. This defines terms of the criminal domain ontology and the relations between them. The terms and the relations are extracted during both this review and the discussions carried out with domain experts. The development of SC-Ont is still ongoing work, where in this paper, we report mainly on the motivation for using smart ontology models and the possible benefits of using them for solving crimes.

Keywords: criminal digital evidence, social media, ontologies, reasoning

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Authors: Weber-Lewerenz Bianca

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Digitization is developing fast and has become a powerful tool for digital planning, construction, and operations. Its transformation bears high potentials for companies, is critical for success, and thus, requires responsible handling. This study provides an assessment of calls made in the sustainable development goals by the United Nations (SDGs), White Papers on AI by international institutions, EU-Commission and German Government requesting for the consideration and protection of values and fundamental rights, the careful demarcation between machine (artificial) and human intelligence and the careful use of such technologies. The study discusses digitization and the impacts of artificial intelligence (AI) in construction engineering from an ethical perspective by generating data via conducting case studies and interviewing experts as part of the qualitative method. This research evaluates critically opportunities and risks revolving around corporate digital responsibility (CDR) in the construction industry. To the author's knowledge, no study has set out to investigate how CDR in construction could be conceptualized, especially in relation to the digitization and AI, to mitigate digital transformation both in large, medium-sized, and small companies. No study addressed the key research question: Where can CDR be allocated, how shall its adequate ethical framework be designed to support digital innovations in order to make full use of the potentials of digitization and AI? Now is the right timing for constructive approaches and apply ethics-by-design in order to develop and implement a safe and efficient AI. This represents the first study in construction engineering applying a holistic, interdisciplinary, inclusive approach to provide guidelines for orientation, examine benefits of AI and define ethical principles as the key driver for success, resources-cost-time efficiency, and sustainability using digital technologies and AI in construction engineering to enhance digital transformation. Innovative corporate organizations starting new business models are more likely to succeed than those dominated by conservative, traditional attitudes.

Keywords: construction engineering, digitization, digital transformation, artificial intelligence, ethics, corporate digital responsibility, digital innovation

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Authors: Roberto Feltrero, Sara Osuna-Acedo

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Responsible Research and Innovation have to accomplish one fundamental aim: everybody has to participate in the benefits of innovation, but also innovation has to be democratic; that is to say, everybody may have the possibility to participate in the decisions in the innovation process. Particularly, a democratic and inclusive model of social participation and innovation includes persons with disabilities and people at risk of discrimination. Innovations on Artificial Intelligence for social development have to accomplish the same dual goal: improving equality for accessing fields of public interest like education, training and public services, as well as improving civic and democratic participation in the process of developing such innovations for all. This research aims to develop innovations, policies and policy recommendations to apply and disseminate such artificial intelligence and social model for making educational and administrative processes more accessible. First, designing a citizen participation process to engage citizens in the designing and use of artificial intelligence tools for public services. This will result in improving trust in democratic institutions contributing to enhancing the transparency, effectiveness, accountability and legitimacy of public policy-making and allowing people to participate in the development of ethical standards for the use of such technologies. Second, improving educational tools for lifelong learning with AI models to improve accountability and educational data management. Dissemination, education and social participation will be integrated, measured and evaluated in innovative educational processes to make accessible all the educational technologies and content developed on AI about responsible and social innovation. A particular case will be presented regarding access for all to educational tools and public services. This accessibility requires cognitive adaptability because, many times, legal or administrative language is very complex. Not only for people with cognitive disabilities but also for old people or citizens at risk of educational or social discrimination. Artificial Intelligence natural language processing technologies can provide tools to translate legal, administrative, or educational texts to a more simple language that can be accessible to everybody. Despite technological advances in language processing and machine learning, this becomes a huge project if we really want to respect ethical and legal consequences because that kinds of consequences can only be achieved with civil and democratic engagement in two realms: 1) to democratically select texts that need and can be translated and 2) to involved citizens, experts and nonexperts, to produce and validate real examples of legal texts with cognitive adaptations to feed artificial intelligence algorithms for learning how to translate those texts to a more simple and accessible language, adapted to any kind of population.

Keywords: responsible research and innovation, AI social innovations, cognitive accessibility, public participation

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Authors: Baker Ssekitto, Ambrose Mbogo

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The study reveals that indeed agriculture employs over 70% of Uganda’s population, of which majority are youth and women. The study further reveals that over 70% of the farmers are smallholder farmers based in rural areas, whose operations are greatly affected by; climate change, weak digital skills, limited access to productivity knowledge along value chains, limited access to quality farm inputs, weak logistics systems, limited access to quality extension services, weak business intelligence, limited access to quality markets among others. It finds that the emerging 4th industrial revolution powered by artificial intelligence, 5G and data science will provide possibilities of addressing some of these challenges. Furthermore, the study finds that despite rapid development of ICT4Agric Innovation, their uptake is constrained by a number of factors including; limited awareness of these innovations, low internet and smart phone penetration especially in rural areas, lack of appropriate digital skills, inappropriate programmes implementation models which are project and donor driven, limited articulation of value addition to various stakeholders among others. Majority of farmers and other value chain actors lacked knowledge and skills to harness the power of ICTs, especially their application of ICTs in monitoring and evaluation on quality of service in the extension system and farm level processes.

Keywords: artificial intelligence, productivity, ICT4agriculture, value chain, logistics

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Authors: Julia Yalalova, Dilawar Khan Durrani

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Subjective and objective career success is one of the vital aims that the employees of any organization want to achieve. However, career success is affected by numerous factors. This study aims to identify few of such key factors that affect career success of individual employees. To achieve this objective, this study aims at empirically analyzing that weather or not self-efficacy of employees impacts their career success. Furthermore, this study also aims to analyze whether or not work effort mediates the relationship between self-efficacy and career success. The study will also test weather emotional intelligence moderate the relationship between self-efficacy and work effort. Furthermore, gender based differences related to all the variables are also the focus of this study. The data will be analyzed using SPSS software and the results, recommendations and future implications will be discussed.

Keywords: career success, emotional intelligence, self-efficacy, work effort

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Authors: Sushma Ghogale

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With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Sao-Jeng Chao, Chia-Yun Wei, Si-Han Lai, Cheng-Yu Huang, Yu-Han Teng

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In Taiwan, the landforms are mainly dominated by mountains and hills. Many road sections of the National Highway are impossible to avoid problems such as slope excavation or slope filling. In order to increase the safety of the slope, various slope protection methods are used to stabilize the slope, especially the soil anchor technique is the most common. This study is inspired by the soil liquefaction potential map. The concept of the potential map is widely used. The typhoon, earth-rock flow, tsunami, flooded area, and the recent discussion of soil liquefaction have safety potential concepts. This paper brings the concept of safety potential to the anchored slope. Because the soil anchor inspection is only the concept of points, this study extends the concept of the point to the surface, using the Quantum GIS program to present the slope damage area, and depicts the slope appearance and soil anchor point with the slope as-built drawing. The soil anchor scores are obtained by anchor inspection data, and the low, medium and high potential areas are remitted by interpolation. Thus, the area where the anchored slope may be harmful is judged and relevant maintenance is provided. The maintenance units can thus prevent judgment and deal with the anchored slope as soon as possible.

Keywords: anchor, slope, potential map, lift-off test, existing load

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Authors: Shreyas Kumar, Mili Shangari

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Integrating artificial intelligence (AI) in the cyber insurance sector represents a significant advancement, offering the potential to revolutionize risk assessment, fraud detection, and claims processing. However, this integration introduces a range of regulatory and economic challenges that must be addressed to ensure responsible and effective deployment of AI technologies. This paper examines the multifaceted regulatory landscape governing AI in cyber insurance and explores the economic implications of compliance, innovation, and market dynamics. AI's capabilities in processing vast amounts of data and identifying patterns make it an invaluable tool for insurers in managing cyber risks. Yet, the application of AI in this domain is subject to stringent regulatory scrutiny aimed at safeguarding data privacy, ensuring algorithmic transparency, and preventing biases. Regulatory bodies, such as the European Union with its General Data Protection Regulation (GDPR), mandate strict compliance requirements that can significantly impact the deployment of AI systems. These regulations necessitate robust data protection measures, ethical AI practices, and clear accountability frameworks, all of which entail substantial compliance costs for insurers. The economic implications of these regulatory requirements are profound. Insurers must invest heavily in upgrading their IT infrastructure, implementing robust data governance frameworks, and training personnel to handle AI systems ethically and effectively. These investments, while essential for regulatory compliance, can strain financial resources, particularly for smaller insurers, potentially leading to market consolidation. Furthermore, the cost of regulatory compliance can translate into higher premiums for policyholders, affecting the overall affordability and accessibility of cyber insurance. Despite these challenges, the potential economic benefits of AI integration in cyber insurance are significant. AI-enhanced risk assessment models can provide more accurate pricing, reduce the incidence of fraudulent claims, and expedite claims processing, leading to overall cost savings and increased efficiency. These efficiencies can improve the competitiveness of insurers and drive innovation in product offerings. However, balancing these benefits with regulatory compliance is crucial to avoid legal penalties and reputational damage. The paper also explores the potential risks associated with AI integration, such as algorithmic biases that could lead to unfair discrimination in policy underwriting and claims adjudication. Regulatory frameworks need to evolve to address these issues, promoting fairness and transparency in AI applications. Policymakers play a critical role in creating a balanced regulatory environment that fosters innovation while protecting consumer rights and ensuring market stability. In conclusion, the integration of AI in cyber insurance presents both regulatory and economic challenges that require a coordinated approach involving regulators, insurers, and other stakeholders. By navigating these challenges effectively, the industry can harness the transformative potential of AI, driving advancements in risk management and enhancing the resilience of the cyber insurance market. This paper provides insights and recommendations for policymakers and industry leaders to achieve a balanced and sustainable integration of AI technologies in cyber insurance.

Keywords: artificial intelligence (AI), cyber insurance, regulatory compliance, economic impact, risk assessment, fraud detection, cyber liability insurance, risk management, ransomware

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Authors: P. Sawatnatee, S. Udomsuk, K-N. Areerak, K-L. Areerak, A. Srikaew

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The paper presents how to design the indirect vector control of three-phase induction motor drive systems using the artificial intelligence technique called the adaptive tabu search. The results from the simulation and the experiment show that the drive system with the controller designed from the proposed method can provide the best output speed response compared with those of the conventional method. The controller design using the proposed technique can be used to create the software package for engineers to achieve the optimal controller design of the induction motor speed control based on the indirect vector concept.

Keywords: indirect vector control, induction motor, adaptive tabu search, control design, artificial intelligence

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Authors: Pawit Monkolprasit, Proud Arunrangsiwed

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

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

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Authors: Chen Xi, Kuan Sinman, LI Haofeng, Huang Hongming, Zeng Chengyu, Tong Zhiyuan

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Stray animals are on the rise in mainland China's cities. There are legal reasons for this, namely the lack of protection for domestic pets in mainland China, where only wildlife protection laws exist. At a social level, the ease with which families adopt pets and the lack of a social view of animal nature has led to the frequent abandonment and loss of stray animals. If left unmanaged, conflicts between humans and stray animals can also increase. This project provides an inexpensive and widely applicable management tool for urban management by collecting videos and pictures of stray animals captured by surveillance or transmitted by humans and using artificial intelligence technology (mainly using YOLOv5 recognition technology) and recording and managing them in a database.

Keywords: urban planning, urban governance, artificial intelligence, convolutional neural network

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Authors: G. Gnanachandrasamy, Y. Zhou, S. Venkatramanan, T. Ramkumar, S. Wang

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The natural resources of groundwater are the most precious resources around the world that balances are shrinking day by day. In connection, there is an urgency need for demarcation of potential groundwater zone. For these rationale integration of geographical information system (GIS) and remote sensing techniques (RS) for the hydrological studies have become a dramatic change in the field of hydrological research. These techniques are provided to locate the potential zone of groundwater. This research has been made to indent groundwater potential zone in Ariyalur of the southern part of India with help of GIS and remote sensing techniques. To identify the groundwater potential zone used by different thematic layers of geology, geomorphology, drainage, drainage density, lineaments, lineaments density, soil and slope with inverse distance weighting (IDW) methods. From the overall result reveals that the potential zone of groundwater in the study area classified into five classes named as very good (12.18 %), good (22.74 %), moderate (32.28 %), poor (27.7 %) and very poor (5.08 %). This technique suggested that very good potential zone of groundwater occurred in patches of northern and central parts of Jayamkondam, Andimadam and Palur regions in Ariyalur district. The result exhibited that inverse distance weighting method offered in this research is an effective tool for interpreting groundwater potential zones for suitable development and management of groundwater resources in different hydrogeological environments.

Keywords: GIS, groundwater potential zone, hydrology, remote sensing

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Authors: Arpan Paul, Joy Sen

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Livability is a holistic concept whose factors include many complex characteristics and levels of interrelationships among them. It has been considered as people’s need for public amenities and is recognized as a major element to create social welfare. The concept and principles of livability are essential for recognizing the significance of community well-being. The attributes and dimensions of livability are also important aspects to measure the overall quality of environment. Livability potential is mainly considered as the capacity to develop into the overall well-being of an urban area in future. The intent of the present study is to identify the prime factors to evaluate livability potential within a metropolis. For ground level case study, the paper has selected Kolkata Metropolitan Area (KMA) as it has wide physical, social, and economic variations within it. The initial part of the study deals with detailed literature review on livability and its significance of evaluating its potential within a metropolis. The next segment is dedicated for identifying the primary factors which would evaluate livability potential within a metropolis. In pursuit of identifying primary factors, which have a direct impact on urban livability, this study delineates the metropolitan area into various clusters, having their distinct livability potential. As a final outcome of the study, variations of livability potential of those selected clusters are highlighted to explain the complexity of the metropolitan development.

Keywords: Kolkata Metropolitan Area (KMA), livability potential, metropolis, wellbeing

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Authors: Shivkanya Fuloria, Neeraj Kumar Fuloria, Sokinder Kumar

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m-Cresol and oxadiazoles are the potent antimicrobial moieties. 2-(m-Tolyloxy)acetohydrazide (1) on cyclization with aromatic acids yielded 2-(aryl)-5-(m-tolyloxymethyl)-1,3,4-oxadiazole (1A-E). The structures of newer oxadiazoles were confirmed by elemental and spectral analysis. The newer compounds were evaluated for their antimicrobial potential. The compound 1E containing strong electron withdrawing group showed maximum antimicrobial potential. Other compounds also displayed antimicrobial potential to certain extent. The SAR of newer oxadiazoles indicated that substitution of strong electronegative group in the tolyloxy derived oxadiazoles enhanced their antimicrobial potential.

Keywords: antibacterial, cresol, hydrazide, oxadiazoles

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Authors: Kolade Olawande Owoeye, George Weir

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Terrorism and extremism activities on the internet are becoming the most significant threats to national security because of their potential dangers. In response to this challenge, law enforcement and security authorities are actively implementing comprehensive measures by countering the use of the internet for terrorism. To achieve the measures, there is need for intelligence gathering via the internet. This includes real-time monitoring of potential websites that are used for recruitment and information dissemination among other operations by extremist groups. However, with billions of active webpages, real-time monitoring of all webpages become almost impossible. To narrow down the search domain, there is a need for efficient webpage classification techniques. This research proposed a new approach tagged: SentiPosit-based method. SentiPosit-based method combines features of the Posit-based method and the Sentistrenght-based method for classification of terrorism and extremism webpages. The experiment was carried out on 7500 webpages obtained through TENE-webcrawler by International Cyber Crime Research Centre (ICCRC). The webpages were manually grouped into three classes which include the ‘pro-extremist’, ‘anti-extremist’ and ‘neutral’ with 2500 webpages in each category. A supervised learning algorithm is then applied on the classified dataset in order to build the model. Results obtained was compared with existing classification method using the prediction accuracy and runtime. It was observed that our proposed hybrid approach produced a better classification accuracy compared to existing approaches within a reasonable runtime.

Keywords: sentiposit, classification, extremism, terrorism

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

Abstract:

In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Jui Afrin, Akhtarul Islam

Abstract:

In this paper, the solution of glucose, yeast and glucose yeast mixture are being used as sample solution for determining the chemical oxygen demand (COD). In general COD determination method used to determine the different rang of oxidative potential. But in this work has shown to determine the definite oxidative potential for different concentration for known COD value and wanted to see the difference between experimental value and the theoretical value for evaluating the method drawbacks. In this study, made the values of oxidative potential like 400 mg/L, 500 mg/L, 600 mg/L, 700 mg/L and 800mg/L for various sample solutions and determined the oxidative potential according to our developed method. Plotting the experimental COD values vs. sample solutions of various concentrations in mg/L to draw the curve. From these curves see that the curves for glucose solution is not linear; its deviate from linearity for the lower concentration and the reason for this deviation is unknown. If these drawback can be removed this method can be effectively used to determine Oxidative Potential of Industrial wastewater (such as: Leather industry wastewater, Municipal wastewater, Food industry wastewater, Textile wastewater, Pharmaceuticals waste water) that’s why more experiment and study required.

Keywords: bod (biological oxygen demand), cod (chemical oxygen demand), oxidative potential, titration, waste water, development

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

Abstract:

This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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

Abstract:

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

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

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Authors: Chen Xi, LIU Xuebin, Kuan Sinman, LI Haofeng, Huang Hongming, Zeng Chengyu, Lao Xuerui

Abstract:

Stray animals are on the rise in mainland China's cities. There are legal reasons for this, namely the lack of protection for domestic pets in mainland China, where only wildlife protection laws exist. At a social level, the ease with which families adopt pets and the lack of a social view of animal nature have led to the frequent abandonment and loss of stray animals. If left unmanaged, conflicts between humans and stray animals can also increase. This project provides an inexpensive and widely applicable management tool for urban management by collecting videos and pictures of stray animals captured by surveillance or transmitted by humans and using artificial intelligence technology (mainly using Yolov5 recognition technology) and recording and managing them in a database.

Keywords: urban planning, urban governance, artificial intelligence, convolutional neural network, machine vision

Procedia PDF Downloads 98