Search results for: edge intelligence

Authors: Y. Wang

Abstract:

The Traveling salesman problem (TSP) is NP-hard in combinatorial optimization. The research shows the algorithms for TSP on the sparse graphs have the shorter computation time than those for TSP according to the complete graphs. We present an improved iterative algorithm to compute the sparse graphs for TSP by frequency graphs computed with frequency quadrilaterals. The iterative algorithm is enhanced by adjusting two parameters of the algorithm. The computation time of the algorithm is O(CN_maxn²) where C is the iterations, N_max is the maximum number of frequency quadrilaterals containing each edge and n is the scale of TSP. The experimental results showed the computed sparse graphs generally have less than 5n edges for most of these Euclidean instances. Moreover, the maximum degree and minimum degree of the vertices in the sparse graphs do not have much difference. Thus, the computation time of the methods to resolve the TSP on these sparse graphs will be greatly reduced.

Keywords: frequency quadrilateral, iterative algorithm, sparse graph, traveling salesman problem

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Authors: Mohamed Gaith, Zaid Haddadin, Abdulah Wahbe, Mahmoud Hamam, Mahmoud Qunees, Mohammad Al Khatib, Mohammad Bsaileh, Abd Al-Aziz Jaber, Ahmad Aqra’a

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The crack is one of the most common types of failure in pipelines that convey fluid, and early detection of the crack may assist to avoid the piping system from experiencing catastrophic damage, which would otherwise be fatal. The influence of flow velocity and the presence of a crack on the performance of a tapered simply supported pipe containing moving fluid is explored using the finite element approach in this study. ANSYS software is used to simulate the pipe as Bernoulli's beam theory. In this paper, the fluctuation of natural frequencies and matching mode shapes for various scenarios owing to changes in fluid speed and the presence of damage is discussed in detail.

Keywords: damage detection, finite element, tapered pipe, vibration characteristics

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Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Dinku Seyoum Zeleke, Rong Fung Huang, Ching Min Hsu

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Comparison of flow and mixing characteristics between non-oscillating jet and transversely oscillating jet was investigated experimentally. Flow evolution process was detected by using high-speed digital camera, and jet spread width was calculated using binary edge detection techniques by using the long-exposure images. The velocity characteristics of transversely oscillating jet induced by a V-shaped fluidic oscillator were measured using single component hot-wire anemometer. The jet spread width of non-oscillating jet was much smaller than the jet exit gap because of behaving natural jet behaviors. However, the transversely oscillating jet has a larger jet spread width, which was associated with the excitation of the flow by self-induced oscillation. As a result, the flow mixing characteristics desperately improved both near-field and far-field. Therefore, this transversely oscillating jet has a better turbulence intensity, entrainment, and spreading width so that it augments flow-mixing characteristics desperately.

Keywords: flow mixing, transversely oscillating, spreading width, velocity characteristics

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Authors: Biniam Bahiru Tufa, Hana Delil Tesfaye, Seife Demisse Legesse, Manaye Tamire

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The healthcare industry is witnessing a digital transformation, with artificial intelligence (AI) offering potential solutions for challenges in health supply chain management (HSCM). However, the adoption of AI in this field remains limited. This research aimed to assess the knowledge, attitude, and practice of AI among students and employees in the health supply chain sector in Ethiopia. Using an explanatory case study research design with a concurrent mixed approach, quantitative and qualitative data were collected simultaneously. The study included 153 participants comprising students and employed health supply chain professionals working in various sectors. The majority had a pharmacy background, and one-third of the participants were male. Most respondents were under 35 years old, and around 68.6% had less than 10 years of experience. The findings revealed that 94.1% of participants had prior knowledge of AI, but only 35.3% were aware of its application in the supply chain. Moreover, the majority indicated that their training curriculum did not cover AI in health supply chain management. Participants generally held positive attitudes toward the necessity of AI for improving efficiency, effectiveness, and cost savings in the supply chain. However, many expressed concerns about its impact on job security and satisfaction, considering it as a burden Graduate students demonstrated higher knowledge of AI compared to employed staff, while graduate students also exhibited a more positive attitude toward AI. The study indicated low previous utilization and potential future utilization of AI in the health supply chain, suggesting untapped opportunities for improvement. Overall, while supply chain experts and graduate students lacked sufficient understanding of AI and its significance, they expressed favorable views regarding its implementation in the sector. The study recommends that the Ethiopian government and international organizations consider introducing AI in the undergraduate pharmacy curriculum and promote its integration into the health supply chain field.

Keywords: knowledge, attitude, practice, supply chain, articifial intellegence

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Authors: Svitov David, Alyamkin Sergey

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The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax

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Authors: Mikko Salminen, Niklas Ravaja

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Emotional intelligence (EI) consists of skills for monitoring own emotions and emotions of others, skills for discriminating different emotions, and skills for using this information in thinking and actions. EI enhances, for example, work outcomes and organizational climate. We suggest that the role and manifestations of EI should also be studied in real leadership situations, especially during the emotional, social interaction. Leadership is essentially a process to influence others for reaching a certain goal. This influencing happens by managerial processes and computer-mediated communication (e.g. e-mail) but also by face-to-face, where facial expressions have a significant role in conveying emotional information. Persons with high EI are typically perceived more positively, and they have better social skills. We hypothesize, that during social interaction high EI enhances the ability to detect other’s emotional state and controlling own emotional expressions. We suggest, that emotionally intelligent leader’s experience less stress during social leadership situations, since they have better skills in dealing with the related emotional work. Thus the high-EI leaders would be more able to enjoy these situations, but also be more efficient in choosing appropriate expressions for building constructive dialogue. We suggest, that emotionally intelligent leaders show more positive emotional expressions than low-EI leaders. To study these hypotheses we observed performance review discussions of 40 leaders (24 female) with 78 (45 female) of their followers. Each leader held a discussion with two followers. Psychophysiological methods were chosen because they provide objective and continuous data from the whole duration of the discussions. We recorded sweating of the hands (electrodermal activation) by electrodes placed to the fingers of the non-dominant hand to assess the stress-related physiological arousal of the leaders. In addition, facial electromyography was recorded from cheek (zygomaticus major, activated during e.g. smiling) and periocular (orbicularis oculi, activated during smiling) muscles using electrode pairs placed on the left side of the face. Leader’s trait EI was measured with a 360 questionnaire, filled by each leader’s followers, peers, managers and by themselves. High-EI leaders had less sweating of the hands (p = .007) than the low-EI leaders. It is thus suggested that the high-EI leaders experienced less physiological stress during the discussions. Also, high scores in the factor “Using of emotions” were related to more facial muscle activation indicating positive emotional expressions (cheek muscle: p = .048; periocular muscle: p = .076, almost statistically significant). The results imply that emotionally intelligent managers are positively relaxed during s social leadership situations such as a performance review discussion. The current study also highlights the importance of EI in face-to-face social interaction, given the central role facial expressions have in interaction situations. The study also offers new insight to the biological basis of trait EI. It is suggested that the identification, forming, and intelligently using of facial expressions are skills that could be trained during leadership development courses.

Keywords: emotional intelligence, leadership, performance review discussion, psychophysiology, social interaction

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Authors: Aparna Saha, Tuhin Ahmed

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West Bengal has a number of important rivers, each with its distinctive character and a story. Traditionally, cities have ‘divulged’ to rivers at the river edges and rivers have been an inseparable part of the urban experience. Considering the research aspect, the area is taken in Barrackpore, a small but important outgrowth of Kolkata Municipal Association, West Bengal. Barrackpore, at present, has ample inadequate public open spaces at the neighborhood level where people of different socio-cultural, economic, and religious backgrounds can come together and engage in various leisure activities, but there is no opportunity either, where people can learn about and explore the rich history of the settlement. Pertaining to these issues forms the backdrop of this research paper which has been conceptualized as a place from space that will bring people back to the river and increase community interactions and will also celebrate and commemorate towards the historical importance of the river and its edges. The entire precinct bordering the river represents the transition from pre-independence (Raj era) to Sepoy phase (Swaraj era), finally culminating into the Gandhian philosophy which is being projected into the already existing Gandhi Ghat. The ultimate aim of the paper entitled ‘Utsadhara- Rejuvenating the dead river edge into an urban activity space along the banks of river Hooghly’ is to create a socio-cultural space keeping the heritage identity intact through judicious use of the water body. Also, a balance is kept between the natural ecosystem and the cosmetic development of the surrounding open spaces. It can be duly achieved by the aforementioned methodology provided in the document, but mainly it would focus into preserving the historic ethnicity of the place by holding its character through various facts and figures as well as features. Most importantly the natural topography of the place is left intact. The second priority is given in terms of hierarchy of well connected public plazas, podiums where people from different socio-economic backgrounds irrespective of age and sex could socialize and reach towards venturing into a cordial relationship with one another. The third priority is to provide a platform for the common mass for showcasing their skills and talent through different art and craft forms which in turn would enhance their individual self and also the community as a whole through economic rise. Apart from this here some spaces are created in accordance to different age groups or class of people. The paper intends to see the river as a major multifunctional public space to attract people for different activities and re-establish the relationship of the river with the settlement. Hence, it is apprehended that the paper is not only intended to a simple riverfront conservation project but unlike others it is a place which is created for the people, by the people and of the people towards a holistic community development through a sustainable approach.

Keywords: holistic community development, public activity space, river-urban precinct, urban dead space

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Ashish Kumar, Tejendra Dixit, I. A. Palani, Vipul Singh

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Zinc oxide, with its interesting properties such as large band gap (3.37eV), high exciton binding energy (60 meV) and intense UV absorption has been studied in literature for various applications viz. optoelectronics, biosensors, UV-photodetectors etc. The performance of ZnO devices is highly influenced by morphologies, size, crystallinity of the ZnO active layer and processing conditions. Recently, our group has shown the influence of the in situ addition of KMnO4 in the precursor solution during the hydrothermal growth of ZnO nanorods (NRs) on their near band edge (NBE) emission. In this paper, we have investigated the effect of post-growth annealing on the variations in NBE and deep level (DL) emissions of as grown ZnO nanorods. These observed results have been explained on the basis of X-ray Diffraction (XRD) and Raman spectroscopic analysis, which clearly show that improved crystalinity and quantum confinement in ZnO nanorods.

Keywords: ZnO, nanorods, hydrothermal, KMnO4

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Authors: Thomas Odaker, Dieter Kranzlmueller, Jens Volkert

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We present an approach to triangle mesh simplification designed to be executed on the GPU. We use a quadric error metric to calculate an error value for each vertex of the mesh and order all vertices based on this value. This step is followed by the parallel removal of a number of vertices with the lowest calculated error values. To allow for the parallel removal of multiple vertices we use a set of per-vertex boundaries that prevent mesh foldovers even when simplification operations are performed on neighbouring vertices. We execute multiple iterations of the calculation of the vertex errors, ordering of the error values and removal of vertices until either a desired number of vertices remains in the mesh or a minimum error value is reached. This parallel approach is used to speed up the simplification process while maintaining mesh topology and avoiding foldovers at every step of the simplification.

Keywords: computer graphics, half edge collapse, mesh simplification, precomputed simplification, topology preserving

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Authors: Ilyes Athimni, Mouna Bouzazi, Mongi Boulehmi, Ahmed Ferchichi

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The principles of good governance, universal values, and digitization are among the tools to fight corruption and improve the quality of service delivery. In recent years, these tools have become one of the most controversial topics in the field of education and a concern of many international organizations and institutions against the problem of corruption. Corruption in the education sector, particularly in higher education, has negative impacts on the quality of education systems and on the quality of administrative or educational services. Currently, the health crisis due to the spread of the COVID-19 pandemic reveals the difficulties encountered by education systems in most countries of the world. Due to the poor governance of these systems, many educational institutions were unable to continue working remotely. To respond to these problems encountered by most education systems in many countries of the world, our initiative is to propose a methodology to reinvent education systems based on global values and digital technologies. This methodology includes a work strategy for educational institutions, whether in the provision of administrative services or in the teaching method, based on information and communication technologies (ICTs), intelligence artificial, and intelligent agents. In addition, we will propose a supervisory law that will be implemented and monitored by intelligent agents to improve accountability, transparency, and accountability in educational institutions. On the other hand, we will implement and evaluate a field experience by applying the proposed methodology in the operation of an educational institution and comparing it to the traditional methodology through the results of teaching an educational program. With these specifications, we can reinvent quality education systems. We also expect the results of our proposal to play an important role at local, regional, and international levels in motivating governments of countries around the world to change their university governance policies.

Keywords: artificial intelligence, corruption in education, distance learning, education systems, ICTs, intelligent agents, good governance

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Authors: Martina Manenova, Lukas Cirus

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This article introduces research focused on elementary school teachers’ approach to innovations in ICT. The diffusion of innovations theory, which was written by E. M. Rogers, captures the processes of innovation adoption. The research method derived from this theory and the Rogers’ questionnaire focused on the diffusion of innovations was used as the basic research method. The research sample consisted of elementary school teachers. The comparison of results with the Rogers’ results shows that among the teachers in the research sample the so-called early majority, as well as the overall division of the data, was rather central (early adopter, early majority, and later majority). The teachers very rarely appeared on the edge positions (innovator, laggard). The obtained results can be applied to teaching practice and used especially in the implementation of new technologies and techniques into the educational process.

Keywords: innovation, diffusion of innovation, information and communication technology, teachers

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

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Authors: Chryssi Vitsilaki, Apostolos Kostas, Ilias Efthymiou

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This paper presents selected best practices on online learning and teaching derived from a novel and innovating Lifelong Learning program through e-Learning, which has during the last five years been set up at the University of the Aegean in Greece. The university, capitalizing on an award-winning, decade-long experience in e-learning and blended learning in undergraduate and postgraduate studies, recently expanded into continuous education and vocational training programs in various cutting-edge fields. So, in this article we present: (a) the academic structure/infrastructure which has been developed for the administrative, organizational and educational support of the e-Learning process, including training the trainers, (b) the mode of design and implementation based on a sound pedagogical framework of open and distance education, and (c) the key results of the assessment of the e-learning process by the participants, as they are used to feedback on continuous organizational and teaching improvement and quality control.

Keywords: distance education, e-learning, life-long programs, synchronous/asynchronous learning

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Authors: C. V. Chen, Y. H. Jeng, S. J. Wang

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Leadership is essential to enhancing followers’ psychological empowerment and has an effect on their willingness to take on extra-role behavior and aim for greater performance. Authentic leadership is confirmed to promote employees’ positive affect, psychological empowerment, well-being, and performance. Employees’ spontaneous undertaking of organizationally desired behaviors allows organizations’ gaining the edge in the fiercely competitive business environment. Apart from the contextual factor of leadership, individuals’ goal orientation is found to be highly related to his/her performance. To better understand the psychological process and potential moderation of personal goal orientation, this study investigates the effect of authentic leadership on employees’ task performance and organizational citizenship behavior by including psychological empowerment as the mediating factor and goal orientation as the moderating factor.

Keywords: authentic leadership, task performance, organizational citizenship behavior, goal orientation

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Authors: Amy Gooden

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When human genomic data is considered, this is often done through only one dimension of the law, or the interplay between the various dimensions is not considered, thus providing an incomplete picture of the legal framework. This research considers and analyzes the various dimensions in South African law applicable to genomic sequence data – including property rights, personality rights, and intellectual property rights. The effective use of personal genomic sequence data requires the acknowledgement and harmonization of the rights applicable to such data.

Keywords: artificial intelligence, data, law, genomics, rights

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Authors: A. Badran, M. Hollocks, H. Markus

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Background: Common risk factors for cardiovascular disease are associated with age-related cognitive decline. There has been much interest in treating modifiable cardiovascular risk factors in the hope of reducing cognitive decline. However, there is currently no validated neuropsychological test to assess the subclinical cognitive effects of vascular risk. The Brief Memory and Executive Test (BMET) is a clinical screening tool, which was originally designed to be sensitive and specific to Vascular Cognitive Impairment (VCI), an impairment characterised by decline in frontally-mediated cognitive functions (e.g. Executive Function and Processing Speed). Objective: To cross-sectionally assess the validity of the BMET as a measure of the subclinical effects of vascular risk on cognition, in an otherwise healthy elderly cohort. Methods: Data from 346 participants (57 ± 10 years) without major neurological or psychiatric disorders were included in this study, gathered as part of a previous multicentre validation study for the BMET. Framingham Vascular Age was used as a surrogate measure of vascular risk, incorporating several established risk factors. Principal Components Analysis of the subtests was used to produce common constructs: an index for Memory and another for Executive Function/Processing Speed. Univariate General Linear models were used to relate Vascular Age to performance on Executive Function/Processing Speed and Memory subtests of the BMET, adjusting for Age, Premorbid Intelligence and Ethnicity. Results: Adverse vascular risk was associated with poorer performance on both the Memory and Executive Function/Processing Speed indices, adjusted for Age, Premorbid Intelligence and Ethnicity (p=0.011 and p<0.001, respectively). Conclusions: Performance on the BMET reflects the subclinical effects of vascular risk on cognition, in age-related cognitive decline. Vascular risk is associated with decline in both Executive Function/Processing Speed and Memory groups of subtests. Future studies are needed to explore whether treating vascular risk factors can effectively reduce age-related cognitive decline.

Keywords: age-related cognitive decline, vascular cognitive impairment, subclinical cerebrovascular disease, cognitive aging

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Authors: Ebenezer Obaloluwa Olaniyi

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In this paper, an intelligent system has been designed to grade apple based on either its defective or healthy for production in food processing. This paper is segmented into two different phase. In the first phase, the image processing techniques were employed to extract the necessary features required in the apple. These techniques include grayscale conversion, segmentation where a threshold value is chosen to separate the foreground of the images from the background. Then edge detection was also employed to bring out the features in the images. These extracted features were then fed into the neural network in the second phase of the paper. The second phase is a classification phase where neural network employed to classify the defective apple from the healthy apple. In this phase, the network was trained with back propagation and tested with feed forward network. The recognition rate obtained from our system shows that our system is more accurate and faster as compared with previous work.

Keywords: image processing, neural network, apple, intelligent system

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Authors: Moor Avigail

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This research examines gender norms that underlie the propensity to commit sexual assault and to carry it out. Factors that have been shown to relate to such propensity will be enumerated and tied to their ramifications. These include sexual objectification of women, endorsement of gender-based rape myths blaming the victim, masculine entitlement, low empathy to victims, along with elevated empathy towards rapists. Heavy use of pornography as well as a misconstruing of the meaning of refusal to sex, have also been implicated. Additionally, a cutting-edge investigation, which we have just completed, examined what seems to occur in the perpetrator's mind during the assault. No research to date has ventured to uncover what essentially allows the rape to be carried out in real time, in the sense of what mental mechanisms go into operation in rapists during the assault itself. Our findings demonstrate that dehumanization and rationalization are pivotal. On the one hand, the perpetrator apparently allows himself to disregard the victim's humanity while simultaneously justifying his actions in relation to the victim's behavior.

Keywords: gender norms, gender psychology, sexual assault, gender

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: P. Jayashree, S. Rajkumar

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With the trend of virtual and augmented reality environments booming to provide a life like experience, gaming is a major tool in supporting such learning environments. In this work, a variant of Voronoi heuristics, employing supervised learning for the TRON game is proposed. The paper discusses the features that would be really useful when a machine learning bot is to be used as an opponent against a human player. Various game scenarios, nature of the bot and the experimental results are provided for the proposed variant to prove that the approach is better than those that are currently followed.

Keywords: artificial Intelligence, automation, machine learning, TRON game, Voronoi heuristics

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Authors: Radhwane Boudjelthia

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Based upon elaborate analysis on torsional design methods of asymmetric and irregular structure under horizontal earthquake action, it points out that the main design principles of an asymmetric building subjected to horizontal earthquake are: the torsion of vertical members induced by the torsion angle of the floor (rigid diaphragm) cannot exceed the allowable value, the inter-story displacement at outermost frame or shear wall should be less than that required by design code, stresses in plane of the slab should be controlled within acceptable extent under different intensity earthquakes. That current seismic design code only utilizes the torsion displacement ratio to control the floor torsion, which seems not reasonable enough since its connotation is the multiple of the floor torsion angle and the distance of floor mass center to the edge frame or shear wall.

Keywords: earthquake, building, seismic forces, displacement, resonance, response

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Authors: Nayer Mofidtabatabaei

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Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

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Authors: Naser Ojaroudi Parchin, Atta Ullah, Haleh Jahanbakhsh Basherlou, Raed A. Abd-Alhameed, Peter S. Excell

Abstract:

A design of beam-steerable antenna array for the future cellular communication (5G) is presented. The proposed design contains eight elements of compact end-fire antennas arranged on the top edge of smartphone printed circuit board (PCB). Configuration of the antenna element consists of the conductive patterns on the top and bottom copper foil layers and a substrate layer with a via-hole. The simulated results including input-impedance and also fundamental radiation properties have been presented and discussed. The impedance bandwidth (S₁₁ ≤ -10 dB) of the antenna spans from 17.5 to 21 GHz (more than 3 GHz bandwidth) with a resonance at 19 GHz. The antenna exhibits end-fire (directional) radiation beams with wide-angle scanning property and could be used for the future 5G beam-forming. Furthermore, the characteristics of the array design in the vicinity of user-hand are studied.

Keywords: beam-steering, end-fire radiation mode, mobile-phone antenna, phased array

Procedia PDF Downloads 151

Authors: Milind Anurag

Abstract:

This study investigates cutting-edge truss design improvements, which are specifically adapted to satisfy the structural demands and difficulties associated with high-performance facades and roofs in modern architectural environments. With a growing emphasis on sustainability, energy efficiency, and eye-catching architectural aesthetics, the structural components that support these characteristics play an important part in attaining the right balance of form and function. The paper seeks to contribute to the evolution of truss design methods by combining data from these investigations, giving significant insights for architects, engineers, and researchers interested in the creation of high-performance building envelopes. The findings of this study are meant to inform future design standards and practices, promoting the development of structures that seamlessly integrate architectural innovation with structural robustness and environmental responsibility.

Keywords: truss design, high-performance, facades, finite element analysis, structural efficiency

Procedia PDF Downloads 50

Authors: Abdoulnabi Tavangari, Nasim Salehzadeh

Abstract:

We presume a cylindrical medium that is under a uniform loading and there is a penny shaped crack located in the center of cylinder. In the crack growth analysis, the Stress Intensity Factor (SIF) is a fundamental prerequisite. In the present study, according to the RITZ method and by considering a cylindrical coordinate system as the main coordinate and a local polar coordinate, the mode-I SIF of threedimensional penny-shaped crack is obtained. In this method the unknown coefficients will be obtained with minimizing the potential energy that is including the strain energy and the external force work. By using the hook's law, stress fields will be obtained and then by using the Irvine equations, the amount of SIF will be obtained near the edge of the crack. This question has been solved for extreme medium in the Tada handbook and the result of the present research has been compared with that.

Keywords: three-dimensional cracks, penny-shaped crack, stress intensity factor, fracture mechanics, Ritz method

Procedia PDF Downloads 393

Authors: Shahriyar Keshtgar, Mahmoud Reza Heyhat, Sasan Bagheri, Ebrahim Gholami, Seyed Naser Raiisosadat

Abstract:

The eastern Iranian range is a Z-shaped sigmoidal outcrop appearing with a NS-trending general strike on the satellite images, has already been known as the Sistan suture zone, recently identified as the product of an orogenic event introduced either by the Paleogene or Sistan orogen names. The flysch sedimentary basin of eastern Iran was filled by a huge volume of fine-grained Eocene turbiditic sediments, smaller amounts of pelagic deposits and Cretaceous ophiolitic slices, which are entirely remnants of older accretionary prisms appeared in a fold-thrust belt developed onto a subduction zone under the Lut/Afghan block, portions of the Cimmerian superterrane. In these ranges, there are Triassic sedimentary and carbonate sequences (equivalent to Nayband and Shotori Formations) along with scattered outcrops of Permian limestones (equivalent to Jamal limestone) and greenschist-facies metamorphic rocks, probably belonging to the basement of the Lut block, which have tectonic contacts with younger rocks. Moreover, the younger Eocene detrital-volcanic rocks were also thrusted onto the Cretaceous or younger turbiditic deposits. The first generation folds (parallel folds) and thrusts with slaty cleavage appeared parallel to the NE edge of the Lut block. Structural analysis shows that the most vergence of thrusts is toward the southeast so that the Permo-Triassic units in Lut have been thrusted on the younger rocks, including older (probably Jurassic) granites. Additional structural studies show that the regional transport direction in this deformation event is from northwest to the southeast where, from the outside to the inside of the orogen in the Sechengi area. Younger thrusts of the second deformation event were either directly formed as a result of the second deformation event, or they were older thrusts that reactivated and folded so that often, two sets or more slickenlines can be recognized on the thrust planes. The recent thrusts have been redistributed in directions nearly perpendicular to the edge of the Lut block and parallel to the axial surfaces of the northwest second generation large-scale folds (radial folds). Some of these younger thrusts follow the out-of-the-syncline thrust system. The both axial planes of these folds and associated penetrative shear cleavage extended towards northwest appeared with both northeast and southwest dips parallel to the younger thrusts. The large-scale buckling with the layer-parallel stress field has created this deformation event. Such consecutive deformation events perpendicular to each other cannot be basically explained by the simple linear orogen models presented for eastern Iran so far and are more consistent with the oroclinal buckling model.

Keywords: thrust, tectonic vergence, orocline buckling, sechangi, eastern iranian ranges

Procedia PDF Downloads 74

Authors: Nadia el Atlas, Mohammed el Aroussi, Mohammed Wahbi

Abstract:

In this paper a novel technique of mass characterization based on robust features-fusion is presented. The proposed method consists of mainly four stages: (a) the first phase involves segmenting the masses using edge information’s. (b) The second phase is to calculate and fuse the most relevant morphological features. (c) The last phase is the classification step which allows us to classify the images into benign and malignant masses. In this step we have implemented Support Vectors Machines (SVM) and Artificial Neural Networks (ANN), which were evaluated with the following performance criteria: confusion matrix, accuracy, sensitivity, specificity, receiver operating characteristic ROC, and error histogram. The effectiveness of this new approach was evaluated by a recently developed database: INBREAST database. The fusion of the most appropriate morphological features provided very good results. The SVM gives accuracy to within 64.3%. Whereas the ANN classifier gives better results with an accuracy of 97.5%.

Keywords: breast cancer, mammography, CAD system, features, fusion

Procedia PDF Downloads 594