Search results for: 3D computer vision

Authors: Sydelle Barreto

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Sexual liberation has been thought of as a movement, an idea, and an antithesis of material oppression. Within feminism it has consistently resisted definition - different feminist schools of thought had entirely different constructions of what liberated feminine sexuality could look like and how we might get there. This paper will critique the current definition of sexual liberation as being limited and carceral in its perspectives of sexual assault and extremely reductive in its imaginings of sexual liberation. The ultimate goal of this assessment is to potentially outline what true sexual liberation might look like in a way that is inclusive but not ignorant of the realities of the patriarchy. The first critique of sexual liberation included in the paper centers around the limits of consent, carceral feminism and sexual subjectivity. The argument will build off the traditionally sex-negative critiques of consent as being limited in scope by explaining how a lack of nuance is even more dangerous to victims of sexual violations. The discussion will also expand an interrogant of consent to an interrogation of wantedness and desire. If we understand that critiquing the conditions of consent is important, we must also critique the way patriarchy and compulsory sexuality have affected desire. Using the aforementioned concept of compulsory sexuality, the paper will argue that while sexual liberation has begun to include queer and transgender individuals, it is still overwhelmingly allonormative. Sex positivity and its opponents both fail to include asexuality. This ultimately leads to a conflation of sexual liberation with genuine material liberation. Just as we cannot divorce our constructions of sexual liberation from the realities of the patriarchy and rape culture, we should consider compulsory sexuality as its own system of social regulation. The conclusion will begin to construct an alternative vision of sexual liberation, leveraging concepts of sexual subjectivity, including a rejection of carceral feminism as a response to sexual violence, and finally, leading to the beginnings of a deconstruction of compulsory sexuality. The paper concludes with a vision of sexual liberation that does not confuse itself with material liberation or mere sexual oppression, but rather a key way stops on the road to constructing our most authentic sexual selves.

Keywords: feminism, sexual assault, sexual liberation, consent

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Authors: Chih-Chun Lai, Jun-Yu Wang

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The keyboard is the most important equipment for computer tasks. However, improper design of keyboard would cause some symptoms like ulnar and/or radial deviations. The research goal of this study was to investigate the optimal size(s) of keycaps to increase efficiency. As shown in the questionnaire pre-study with 49 participants aged from 20 to 44, the most commonly used keyboards were 101-key standard keyboards. Most of the keycap sizes (W × L) were 1.3 × 1.5 cm and 1.5 × 1.5 cm. The fingertip breadths of most participants were 1.2 cm. Therefore, in the main study with 18 participants, a standard keyboard with each set of the 3-sized (1.2 × 1.4 cm, 1.3 × 1.5 cm, and 1.5 × 1.5 cm) keycaps was used to investigate their typing efficiency, respectively. The results revealed that the differences between the operating times for using 1.3 × 1.5 cm and 1.2 × 1.4 cm keycaps were insignificant while operating times for using 1.5 × 1.5 cm keycaps were significantly longer than for using 1.2 × 1.4 cm or 1.3 × 1.5 cm, respectively. As for the typing error rate, there was no significant difference.

Keywords: keyboard, keycap size, typing efficiency, computer tasks

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Authors: Mustafa Engin Başoğlu, Bekir Çakir

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Since large part of electricity generation is provided by using fossil based resources, energy is an important agenda for countries. Depletion of fossil resources, increasing awareness of climate change and global warming concerns are the major reasons for turning to alternative energy resources. Solar, wind and hydropower energy are the main renewable energy sources. Among of them, wind energy is promising for Turkey whose installed power capacity increases approximately eight times between 2008 - seventh month of 2014. Signing of Kyoto Protocol can be accepted as a milestone for Turkey's energy policy. Turkish government has announced 2023 Vision (2023 targets) in 2010-2014 Strategic Plan prepared by Ministry of Energy and Natural Resources (MENR). 2023 Energy targets can be summarized as follows: Share of renewable energy sources in electricity generation is 30% of total electricity generation by 2023. Installed capacity of wind energy will be 20 GW by 2023. Other renewable energy sources such as solar, hydropower and geothermal are encouraged with new incentive mechanisms. Share of nuclear power plants in electricity generation will be 10% of total electricity generation by 2023. Dependence on foreign energy is reduced for sustainability and energy security. As of seventh month of 2014, total installed capacity of wind power plants is 3.42 GW and a lot of wind power plants are under construction with capacity 1.16 GW. Turkish government also encourages the locally manufactured equipments. MILRES is an important project aimed to promote the use of renewable sources in electricity generation. A 500 kW wind turbine will be produced in the first phase of project. Then 2.5 MW wind turbine will be manufactured domestically within this project

Keywords: wind energy, wind speed, 2023 vision, MILRES, wind energy potential in TURKEY

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Authors: Shohreh Ghasemi

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Introduction: Melanoma is a complex disease with various clinical and histopathological features that impact prognosis and treatment decisions. Traditional methods of melanoma prognosis involve manual examination and interpretation of clinical and histopathological data by dermatologists and pathologists. However, the subjective nature of these assessments can lead to inter-observer variability and suboptimal prognostic accuracy. AI, with its ability to analyze vast amounts of data and identify patterns, has emerged as a promising tool for improving melanoma prognosis. Methods: A comprehensive literature search was conducted to identify studies that employed AI techniques for melanoma prognosis. The search included databases such as PubMed and Google Scholar, using keywords such as "artificial intelligence," "melanoma," and "prognosis." Studies published between 2010 and 2022 were considered. The selected articles were critically reviewed, and relevant information was extracted. Results: The review identified various AI methodologies utilized in melanoma prognosis, including machine learning algorithms, deep learning techniques, and computer vision. These techniques have been applied to diverse data sources, such as clinical images, dermoscopy images, histopathological slides, and genetic data. Studies have demonstrated the potential of AI in accurately predicting melanoma prognosis, including survival outcomes, recurrence risk, and response to therapy. AI-based prognostic models have shown comparable or even superior performance compared to traditional methods.

Keywords: artificial intelligence, melanoma, accuracy, prognosis prediction, image analysis, personalized medicine

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Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Murat Durucu

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Glaucoma is a condition of irreversible blindness, early diagnosis and appropriate interventions to make the patients able to see longer time. In this study, it addressed that the importance of developing a decision support system for glaucoma diagnosis. Glaucoma occurs when pressure happens around the eyes it causes some damage to the optic nerves and deterioration of vision. There are different levels ranging blindness of glaucoma disease. The diagnosis at an early stage allows a chance for therapies that slows the progression of the disease. In recent years, imaging technology from Heidelberg Retinal Tomography (HRT), Stereoscopic Disc Photo (SDP) and Optical Coherence Tomography (OCT) have been used for the diagnosis of glaucoma. This better accuracy and faster imaging techniques in response technique of OCT have become the most common method used by experts. Although OCT images or HRT precision and quickness, especially in the early stages, there are still difficulties and mistakes are occurred in diagnosis of glaucoma. It is difficult to obtain objective results on diagnosis and placement process of the doctor's. It seems very important to develop an objective decision support system for diagnosis and level the glaucoma disease for patients. By using OCT images and pattern recognition systems, it is possible to develop a support system for doctors to make their decisions on glaucoma. Thus, in this recent study, we develop an evaluation and support system to the usage of doctors. Pattern recognition system based computer software would help the doctors to make an objective evaluation for their patients. It is intended that after development and evaluation processes of the software, the system is planning to be serve for the usage of doctors in different hospitals.

Keywords: decision support system, glaucoma, image processing, pattern recognition

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Authors: A. Aldawsari

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The complex issue of women working in a mixed-gender work environment has its roots in social and cultural factors. This research was done to identify and explore the social and cultural challenges Saudi women face in a mixed-gender work environment in Saudi Arabia. Over the years, Saudi women in mixed-gender work environments in Saudi Arabia have been of interest in various research areas, especially within the context of a hospital work environment. This research, which involves a female researcher interacting one-on-one with Saudi women, will address this issue as well as the effect of the 2030 Vision in Saudi Arabia, and it will aim to include several new fields of work environments for women in Saudi Arabia. The aim of this research is to examine the perceptions of Saudi women who work in a mixed gender environment regarding the general empowerment of women in these settings. The objective of this research is to explore the cultural and social challenges that influence Saudi women's rights to work in a mixed-gender environment in Saudi Arabia. The significance of this research lies in the fact that there is an urgency to resolve issue of female employment in Saudi Arabia, where Saudi women still suffer from inequality in employment opportunity. Although the Saudi government is seeking to empower women by integrating them into a mixed-gender work environment, which is a key goal and prominent social change advocated for in the 2030 Vision, this same goal is one of the main challenges in the face of achieving female empowerment. The methodology section focuses on appropriate methods that can be used to study the effect of social and cultural challenges on the employment of women. It then determines the conditions and limitations of the research by applying a qualitative research approach to the investigation and analysing the data collected from the interviews. A statistical analysis tool, such as NVivo, will be used for the qualitative analysis of the interviews. The study found that the factor most responsible for creating social and cultural challenges is family—whether close family or distant family—more so than tribe or community.

Keywords: women, work, mixed-gender, environment

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Authors: Mohamed Lotfy, Ghada Soliman

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Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on Convolutional Neural Networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the digit images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can also be extended to text recognition tasks, such as text classification and sentiment analysis.

Keywords: computer vision, pattern recognition, optical character recognition, deep learning

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Authors: Yotaro Natani

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In his recent works, Frankfurt School theorist Axel Honneth elucidates an intersubjective notion of social freedom and outlines a vision of socialism as the realization of social freedom in the family, market economy, and public sphere. These arguments are part of his broader project of defending the tradition of immanent critique and normative reconstruction. In contrast, American Marxist sociologist Erik Olin Wright spells out a vision of socialism in terms of building real utopias -democratic, egalitarian, alternative institutions- through the exercise of civil society’s social power over the economy and state. Wright identifies ‘eroding capitalism’ as the framework for thinking about the strategic logics of gradually diminishing the dominance of capitalism. Both thinkers envision the transition toward socialism in terms of democratic experimentation; Honneth is more attentive to the immanent norms of social life, whereas Wright is better aware of the power of antagonistic structures. This paper attempts to synthesize the ideas of Honneth and Wright. It will show that Honneth’s critique of capitalism suffers from certain ambiguities because he attributes normative legitimacy to existing institutions, resulting in arguments that do not problematize aspects of capitalist structures. This paper will argue that incorporating the notion of power and thematizing the erosion of capitalism as a long-term goal for socialist change will allow Honneth to think more precisely about the conditions for realizing social freedom, in a manner that is still consistent with the immanent critique tradition. Such reformulation will result in a concept of social freedom that is less static and rooted in functional teleology and more oriented toward creative agency and experimental democracy.

Keywords: Axel Honneth, immanent critique, real utopias, social freedom, socialism

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Authors: Ł. Morawiński, C. Jasiński, M. Jurkiewicz, S. Bou Habib, M. Bondyra

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With the development of optics, electronics, and computers, vision systems are increasingly used in various areas of life, science, and industry. Vision systems have a huge number of applications. They can be used in quality control, object detection, data reading, e.g., QR-code, etc. A large part of them is used for measurement purposes. Some of them make it possible to obtain a 3D reconstruction of the tested objects or measurement areas. 3D reconstruction algorithms are mostly based on creating depth maps from data that can be acquired from active or passive methods. Due to the specific appliance in airfield technology, only passive methods are applicable because of other existing systems working on the site, which can be blinded on most spectral levels. Furthermore, reconstruction is required to work long distances ranging from hundreds of meters to tens of kilometers with low loss of accuracy even with harsh conditions such as fog, rain, or snow. In response to those requirements, HRESS (Heliport REmote Safeguard System) was developed; which main part is a rotational head with a two-camera stereovision rig gathering images around the head in 360 degrees along with stereovision 3D reconstruction and point cloud combination. The sub-pixel analysis introduced in the HRESS system makes it possible to obtain an increased distance measurement resolution and accuracy of about 3% for distances over one kilometer. Ultimately, this leads to more accurate and reliable measurement data in the form of a point cloud. Moreover, the program algorithm introduces operations enabling the filtering of erroneously collected data in the point cloud. All activities from the programming, mechanical and optical side are aimed at obtaining the most accurate 3D reconstruction of the environment in the measurement area.

Keywords: airfield monitoring, artificial intelligence, stereovision, 3D reconstruction

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Authors: Mohanad Moustafa, Julia Sieberer, Rhys Davies

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Background: Orbital compartment syndrome (OCS) is a sight-threatening Ophthalmologic emergency caused by rapidly increasing intraorbital pressure. It is usually caused by a retrobulbar hemorrhage as a result of trauma. If not treated in a timely manner, permanent vision loss can occur. Lateral canthotomy and cantholysis are minor procedures that can be performed bedside with equipment available in the emergency department. The aim of the procedure is to release the attachments between the suspensory ligaments of the eye and the bony orbital wall, leading to a decrease in intraorbital pressure and preventing irreversible loss of vision. As most Ophthalmologists across the UK provide non-resident on-call service, this may lead to a delay in the treatment of OCS and stresses the need for Emergency medical staff to be able to provide this sight-saving procedure independently. Aim: To survey current training, experience, and confidence levels among Emergency Medicine doctors in performing emergency lateral canthotomy and to establish whether these variables change the following teaching from experienced ophthalmologists. RESULTS: Most EM registrars had little to no experience in performing lateral canthotomy and cantholysis. The majority of them showed a significant increase in their confidence to perform the procedure following ophthalmic-led teaching. The survey also showed that the registrars felt such training should be added to/part of the EM curriculum. Conclusion: The involvement of Ophthalmologists in the teaching of EM doctors to recognise and treat OCS independently may prevent delays in treatment and reduce the risk of permanent sight loss. This project showed potential in improving patient care and will lead to a National Survey of EM doctors across the UK.

Keywords: lateral canthotomy, retrobulbar hemorrhage, Ophthalmology, orbital compartment syndrome, sight loss, blindness

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Authors: Raghul Vignesh Kumar, M. Vadivel

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With the recent years many industries and companies have turned their attention in realizing how going 'green' can benefit public relations, lower cost, and reduce global emissions from industrial manufacturing. Green Computing has become an originative way on how technology and ecology converge together. It is a growing import subject that creates an urgent need to train next generation computer scientists or practitioners to think ‘green’. However, green computing has not yet been well taught in computer science or computer engineering courses as a subject. In this modern IT world it’s impossible for an organization or common man to work without hardware like servers, desktop, IT devices, smartphones etc. But it is also important to consider the harmful impact of those devices and steps to achieve energy saving and environmental protection. This paper presents the magnitude of green computing and steps to be followed to go green.

Keywords: green computing, carbon-dioxide, greenhouse gas, energy saving, environmental protection agency

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Authors: Ramuel Spirituel Mattathiah A. San Juan, Neil Ambasing

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Background: Reversal of vision metamorphopsia is a transient form of metamorphopsia described as an upside-down alteration of the visual field in the coronal plane. Patients would describe objects, such as cups, upside down, but the tea would not spill, and people would walk on their heads. It is extremely rare as a stable finding, lasting days or weeks. We report a case wherein this type of metamorphopsia occurred only in written words and lasted for six months. Objective: To the best of our knowledge, we report the first rare occurrence of reversal of vision metamorphopsia described as inverted words as the sole initial presentation of an underlying stroke. Case Presentation: We report a 59-year-old male with poorly controlled hypertension and diabetes mellitus who presented with a 3-day history of difficulty reading, described as the words were turned upside down as if the words were inverted horizontally then with the progression of deficits such as right homonymous hemianopia and achromatopsia, prosopagnosia. Cranial magnetic resonance imaging (MRI) revealed an acute infarct on the left posterior cerebral artery territory. Follow-up after six months revealed improvement of the visual field cut but with the persistence of the higher cortical function deficits. Conclusion: We report the first rare occurrence of metamorphopsia described as purely inverted words as the sole initial presentation of an underlying stroke. The differential diagnoses of a patient presenting with text reversal metamorphopsia should include stroke in the occipitotemporal areas. It further expands the landscape of metamorphopsias due to its exclusivity to written words and prolonged duration. Knowing these clinical features will help identify the lesion locus and improve subsequent stroke care, especially in time-bound management like intravenous thrombolysis.

Keywords: rare presentation, text reversal metamorphopsia, ischemic stroke, stroke

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Authors: Paul B Stone, Subhashini Ganapathy, Mary E. Fendley, Layla Akilan

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As notifications become more common through mobile devices, it is important to understand the impact of wearable devices on the improved user experience of man-machine interfaces. This study examined the use of a wearable device for a real-time system using a computer-simulated petrochemical system. The key research question was to determine how using the information provided by the wearable device can improve human performance through measures of situational awareness and decision making. Results indicate that there was a reduction in response time when using the watch, and there was no difference in situational awareness. Perception of using the watch was positive, with 83% of users finding value in using the watch and receiving haptic feedback.

Keywords: computer applications, haptic feedback, petrochemical systems, situational awareness, wearable technology

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Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Ramón Aparicio-García, Gustavo Juárez Gracia, Jesús Álvarez Cedillo

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A research line of the computer science that involve the study of the Human-Computer Interaction (HCI), which search to recognize and interpret the user intent by the storage and the subsequent analysis of the electrical signals of the brain, for using them in the control of electronic devices. On the other hand, the affective computing research applies the human emotions in the HCI process helping to reduce the user frustration. This paper shows the results obtained during the hardware and software development of a Brain Computer Interface (BCI) capable of recognizing the human emotions through the association of the brain electrical activity patterns. The hardware involves the sensing stage and analogical-digital conversion. The interface software involves algorithms for pre-processing of the signal in time and frequency analysis and the classification of patterns associated with the electrical brain activity. The methods used for the analysis and classification of the signal have been tested separately, by using a database that is accessible to the public, besides to a comparison among classifiers in order to know the best performing.

Keywords: affective computing, interface, brain, intelligent interaction

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Authors: Chimuanya Osuji, Kido Uyamasi, Morgan Bradley

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Introduction: Obesity is a major risk factor for many chronic diseases, with higher rates occurring among certain populations. Even though disparities in obesity rates exist for those with disabilities, few studies have assessed the association between disability and obesity status. This study aims to examine the association between type of disability and obesity status among US adults during the Covid-19 pandemic (2019-2021). Methods: Data for this cross-sectional study was obtained from the 2019, 2020 and 2021 NHIS. Multinomial logistic regressions were used to assess the relationship between each type of disability and obesity status (reference= normal/underweight). Each model adjusted for demographic, health status and health-related quality of life variables. Statistical analyses were conducted using SAS version 9.4. Results: Of the 82,632 US adults who completed the NHIS in 2019, 2020, and 2021. 8.9% (n= 7,354) reported at least 1 disability-related condition. Respondents reported having a disability across vision (1.5%), hearing (1.5%), mobility (5.3%), communication (0.8%), cognition (2.4%) and self-care (1.1%) domains. After adjusting for covariates, adults with at least 1 disability-related condition were about 30% more likely to have moderate-severe obesity (AOR=1.3; 95% CI=1.11, 1.53). Mobility was the only disability category positively associated with mild obesity (AOR=1.16; 95% CI=1.01, 1.35) and moderate/severe obesity (AOR=1.6; 95% CI=1.35, 1.89). Individuals with vision disability were about 35% less likely to have mild obesity (AOR=0.66; 95% CI=0.51, 0.86) and moderate-severe obesity (AOR=0.66; 95% CI= 0.48, 0.9). Individuals with hearing disability were 28% less likely to have mild obesity (AOR=0.72; 95% CI= 0.56, 0.94). Individuals with communication disability were about 30% less likely to be overweight (AOR=0.66; 95% CI=0.47, 0.93) and 50% less likely to have mild obesity (AOR=0.45; 95% CI= 0.29, 0.71). Individuals with cognitive disability were about 25% less likely to have mild obesity and about 35% less likely to have moderate-severe obesity. Individuals with self-care disability were about 30% less likely to be overweight. Conclusion: Mobility-related disabilities are significantly associated with obesity status among adults residing in the United States. Researchers and policy makers should implement obesity intervention methods that can address the gap in obesity prevalence rates among those with and without disabilities.

Keywords: cognition, disability, mobility, obesity

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

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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: David de Oliveira Costa, Miguel Ângelo Lellis Moreira, Carlos Francisco Simões Gomes, Daniel Augusto de Moura Pereira, Marcos dos Santos

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Strategic management is a fundamental process for global companies that intend to remain competitive in an increasingly dynamic and complex market. To do so, it is necessary to maintain alignment with their principles and values. The Balanced Scorecard (BSC) proposes to ensure that the overall business performance is based on different perspectives (financial, customer, internal processes, and learning and growth). However, relying solely on the BSC may not be enough to ensure the success of strategic management. It is essential that companies also evaluate and prioritize strategic projects that need to be implemented to ensure they are aligned with the business vision and contribute to achieving established goals and objectives. In this context, the proposition involves the incorporation of the SAPEVO-M multicriteria method to indicate the degree of relevance between different perspectives. Thus, the strategic objectives linked to these perspectives have greater weight in the classification of structural projects. Additionally, it is proposed to apply the concept of the Impact & Probability Matrix (I&PM) to structure and ensure that strategic projects are evaluated according to their relevance and impact on the business. By structuring the business's strategic management in this way, alignment and prioritization of projects and actions related to strategic planning are ensured. This ensures that resources are directed towards the most relevant and impactful initiatives. Therefore, the objective of this article is to present the proposal for integrating the BSC methodology, the SAPEVO-M multicriteria method, and the prioritization matrix to establish a concrete weighting of strategic planning and obtain coherence in defining strategic projects aligned with the business vision. This ensures a robust decision-making support process.

Keywords: MCDA process, prioritization problematic, corporate strategy, multicriteria method

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization

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Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya

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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.

Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification

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Authors: Mohammad Salehi, Mohammad Erfan Doraki

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In this paper, airflow analysis inside a desktop computer case is performed by simulating computational fluid dynamics. The purpose is to investigate the cooling process of the central processing unit (CPU) with thermal capacities of 80 and 130 watts. The airflow inside the computer enclosure, selected from the microATX model, consists of the main components of heat production such as CPU, hard disk drive, CD drive, floppy drive, memory card and power supply unit; According to the amount of thermal power produced by the CPU with 80 and 130 watts of power, two different geometries have been used for a direct and radial heat sink. First, the independence of the computational mesh and the validation of the solution were performed, and after ensuring the correctness of the numerical solution, the results of the solution were analyzed. The simulation results showed that changes in CPU temperature and other components linearly increased with increasing CPU heat output. Also, the ambient air temperature has a significant effect on the maximum processor temperature.

Keywords: computational fluid dynamics, CPU cooling, computer case simulation, heat sink

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Authors: Rafik Djemili, Hocine Bourouba, M. C. Amara Korba

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In this paper, we present a method in order to classify EEG signals for Brain-Computer Interfaces (BCI). EEG signals are first processed by means of spectral estimation methods to derive reliable features before classification step. Spectral estimation methods used are standard periodogram and the periodogram calculated by the Welch method; both methods are compared with Logarithm of Band Power (logBP) features. In the method proposed, we apply Linear Discriminant Analysis (LDA) followed by Support Vector Machine (SVM). Classification accuracy reached could be as high as 85%, which proves the effectiveness of classification of EEG signals based BCI using spectral methods.

Keywords: brain-computer interface, motor imagery, electroencephalogram, linear discriminant analysis, support vector machine

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Authors: Mustafa Eruyar, Ersoy Pehlivan, Fatih Kafalı, Fatih Gundogan

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All megacities are not only under the pressure of common urbanization and growth problems but also dealing with different challenges according to their specific circumstances. However, the majority of cities focuses mainly on popular smart city projects, which are usually driven by strong private sector, regardless of their characteristics, each city needs to develop customized projects within a tailor-made smart city roadmap to be able to solve its own challenges. Smart city manifest, helps citizens to feel the action better than good reading smart city vision statements, which consists of five elements; namely purpose, values, mission, vision, and strategy. This study designs a methodology for smart city roadmap based on a top-down approach, breaking down of smart city manifest to feasible projects for a systematic smart city transformation. This methodology was implemented in Istanbul smart city transformation program which includes smart city literature review, current state analysis, roadmap, and architecture projects, respectively. Istanbul smart city roadmap project followed an extensive literature review of certain leading smart cities around the world and benchmarking of the city’s current state using well known smart city indices. In the project, needs of citizens and service providers of the city were identified via stakeholder, persona and social media analysis. The project aimed to develop smart city projects targeting fulfilling related needs by implementing a gap analysis between current state and foreseen plans. As a result, in 11 smart city domains and enablers; 24 strategic objectives, 50 programs, and 101 projects were developed with the support of 183 smart city stakeholder entities and based on 125 citizen persona profiles and last one-year social media analysis. In conclusion, the followed methodology helps cities to identify and prioritize their needs and plan for long-term sustainable development, despite limited resources.

Keywords: needs-based, manifest, roadmap, smart city, top-down approach

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Authors: Nirupma Bhatti

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This empirical study was aimed to compare the effectiveness of Computer Assisted Instruction (CAI) and Conventional Method (CM) in attaining and retaining mathematical concepts. Instructional and measuring tools were developed for five units of Matrix Algebra, two of Calculus and five of Numerical Analysis. Reliability and validity of these tools were also examined in pilot study. Ninety undergraduates participated in this study. Pre-test – post-test equivalent – groups research design was used. SPSS v.16 was used for data analysis. Findings supported CAI as better mode of instruction for attainment and retention of basic mathematical concepts. Administrators should motivate faculty members to develop Computer Assisted Instructional Material (CAIM) in mathematics for higher education.

Keywords: attainment, CAI, CAIM, conventional method, retention

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Authors: Rajender Singh, Nidhi Sharma, Aastha Chauhan, Meenakshi Barsaul, Jyoti Deswal, Chetan Chhikara

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Introduction: Pre-eclampsia and eclampsia are hypertensive disorders of pregnancy with multisystem involvement and are common causes of morbidity and mortality in obstetrics. It is believed that changes in retinal arterioles may indicate similar changes in the placenta. Therefore, this study was undertaken to evaluate the ocular manifestations in cases of pre-eclampsia and eclampsia and to deduce any association between the retinal changes and blood pressure, the severity of disease, gravidity, proteinuria, and other lab parameters so that a better approach could be devised to ensure maternal and fetal well-being. Materials and Methods: This was a hospital-based cross-sectional study conducted over a period of one year, from April 2021 to May 2022. 350 admitted patients with diagnosed pre-eclampsia, eclampsia, and pre-eclampsia superimposed on chronic hypertension were included in the study. A pre-structured proforma was used. After taking consent and ocular history, a bedside examination to record visual acuity, pupillary size, corneal curvature, field of vision, and intraocular pressure was done. Dilated fundus examination was done with a direct and indirect ophthalmoscope. Age, parity, BP, proteinuria, platelet count, liver and kidney function tests were noted down. The patients with positive findings only were followed up after 72 hours and 6 weeks of termination of pregnancy. Results: The mean age of patients was 26.18±4.33 years (range 18-39 years).157 (44.9%) were primigravida while 193(55.1%) were multigravida.53 (15.1%) patients had eclampsia, 128(36.5%) had mild pre-eclampsia,128(36.5%) had severe pre-eclampsia and 41(11.7%) had chronic hypertension with superimposed pre-eclampsia. Retinal changes were found in 208 patients (59.42%), and grade I changes were the most common. 82(23.14%) patients had grade I changes, 75 (21.4%) had grade II changes, 41(11.71%) had grade III changes, and 11(3.14%) had serous retinal detachment/grade IV changes. 36 patients had unaided visual acuity <6/9, of these 17 had refractive error and 19(5.4%) had varying degrees of retinal changes. 3(0.85%) out of 350 patients had an abnormal field of vision in both eyes. All 3 of them had eclampsia and bilateral exudative retinal detachment. At day 4, retinopathy in 10 patients resolved, and 3 patients had improvement in visual acuity. At 6 weeks, retinopathy in all the patients resolved spontaneously except persistence of grade II changes in 23 patients with chronic hypertension with superimposed pre-eclampsia, while visual acuity and field of vision returned to normal in all patients. Pupillary size, intraocular pressure, and corneal curvature were found to be within normal limits at all times of examination. There was a statistically significant positive association between retinal changes and mean arterial pressure. The study showed a positive correlation between fundus findings and severity of disease (p value<0.05) and mean arterial pressure (p value<0.005). Primigravida had more retinal changes than multigravida patients. A significant association was found between fundus changes and thrombocytopenia and deranged liver and kidney function tests (p value<0.005). Conclusion: As the severity of pre-eclampsia and eclampsia increases, the incidence of retinopathy also increases, and it affects visual acuity and visual fields of the patients. Thus, timely ocular examination should be done in all such cases to prevent complications.

Keywords: eclampsia, hypertensive, ocular, pre-eclampsia

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Authors: Jiri Barta

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At the present time, awareness, education, computer simulation and information systems protection are very serious and relevant topics. The article deals with perspectives and possibilities of implementation of emergence or natural hazard threats into the system which is developed for communication among members of crisis management staffs. The Czech Hydro-Meteorological Institute with its System of Integrated Warning Service resents the largest usable base of information. National information systems are connected to foreign systems, especially to flooding emergency systems of neighboring countries, systems of European Union and international organizations where the Czech Republic is a member. Use of outputs of particular information systems and computer simulations on a single communication interface of information system for communication among members of crisis management staff and setting the site interoperability in the net will lead to time savings in decision-making processes in solving extraordinary events and crisis situations. Faster managing of an extraordinary event or a crisis situation will bring positive effects and minimize the impact of negative effects on the environment.

Keywords: computer simulation, communication, continuity, critical infrastructure, information systems, safety

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Authors: Jutta Ransmayr

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The digital age has long arrived at Austrian schools, so both society and educationalists demand that digital means should be integrated accordingly to day-to-day school routines. Therefore, the Austrian school-leaving exam (A-levels) can now be written either by hand or by using a computer. However, the choice of writing medium (pen and paper or computer) for written examination papers, which are considered 'high-stakes' exams, raises a number of questions that have not yet been adequately investigated and answered until recently, such as: What effects do the different conditions of text production in the written German A-levels have on the component of normative linguistic accuracy? How do the spelling skills of German A-level papers written with a pen differ from those that the students wrote on the computer? And how is the teacher's assessment related to this? Which practical desiderata for German didactics can be derived from this? In a trilateral pilot project of the Austrian Center for Digital Humanities (ACDH) of the Austrian Academy of Sciences and the University of Vienna in cooperation with the Austrian Ministry of Education and the Council for German Orthography, these questions were investigated. A representative Austrian learner corpus, consisting of around 530 German A-level papers from all over Austria (pen and computer written), was set up in order to subject it to a quantitative (corpus-linguistic and statistical) and qualitative investigation with regard to the spelling and punctuation performance of the high school graduates and the differences between pen- and computer-written papers and their assessments. Relevant studies are currently available mainly from the Anglophone world. These have shown that writing on the computer increases the motivation to write, has positive effects on the length of the text, and, in some cases, also on the quality of the text. Depending on the writing situation and other technical aids, better results in terms of spelling and punctuation could also be found in the computer-written texts as compared to the handwritten ones. Studies also point towards a tendency among teachers to rate handwritten texts better than computer-written texts. In this paper, the first comparable results from the German-speaking area are to be presented. Research results have shown that, on the one hand, there are significant differences between handwritten and computer-written work with regard to performance in orthography and punctuation. On the other hand, the corpus linguistic investigation and the subsequent statistical analysis made it clear that not only the teachers' assessments of the students’ spelling performance vary enormously but also the overall assessments of the exam papers – the factor of the production medium (pen and paper or computer) also seems to play a decisive role.

Keywords: exam paper assessment, pen and paper or computer, learner corpora, linguistics

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