Classification of Humans into Ayurvedic Prakruti Types using Computer Vision

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine. This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda

MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters

At present, recognition of the Bangla handwriting compound character has been an essential issue for many years. In recent years there have been application-based researches in machine learning, and deep learning, which is gained interest, and most notably is handwriting recognition because it has a tremendous application such as Bangla OCR. MatrriVasha, the project which can recognize Bangla, handwritten several compound characters. Currently, compound character recognition is an important topic due to its variant application, and helps to create old forms, and information digitization with reliability. But unfortunately, there is a lack of a comprehensive dataset that can categorize all types of Bangla compound characters. MatrriVasha is an attempt to align compound character, and it's challenging because each person has a unique style of writing shapes. After all, MatrriVasha has proposed a dataset that intends to recognize Bangla 120(one hundred twenty) compound characters that consist of 2552(two thousand five hundred fifty-two) isolated handwritten characters written unique writers which were collected from within Bangladesh. This dataset faced problems in terms of the district, age, and gender-based written related research because the samples were collected that includes a verity of the district, age group, and the equal number of males, and females. As of now, our proposed dataset is so far the most extensive dataset for Bangla compound characters. It is intended to frame the acknowledgment technique for handwritten Bangla compound character. In the future, this dataset will be made publicly available to help to widen the research.Comment: 19 fig, 2 tabl

Remote Gait type classification system using markerless 2D video

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating 5 types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating 5 types of gait, at 2 severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.info:eu-repo/semantics/publishedVersio

AI that Matters:A Feminist Approach to the Study of Intelligent Machines

n this chapter, Drage and Frabetti argue that computer code uses data to not only make statements about the world but also bring that world into existence. Drawing on the concept of performativity, arguably gender studies’ best-known export, they explain why we must view artificial intelligence (AI) as performative to understand how it genders and racialises populations even when it appears to be ‘unbiased’ or correctly functioning. In its reading of neural networks, ‘AI that Matters: A Feminist Approach to the Study of Intelligent Machines’ demonstrates that Facial Detection and Recognition Technologies and Automatic Gender Recognition never objectively identify or recognise a person (or their gender), as they claim to do. Instead, drawing on work by Judith Butler and Karen Barad, they merely comment on and annotate a person’s body in accordance with dominant social rules and perspectives. They present this framing as an intervention into misguided attempts to treat discrimination as an error that can be corrected by a better functioning machine

Klasifikasi Sentimen Ulasan Film Indonesia dengan Konversi Speech-to-Text (STT) Menggunakan Metode Convolutional Neural Network (CNN)

Ulasan film adalah sebuah opini yang bersifat subjektif. Ulasan film memiliki media yang bera-gam, seperti tulisan, audio, dan video. Ulasan film dapat diolah dengan menggunakan klasifikasi sentimen, agar u-capan seseorang terkait film dapat ditentukan sebagai sen-timen tertentu. Di masa sekarang, data memiliki berbagai bentuk, pemilihan jenis data yang lebih baik juga dapat mempengaruhi klasifikasi sentimen. Data video dapat di-konversi menjadi data teks dengan bantuan Speech-to-Text (STT). Data teks digunakan karena kata atau kalimat dapat dibedakan secara negatif atau positif. Data ulasan dikelom-pokkan berdasarkan aspek penilaian film dan klasifikasi sentimen dilakukan pada keseluruhan potongan ulasan serta di tiap aspek yang ada. Dengan menggunakan metode Convolutional Neural Network, didapatkan bahwa model klasifikasi sentimen tiap aspek memiliki nilai AUC lebih baik dibandingkan model klasifikasi sentimen dengan keseluruhan data

Impact evaluation of skin color, gender, and hair on the performance of eigenface, ICA, and, CNN methods

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAlthough face recognition has made remarkable progress in the past decades, it is still a challenging area. In addition to traditional flaws (such as illumination, pose, occlusion in part of face image), the low performance of the system with dark skin images and female faces raises questions that challenge transparency and accountability of the system. Recent work has suggested that available datasets are causing this issue, but little work has been done with other face recognition methods. Also, little work has been done on facial features such as hair as a key face feature in the face recognition system. To address the gaps this thesis examines the performance of three face recognition methods (eigenface, Independent Component Analysis (ICA) and Convolution Neuron Network (CNN)) with respect to skin color changes in two different face mode “only face” and “face with hair”. The following work is reported in this study, 1st rebuild approximate PPB dataset based on work done by “Joy Adowaa Buolamwini” in her thesis entitled “Gender shades”. 2nd new classifier tools developed, and the approximate PPB dataset classified based on new methods in 12 classes. 3rd the three methods assessed with approximate PPB dataset in two face mode. The evaluation of the three methods revealed an interesting result. In this work, the eigenface method performs better than ICA and CNN. Moreover, the result shows a strong positive correlation between the numbers of train sets and results that it can prove the previous finding about lack of image with dark skin. More interestingly, despite the claims, the models showed a proactive behavior in female’s face identification. Despite the female group shape 21% of the population in the top two skin type groups, the result shows 44% of the top 3 recall for female groups. Also, it confirms that adding hair to images in average boosts the results by up to 9%. The work concludes with a discussion of the results and recommends the impact of classes on each other for future stud