Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Rheumatoid Arthritis using Deep Learning Techniques

Rheumatoid Arthritis (RA), is a disorder where the immune system attacks the cells in the body which are healthy. Those parts of the body which are affected by RA get swelled up. The main area of focus for The RA is the joints, generally many joints at the same time. The joints which are most often affected by RA are the joints which are present in the wrist, knees and hands. Joint tissue gets affected in a joint containing RA because of swelling present in the lining of the joint. Tissue damage may lead to long-lasting or chronic pain, shakiness, and deformity. Over the past few years deep learning techniques has helped identifying and predicting diseases become easily. This study discusses about a more accurate way of identifying arthritis. In this study, standard CNN model is proposed to predict arthritis, ResNet CNN and AlexNet CNN. Proposed dataset contains over 654 images obtained from various different sources. To enhance the quality of images in the dataset image preprocessing and image segmentation has been performed. An accuracy of around 97.5% has been obtained from this method.
A Review of Elderly Fall Detection Systems using Artificial Intelligence

Physical falls are a significant threat for the elderly population. This research study reviews the existing AI-based fall detection systems, which can also predict falls with an increased accuracy. Even though many fall detection systems exist for elderly people, the existing systems face various challenges in terms of power consumption, real-time operation, accessibility, and accuracy in real-time situations. This research study discusses about the implementation of Artificial Intelligence (AD technologies in elderly fall detection and how far it contributes to solve the aforementioned challenges in the existing fall detection systems.
Sentiment Analysis of YouTube comments in Dravidian Code-Mixed Language using Deep Neural Network

Sentiment analysis is a method for determining the positivity, negativity, or neutrality of a text block. The purpose of sentiment analysis is to study public sentiment in a manner that promotes company growth. This study seeks to classify the feelings of a dataset of comments/posts into pre-defined classifications for the code-mixed languages Tamil, Malayalam, and Kannada. The Sequential Deep Learning model is used to the code-mixed dataset to identify sentiments. The experiment was carried out using the dataset from the Codalab 2022 competition "Shared Task on Sentiment Analysis and Homophobia Detection of YouTube Comments in Code-Mixed Dravidian Languages", which included social media comments in Tamil, Malayalam, and Kannada code-mixed languages.
IoT-based Sweat Glucose Monitoring System

This brief is focused on sweat glucose monitoring for diabetes patients. Diabetes is a continual disease or a set of metabolic ailments wherein someone suffers from an excessive level of glucose in the frame. The majority of people currently suffer from diabetes. Diabetes is a manageable illness. People with diabetes must test their blood regularly and take the appropriate insulin. Collecting blood from the body on regular basis can cause a variety of difficulties, including heart and kidney disorders, stroke, and blindness. There is currently no reliable approach for determining the likelihood of developing diabetes. As a result, the appropriate safeguards can be implemented. Sweat-based blood glucose monitoring relieves the pain of blood-based analysis. The conductivity of the skin is measured using a Sweat glucose sensor module. The glucose level is calculated using this data. This method of measuring glucose is believed to be comfortable, cost-effective, and simple to monitor.
An Early Warning Smart Healthcare Kit to Avoid Road Accidents

Smart health kits are innovative devices that can monitor various health-related parameters in real time. Inadequate healthcare facilities in rural areas, people have limited access to quality healthcare services, leading to increased morbidity and mortality rates. Lack of patient monitoring makes it challenging to identify and treat illnesses in their early stages since remote patients frequently do not receive routine checkups and monitoring. The enormous cost of healthcare is a significant burden for many individuals, particularly those living in poverty. Inefficient healthcare delivery in the traditional healthcare system is often bogged down by bureaucracy and inefficiency, leading to long wait times, incorrect diagnoses, and ineffective treatments. Real-time healthcare monitoring is necessary to deliver effective treatment. Real-time patient data, such as vital signs, medical history, and other pertinent information, must be available to healthcare practitioners. The goal of the smart healthcare kit using an embedded system is to address the health problems of persons while driving vehicles. The embedded medical sensors such as temperature, heart rate, and alcohol gas sensors collect and transmit patient data to healthcare providers in real time enabling them to make informed decisions about patient care. This kit may be mounted to the steering wheel of any vehicle, making it simple to use and accessible for anyone while driving. The temperature sensor keeps track of the driver’s body temperature and alerts the driver if it rises above a certain threshold. The heart rate sensor monitors the heart rate and notifies the user when it changes unexpectedly. Similarly, an alcohol gas sensor measures the alcohol concentration in the driver’s exhaled breath to determine whether they have consumed alcohol. Thus, the smart health kit is an effective tool that can detect serious health hazards, ensure motorists are healthy while operating vehicles, and prevent accidents caused by medical conditions or alcohol consumption; this greatly increases the safety of individuals, particularly those who drive professionally. The availability of real-time health monitoring and notifications enables people to take preventative action to maintain their health and stay safe.
Vein Detection and Cannula Insertion using Raspberry Pi

There have been cases where it is difficult to recognise veins due to a lack of skilled medical personnel in a number of nations, including underdeveloped nations.Here, a method for automatic injection with vein detection is suggested as a solution for this issue. The device is capable of detecting veins automatically and injecting cannulas into them. Finding the best vein for this process can be challenging, even for experienced doctors, which is a significant issue for the medical industry. When veins are not detected correctly, they can lead to severe complications, including blood clots, rashes, bruises, and even amputation. During the vein detection process, an altered near IR camera is used to take pictures of the veins, from which, through image processing, by applying grey scaling, CLAHE, bilateral filter the vein is identified. The position for injecting can be determined by using bounding box. The centroid of the bounding box determines the position of the vein. The determined vein is considered as the injecting point. Using the IR camera and LIDAR sensor the 3 coordinates of the point is measured and the corresponding data is given to the robotic arm where the cannula is present. After the data is received, with the help of robotic arm the cannula is precisely inserted to the point on the vein. Live video feedback is offered to guarantee the needle's exact alignment and the identified vein spot. To put up all the required components, a mechanical structure is created. The medical industry's modernization could significantly benefit from this initiative.
A Deep Learning-based System for Classification and Quality Evaluation of Seeds

Farmers and agro-industries must automate seed segregation because it is a time-consuming and labor-intensive task when performed manually. Deep learning (DL) and Machine Learning (ML) based algorithms have exhibited promising results in object recognition, classification, and pattern recognition. Despite this, no research has been conducted on the classification of seed types for crops grown with several crop kinds and varying quality criteria. The objective of this research is to develop a system based on deep learning dubbed the Mixed Cropping Seed Classifier and Quality Tester (McscQT). This system will be capable of analyzing the quality of seeds based on their shape, color, and texture. The system is trained with labelled images of healthy and damaged pearl millet and maize seeds, and it has a recall and accuracy of 98.9%. The MCSCQT is an essential piece of equipment in the food sector since it can distinguish between healthy and damaged maize seeds.
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.
A Novel Two Level Bank Security System using IoT based Controller

Due to an uptick in bank fraud, improved bank security is now a matter of personal security in today’s rapidly developing world. Lockers at banks have traditionally been safe places to keep important papers, jewellery, and other assets, but incidents like robberies have created a security risk, and banks have denied any responsibility for the loss of customer property. Even though the Reserve Bank of India (RBI) has mandated new procedures and policies to ensure the security of bank vaults, users will still need to manually operate their lockers with keys. This article proposes a two-tiered bank security system that makes use of an IoT-based controller, with the RBI’s plan and the need for locker safety as its primary considerations. Using biometrics and a one-time password, this method prioritizes security. It eliminates the need for keys and passwords. The prototype was developed, and the final system successfully combines a controller, GSM technology, and biometrics to provide two-factor identification for locking the lockers. Two-tiered security systems for bank lockers are the subject of this piece, which details their introduction and execution.
Finite element model for linear-elastic mixed mode loading using adaptive mesh strategy

An adaptive mesh finite element model has been developed to predict the crack propagation direction as well as to calculate the stress intensity factors (SIFs), under linear-elastic assumption for mixed mode loading application. The finite element mesh is generated using the advancing front method. In order to suit the requirements of the fracture analysis, the generation of the background mesh and the construction of singular elements have been added to the developed program. The adaptive remeshing process is carried out based on the posteriori stress error norm scheme to obtain an optimal mesh. Previous works of the authors have proposed techniques for adaptive mesh generation of 2D cracked models. Facilitated by the singular elements, the displacement extrapolation technique is employed to calculate the SIF. The fracture is modeled by the splitting node approach and the trajectory follows the successive linear extensions of each crack increment. The SIFs values for two different case studies were estimated and validated by direct comparisons with other researchers work.
Explainable Software Defect Prediction from Cross Company Project Metrics using Machine Learning

Predicting the number of defects in a project is critical for project test managers to allocate budget, resources, and schedule for testing, support and maintenance efforts. Software Defect Prediction models predict the number of defects in given projects after training the model with historical defect related information. The majority of defect prediction studies focused on predicting defect-prone modules from methods, and class-level static information, whereas this study predicts defects from project-level information based on a cross-company project dataset. This study utilizes software sizing metrics, effort metrics, and defect density information, and focuses on developing defect prediction models that apply various machine learning algorithms. One notable issue in existing defect prediction studies is the lack of transparency in the developed models. Consequently, the explain-ability of the developed model has been demonstrated using the state-of-the-art post-hoc model-agnostic method called Shapley Additive exPlanations (SHAP). Finally, important features for predicting defects from cross-company project information were identified.
Peer-to-Peer Energy Trading Considering Low-Carbon Preference

The acceleration of renewable energy resources prompts transition to the low-carbon networks. However, the intermittent output of RES causes severe operational problems. To cope with these problems, we develop a peer-to-peer (P2P) energy trading algorithm considering low-carbon preference. In the prosumer-centric market, prosumers are self-profit driven stakeholders who compete with each other by implementing both energy trading and demand response. A bi-level framework of self-profit driven prosumers as well as independent distribution system operator are proposed. The P2P energy trading market is formulated as an evolutionary game, characterizing competition relationships between seller prosumer and buyer prosumer. Furthermore, we propose a joint optimization algorithm of external energy trading and internal demand response. We introduce the operation and carbon network charges to incentive the autonomous prosumers to behave low-carbon and grid-friendly. Finally, we utilized the IEEE simulation system for verifying P2P trading algorithm on prompting low-carbon transition as well as increase profits of peers.
Deep Learning Approaches for HAR of Daily Living Activities Using IMU Sensors in Smart Glasses

In the latest days, study into the development of intelligent technologies has proven valuable, contributing to attempts to improve the quality of human existence. Smart glass is one of the intelligent wearable devices that can be used for various purposes, including healthcare monitoring, fall detection, sleep tracking, and human activity recognition (HAR). Smart-phones and smartwatches are the primary wearables utilized in sensor-based HAR to collect human motions for training recognition models based on physical movement. These wearable tools, nevertheless, are more intrusive than smart glasses. Using IMU sensor data acquired via smart glasses, we investigate deep learning algorithms for detecting people's activities of daily living (ADL). This work proposes a hybrid deep neural network that automatically extracts spatial-temporal information from raw data to enhance identification $\mathbf{p}$ erformance. We performed tests to evaluate deep learning models using a publically available benchmark dataset, UCA-EHAR, which included IMU sensor data from multiple ADL from smart eyewear. The recommended CNN-LSTM model achieved the best effectiveness with the highest F1-score of 93.20%, as determined by experimental findings.
A Study on Various Approaches Towards Non-Factoid Question Answering Systems

Question Answering (QA) is a branch of Natural Language Processing (NLP) which develop systems that automatically provide answer to questions queried by humans in any natural language. Depending upon the type of question, QA systems can be both factoid and non-factoid type. There are numerous methods for retrieving answers for the input queries. Due to its complex behavior, non-factoid QA still remains a challenge to many researchers. This survey mainly focuses on works done so far in the area of non-factoid QA systems. Beginning with traditional deep learning approaches and moving towards the usage of Knowledge-Graphs (KG), this paper focuses on studying how to improve the efficiency of such QA systems. Transformer models like Bidirectional Encoder Representations from Transformers (BERT) and its variants reflect a significant improvement in QA implementation. One of the main challenges faced by those models was to answer the questions from knowledge-dependable domains. The study shows that knowledge-graphs help to address these issues to some extent. Also, it provides an analytical discussion on various approaches in non-factoid QA system implementation and how KG outperform previous methods.
Study on the Relationship between Residential Cooling Load and Temperature under Extreme Weather of Summer

Industrial structure adjustment, frequent extreme weather and other reasons have profoundly changed the load characteristics of the power grid, bringing new challenges to the power supply. Among all kinds of power load, residential load, as an important part of the social electricity power consumption, has become an important reason for the power grid load repeatedly reaching new highs in summer and winter, and is also the key point of load prediction. Based on residential load data from Chengdu, this paper conducts a deep analysis on the relationship between residential load and temperature under extreme weather of summer, as well as the correlation between cooling power load, meteorological factors and economic development level in summer. The research of this paper is helpful to realize the refined forecast of power demand under extreme weather condition in Sichuan province.
IoT-based Mobile Application for Road Accident Detection and Notification

Vehicle accidents are major concern today in traffic management systems which result in an increasing number of deaths. India is currently in top position in road accidents around the globe. This leads to a huge number in future with growing population and other causes e.g. bad road conditions, reckless driving, delay in medical help, etc. To overcome these, a proper accident detection and notification system is required. Here comes our android app based on IoT sensors (Accelerometer, Gyroscope) of smartphones used in our daily lives. The app automatically detects an accident from sensory data and sends notification to emergency numbers so that victims receive immediate medical assistance. The app covers most of the solutions in this regard in post-accident scenarios, such as accident detection, sending notifications, and a few which are applicable for alerting people about accidents and handling traffic congestion scenarios.Later includes warning drivers while entering an accident-prone zone, suggesting an alternate route to avoid congestion in the accident occurrence area, etc. The performance is analyzed by delay time, false alarm rates and it outperforms the existing systems in terms of different features. Thus, it is user-friendly, reliable, and cost effective.
An Analysis of Advanced Soft-Switching Techniques for DC–AC Power Converters Based on Auxiliary Circuits

At present, there is a huge trend in the industry for power converters that have the ability to reach a high efficiency at high switching frequencies. General power converters have switching losses which act as a barrier in terms of efficiency while designing high-frequency converters. As a solution for this, soft switching techniques come into play by integrating a resonant circuit into the traditional power converter. These resonant converters give the ability of the power converters to operate in high frequencies without switching losses. Also in electric vehicles as well as wireless charging, the switching frequency can be increased to a higher level without the switching losses using these converters. Furthermore, it can be used in bi-directional converters with MOSFETs like SiC and GaN. An analytical review of the advanced resonant converter design topologies is discussed in this paper based on three main designs for auxiliary-based circuits. Finally, a comparative analysis is conducted using the various converter parameters. This analysis will be a huge benefit for the power electronics circuit design area to select the most suitable type of circuit for the application.
Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data Systems

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS.