Soly Mathew Biju

The telecommunications industry is one of the top industries affected by fraudulent activities. Given the financial impact, on top of confidentiality breaches, security concerns, and reduced service quality as well as consumer dissatisfaction, there is an immediate need to implement effective fraud detection approaches. While there have been different fraud detection systems implemented, technological advancements as well as the improved techniques of fraudsters have made the traditional approaches no longer efficient. This paper aims to further investigate the use of artificial intelligence (AI) and machine learning (ML) to create efficient and advanced fraud detection models based on the strategy used, models applied, accuracy of the system, as well as future research work suggested. A systematic review of 50 papers was conducted. The most prevalent strategy was supervised one, majority of papers used software instead of hardware, and the most common ML models were artificial neural network (ANN), support vector machines (SVM), and decision tree.

This chapter will provide a comprehensive overview of multi agent systems, their architecture and of how Langflow can be employed to create, orchestrate, and manage a network of agents that communicate and collaborate using LLMs. We will demonstrate the power of this approach in scenarios such as automated decision-making, knowledge management, and intelligent automation. Moreover, we will examine how Langflow's flexible architecture allows for a dynamic combination of agent types, ranging from simple rule-based agents to advanced models powered by LLMs.

Parkinson’s disease (PD) is a central sensory system-based progressive illness with no cure. The origin of this illness is unknown. According to various research, it has been found that it is caused due to genetics or environmental factors. It is usually found in older people. However, there is no accurate treatment for this disease. So, the patient must be monitored periodically. It usually starts with deterioration in speech performance. The major problem with this disease is that it’s very costly to treat. The paper aims to report details of numerous aspects of detection of PD published in recent years based on the focus and benefits of the study, the methodology being used, accuracy of the system, and future research suggested for the study. A systematic study was done based on a search of the literature. A total of 50 articles were discovered.

Loss of the capability to talk or hear has psychological and social effects onthe affected individuals due to the absence of appropriate interaction. SignLanguage is used by such individuals to assist them in communicating witheach other. This paper proposes a glove called GloSign that can convertAmerican sign language to characters. This glove consists of flex and inertialmeasurement unit (IMU) sensors to identify gestures. The data from glove isuploaded on IoT platform, which makes the glove portable and wireless. Thedata from gloves is passed through a k-nearest neighbors (KNN) Algorithmmachine learning algorithm to improve the accuracy of the system. Thesystem was able to achieve an accuracy of 96.8%. The glove can also be usedto form sentences. The output is displayed on the screen or is converted tospeech. This glove can be used in communicating with people who don’t knowsign language.

Global livelihoods are impacted by the novel coronavirus (COVID-19) disease, which mostly affects the respiratory system and spreads via airborne transmission. The disease has spread to almost every nation and is still widespread worldwide. Early and reliable diagnosis is essential to prevent the development of this highly risky disease. The computer-aided diagnostic model facilitates medical practitioners in obtaining a quick and accurate diagnosis. To address these limitations, this study develops an optimized Xception convolutional neural network, called "XCovNet," for recognizing COVID-19 from point-of-care ultrasound (POCUS) images. This model employs a stack of modules, each of which has a slew of feature extractors that enable it to learn richer representations with fewer parameters. The model identifies the presence of COVID-19 by classifying POCUS images containing Coronavirus samples, viral pneumonia samples, and healthy ultrasound images. We compare and evaluate the proposed network with state-of-the-art (SOTA) deep learning models such as VGG, DenseNet, Inception-V3, ResNet, and Xception Networks. By using the XCovNet model, the previous study's problems are cautiously addressed and overhauled by achieving 99.76% accuracy, 99.89% specificity, 99.87% sensitivity, and 99.75% F1-score. To understand the underlying behavior of the proposed network, different tests are performed on different shuffle patterns. Thus, the proposed "XCovNet" can, in regions where test kits are limited, be used to help radiologists detect COVID-19 patients through ultrasound images in the current COVID-19 situation.

Loss of the capability to talk or hear applies psychological and social effects on the affected individuals due to the absence of appropriate interaction. Sign Language is used by such individuals to assist them in communicating with each other. The paper aims to report details of various aspects of wearable healthcare technologies designed in recent years based on the aim of the study, the types of technologies being used, accuracy of the system designed, data collection and storage methods, technology used to accomplish the task, limitations and future research suggested for the study. The aim of the study is to compare the differences between the papers. There is also comparison of technology used to determine which wearable device is better, which is also done with the help of accuracy. The limitations and future research help in determining how the wearable devices can be improved. A systematic review was performed based on a search of the literature. A total of 23 articles were retrieved. The articles are study and design of various wearable devices, mainly the glove-based device, to help you learn the sign language.

This paper discusses the evaluation of the sensors used in the hand grip strength glove. The glove comprises of flex and force resisting sensors. Force resisting sensor determines the force applied by various parts of the palm, while the flex sensor determines the flexion of the fingers. These sensors are placed in a specific position on the glove to obtain correct data when the glove is used. The glove has two modes, which are pencil grip mode and object grip mode. The sensors determine which mode the glove is in depending on the gesture made. The glove is examined using a pencil and a cylindrical object to evaluate the strength of the grip. After gripping the object or pencil, the system evaluates the force applied using the sensors. This data is transferred to a computer for further analysis using a trained model. The model was able to achieve an accuracy of 90.8%.