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Evaluation of the Effectiveness of Movement Control Order to Limit the Spread of COVID-19 Md Amiruzzaman, M. Abdullah-Al-Wadud, Rizal Mohd Nor and Normaziah A. Aziz Abstract: This study presents a prediction model based on Logistic Growth Curve (LGC) to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depends on partial data from the COVID-19 cases in Malaysia. The model is studied on the effectiveness of the three phases of MCO implemented in Malaysia, where the model perfectly fits with the R2 value 0.989. Evidence from this study suggests that results of the prediction model match with the progress and effectiveness of the MCO to flatten the curve, and thus is helpful to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth. Keywords: COVID-19; Evaluation; Logistic Growth Curve; Movement Control Order; Prediction. |
Causal Reasoning Application in Smart Farming and Ethics: A Systematic Review Shkurte Luma-Osmani, Florije Ismaili, Bujar Raufi and Xhemal Zenuni Abstract: In the last decade, there has been paradigm shift on causal reasoning, the discovery of causal relationships between variables and its potential to help understand and solve different complex real-life problems. The aim of this paper is to present a systematic review of relevant studies related to causal reasoning, with emphasis on smart agriculture and ethics. The paper considers the literature review as an answer to several research questions that intend to broadly recapitulate and scrutinise the causal reasoning problem in smart agriculture as well as research ethics, viewed from diverse lookouts. Keywords: causal reasoning; smart agriculture; ethics in research; smart farming. |
Md. Abdur Rahim, Jungpil Shin and Keun Soo Yun Abstract: Sign language (SL) recognition is intended to connect deaf people with the general population via a variety of perspectives, experiences, and skills that serve as a basis for the development of human-computer interaction. Hand gesture-based SL recognition encompasses a wide range of human capabilities and perspectives. The efficiency of hand gesture performance is still challenging due to the complexity of varying levels of illumination, diversity, multiple aspects, self-identifying parts, different shapes, sizes, and complex backgrounds. In this context, we present an American Sign Language alphabet recognition system that translates sign gestures into text and creates a meaningful sentence from continuously performed gestures. We propose a segmentation technique for hand gestures and present a convolutional neural network (CNN) based on the fusion of features. The input image is captured directly from a video via a low-cost device such as a webcam and is pre-processed by a filtering and segmentation technique, for example the Otsu method. Following this, a CNN is used to extract the features, which are then fused in a fully connected layer. To classify and recognize the sign gestures, a well-known classifier such as Softmax is used. A dataset is proposed for this work that contains only static images of hand gestures, which were collected in a laboratory environment. An analysis of the results shows that our proposed system achieves better recognition accuracy than other state-of-the-art systems. Keywords: Convolutional neural network; Human-computer interaction; Hand gesture; Otsu method; Sign language. |
Neural Nets Distributed on Microcontrollers using Metaheuristic Parallel Optimization Algorithm Fazal Noor and Hatem ElBoghdadi Abstract: Metaheuristic algorithms are powerful methods for solving compute intensive problems. neural Networks, when trained well, are great at prediction and classification type of problems. Backpropagation is the most popular method utilized to obtain the weights of Neural Nets though it has some limitations of slow convergence and getting stuck in a local minimum. In order to overcome these limitations, in this paper, a hybrid method combining the parallel distributed bat algorithm with backpropagation is proposed to compute the weights of the Neural Nets. The aim is to use the hybrid method in applications of a distributed nature. Our study uses the Matlab® software and Arduino® microcontrollers as a testbed. To test the performance of the testbed, an application in the area of speech recognition is carried out. Due to the resource limitations of Arduino microcontrollers, the core speech pre-processing of LPC (linear predictive coding) feature extractions are done in Matlab® and only the LPC parameters are passed to the Neural Nets, which are implemented on Arduino microcontrollers. The experimental results show that the proposed scheme does produce promising results. Keywords: Bat algorithm; Genetic algorithm; Neural Networks; Speech recognition; Optimization algorithm. |
IoT Based Virtual Reality Game for Physio-therapeutic Patients K. Martin Sagayam, Shibin D, Hien Dang, Mohd Helmy Abd Wahab and Radzi Ambar Abstract: Biofeedback therapy trains the patient to control voluntarily the involuntary process of their body. This non-invasive and non-drug treatment is also used as a means to rehabilitate the physical impairments that may follow a stroke, a traumatic brain injury or even in neurological aspects within occupational therapy. The idea behind this study is based on using immersive gaming as a tool for physical rehabilitation that combines the idea of biofeedback and physical computing to get a patient emotionally involved in a game that requires them to do the exercises in order to interact with the game. This game is aimed towards addressing the basic treatment for ‘Frozen Shoulder’. In this work, the physical motions are captured by the wearable ultrasonic sensor attached temporarily to the various limbs of the patient. The data received from the sensors are then sent to the game via serial wireless communication. There are two main aspects to this study: motion capturing and game design. The current status of the application is a single ultrasonic detector. The experimental result shows that physio-therapeutic patients are benefited through the IoT based virtual reality game. Keywords: Virtual reality (VR); Internet of Things (IoT); physiotherapy; rehabilitation; Arduino; cloud computing. |
Integration of Blockchain and IoT: An Enhanced Security Perspective Mahdi H. Miraz and Maaruf Ali Abstract: Blockchain (BC), a by-product of Bitcoin cryptocurrency, has gained immense and wide scale popularity for its applicability in various diverse domains – especially in multifaceted non-monetary systems. By adopting cryptographic techniques such as hashing and asymmetric encryption - along with distributed consensus approach, a Blockchain based distributed ledger not only becomes highly secure but also immutable and thus eliminates the need for any third-party intermediators. On the contrary, innumerable IoT (Internet of Things) devices are increasingly being added to the network. This phenomenon poses higher risk in terms of security and privacy. It is thus extremely important to address the security aspects of the growing IoT ecosystem. This paper explores the applicability of BC for ensuring enhanced security and privacy in the IoT ecosystem. Recent research articles and projects/applications were surveyed to assess the implementation of BC for IoT Security and identify associated challenges and propose solutions for BC enabled enhanced security for the IoT ecosystem. Keywords: Blockchain, Blockchain of Things (BCoT), Distributed Ledger Technology (DLT), Internet of Things (IoT), Proof-of-Work (PoW), Security. |
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