Sarina Mansor

• Multimedia University

Ambient Assisted Living (AAL) leverages technology to support the elderly and individuals with disabilities. A key challenge in these systems is efficient Human Activity Recognition (HAR). However, no study has systematically compared single-view (SV) and multi-view (MV) Human Activity Recognition approaches. This review addresses this gap by analy...

Breast cancer represents a significant global health challenge, which makes it essential to detect breast cancer early and accurately to improve patient prognosis and reduce mortality rates. However, traditional diagnostic processes relying on manual analysis of medical images are inherently complex and subject to variability between observers, hig...

Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. Accurate flood detection plays a...

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (...

Bangladesh's economic development is largely dependent on the power sector, which promotes sustainability and growth. The country's future energy security, however, is seriously threatened by the natural gas reserves running out by 2028. As a result, the current energy mix has to be modified right away to ensure Bangladesh's sustained economic grow...

Background: In the realm of system biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time-series gene expression dataset. However, most of these techniques pose scalability conc...

Human Activity Recognition (HAR) is crucial for the development of intelligent assistive technologies in Ambient Assisted Living (AAL) environments. This paper proposes an innovative method for Multi-View Human Activity Recognition (MV-HAR) using lightweight deep learning models, specifically MobileNet and Cyclone-CNN (CCNet), to achieve quick and...

In the realm of System Biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time series gene expression dataset. However, most of these techniques pose scalability concerns given t...

In Bangladesh's distant regions, where dependable access to energy supplies is still an issue, effective energy consumption forecasting is essential for tackling the country's energy problems. In order to anticipate energy consumption in these neglected areas effectively, this study suggests a novel method that combines inverse matrix method (IMM)...

In this paper, we propose a framework for multiuser mmWave DCT-Spread CP-less OFDM communication system and analyze it comprehensively. Due to excessive cyclic prefix (CP) usage in conventional multicarrier systems, spectral efficiency reduces and increases transmission latency. Our proposed system enhances spectral efficiency and reduces transmiss...

The context of recognizing handwritten city names, this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script. In today’s technology-driven era, where precise tools for reading handwritten text are essential, this study focuses on leveraging deep learning to understand the intricacies of Ba...

Agriculture plays a vital role in Bangladesh’s economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification...

In the most agriculturally dependent countries both novice and expert observers are appointed to monitor vast agricultural estates. It is vital to have a technique for automatically identifying leaf diseases in order to monitor swiftly and effectively. It is evident that a great deal of research has been done on plant disease using support vector m...

Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In respons...

With an increasing number of people on the planet today, innovative human-computer interaction technologies and approaches may be employed to assist individuals in leading more fulfilling lives. Gesture-based technology has the potential to improve the safety and well-being of impaired people, as well as the general population. Recognizing gestures...

Global food security is seriously threatened by wheat leaf disease, which makes effective and precise disease detection and classification techniques necessary. For efficient disease control and the best possible crop health, timely identification and precise classification are essential. However, the limited availability of datasets for wheat leaf...

Eye diseases, a significant global health concern, require timely detection to prevent vision loss. The alarming prevalence of eye diseases necessitates immediate action through early diagnosis, making it urgent to develop an automatic detection system. Many researchers have been working to develop such systems. Yet, existing solutions still face d...

Model Predictive Control (MPC) is a widely used control strategy for multivariable systems due to its ability to handle complex dynamics and constraints. The design of perturbation signals that can improve MPC's performance. This research investigates the selection of optimal perturbation signals for multivariable systems under MPC to enhance contr...

System identification is a fundamental process in engineering and science that involves modeling and understanding the behavior of complex systems. This paper provides a comprehensive synopsis of system identification techniques, with a focus on parametric and non-parametric approaches, along with the role of perturbation signals in enhancing the a...

This paper presents a thorough review and analysis of solar photovoltaic (PV) home systems in Malaysia, offering a comprehensive exploration of their implementation, challenges, benefits, and future potential. As a nation striving to embrace sustainable and renewable energy solutions, Malaysia’s adoption of solar PV systems at the residential level...

Simulators—games that simulate the real world in a virtual environment, such as racing simulators, have been widely studied and documented. Their uses could, however, be further expanded into the field of driving education. The motivations behind this study are to dive into the trend of immersive learning and exploit artificial intelligence (AI)-ba...

Finite state machine (FSM) is a model of computation that executes an exact finite number of states at any given time where Hierarchical Finite State Machines (HFSM) can group multiple FSMs and execute as one state. Given these techniques can be used to exclusively execute certain states, it is widely used in games. In this paper we will explore an...

Driving simulator has been widely used as one of driver training tools because it provides a safe environment which does not expose drivers to hazards. However, Malaysia has yet to adopt the driving simulator in the driving course. In this paper, a cost effective and modular driving simulator prototype integrated is designed and developed based on...

The demand for nudity and pornographic content detection is increasing due to the prevalence of media products containing sexually explicit content with Internet being the main source. Recent literature has proved the effectiveness of deep learning techniques for adult image and video detection. However, the requirement for a huge dataset with labe...

Simulators are games that give a sense of realism and simulate of the real world in a virtual world, such as racing games or serious medical training games. These have been widely documented to have positive effects on the specific technical skills training on their respective field of users. However, in the area of simulators for driving education...

Audible content has become an effective tool for shaping one’s personality and character due to the ease of accessibility to a huge audible content that could be an independent audio files or an audio of online videos, movies, and television programs. There is a huge necessity to filter inappropriate audible content of the easily accessible videos...

Traffic sign recognition is a critical aspect of intelligent transportation systems that enhances road safety, traffic management, and driver assistance. Notably, while significant research efforts have been devoted to many prevalent traffic signs, Bangladeshi sign recognition remains relatively underexplored due to a lack of traffic sign knowledge...

Pornographic and nudity content detection in videos is gaining importance as Internet grows to become a source for exposure to such content. Recent literature involved pornography recognition using deep learning techniques such as convolutional neural network, object detection models and recurrent neural networks, as well as combinations of these m...

Foul language exists in films, video-sharing platforms, and social media platforms, which increase the risk of a viewer to be exposed to large number of profane words that have negative personal and social impact. This work proposes a CNN-based spoken Malay foul words recognition to establish the base of spoken foul terms detection for monitoring a...

Inappropriate visual content on the internet has spread everywhere, and thus children are exposed unintentionally to sexually explicit visual content. Animated cartoon movies sometimes have sensitive content such as pornography and sex. Usually, video sharing platforms take children’s e-safety into consideration through manual censorship, which is...

Given the excessive foul language identified in audio and video files and the detrimental consequences to an individual’s character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship were implemented, the methods proved tedious. I...

Video pornography and nudity detection aim to detect and classify people in videos into nude or normal for censorship purposes. Recent literature has demonstrated pornography detection utilising the convolutional neural network (CNN) to extract features directly from the whole frames and support vector machine (SVM) to classify the extracted featur...

Content filtering is gaining popularity due to easy exposure of explicit visual contents to the public. Excessive exposure of inappropriate visual contents can cause devastating effects such as the growth of improper mindset and rise of societal issues such as free sex, child abandonment and rape cases. At present, most of the broadcasting media si...

Recent discovered technologies have exposed many new theories and possibilities to improve our standard of living. Medical assistance has been a major research topic in the past, many efforts were put in to simplify the process of following treatment prescriptions. This paper summarizes the work done in developing LoRa driven medical adherence syst...

Excessive content of profanity in audio and video files has proven to shape one’s character and behavior. Currently, conventional methods of manual detection and censorship are being used. Manual censorship method is time consuming and prone to misdetection of foul language. This paper proposed an intelligent model for foul language censorship thro...

The main objective of this paper is pornography recognition using audio features. Unlike most of the previous attempts, which have concentrated on the visual content of pornography images or videos, we propose to take advantage of sounds. Using sounds is particularly important in cases in which the visual features are not adequately informative of...

This research proposes a pornography recognition model using audio features utilizing recurrent neural network. The proposed work is totally different from most of the previous attempts on pornography recognition using visual contents of the nudity images or videos. The importance of using sounds for pornography recognition arises in the cases in w...

Adult contents have become available everywhere whether in social networks, TV channels and websites. Children protection from pornographic contents is required in all societies and environments. Inappropriate visual contents have an impact on children’s psychological development. Parents’ censorship is important to solve the problem but this task...

Systematic procedures for data storage and retrieval are obligatory to the fluoroscopy and other conventional diagnostic imaging devices in which they produce a large number of medical images. This paper proposes and efficient method for lossless compression of fluoroscopic images. There are two components in this paper; segmentation and compressio...

The massive number of medical images produced by fluoroscopic and other conventional diagnostic imaging devices demand a considerable amount of space for data storage. This paper proposes an effective method for lossless compression of fluoroscopic images. The main contribution in this paper is the extraction of the regions of interest (ROI) in flu...

Diagnostic imaging devices such as fluoroscopy produce a vast number of sequential images, ranging from localization images to functional tracking of the contrast agent moving through anatomical structures such as the pharynx and esophagus. In this paper, an effective method for lossless and diagnostically lossless compression of fluoroscopic image...

Enormous amounts of sequential medical images are produced in modern medical examinations, typically in Fluoroscopy. Although highly effective, such large quantities of images incur a high cost in terms of storage, processing time and transmission. This paper proposes a method for lossless compression of targeted parts within Fluoroscopy images, ex...

Content Based Image Retrieval (CBIR) has been an active and fast growing research area in both image processing and data mining. Malaysia has been recognized with a rich marine ecosystem. Challenges of these images are low resolution, translation, and transformation invariant. In this paper, we have designed an automated CBIR system to characterize...

Hospitals and medical centers produce a tremendous amount of sequential images for medical examinations such as MRI, CT and Fluoroscopy. This series of images takes up a large amount of storage space, in addition to the cost and time incurred during transmission. For medical data, lossless compression is preferable to the greater gains of lossy com...

Content based image retrieval (CBIR) has been an active and fast growing research area in both image processing and data mining. CBIR Dynamic nature overcomes/complements the drawbacks of Text Based Image Retrieval (TBIR). In CBIR, extraction of feature and feature matching plays important role in performance of retrieval system. Marine species of...

Medical centers produce a massive amount of sequential medical images for examinations such as CT, MRI and Fluoroscopy, where each examination of a patient consists of a series of images. This takes up a large amount of storage space, in addition to the cost and time incurred during transmission. For medical data, lossless compression is preferred...

Medical institutions generate an enormous amount of medical images for examinations such as fluoroscopy, where each examination of a patient consists of a collection of images. This takes up a large amount of valuable storage space, in addition to the amount of time and cost incurred during transmission. Although lossy compression provides for bett...

Image de-noising is a core operation in image processing and computer vision. In this paper, combination of two popular methods in image de-noising bilateral and anisotropic-diffusion filtering is investigated to reduce the noise in medical images, while preserving the clarity of images. The proposed method experimented on 23 MRI images. The result...

Research on content based image retrieval (CBIR) has received a considerable attention as it offers solutions to overcome and complement the drawbacks of text based image retrieval (TBIR). One of the crucial studies in this system is the feature extraction process where the low level features, i.e. shape, color and texture are the common features u...

Malaysia has been recognized as one of the twelve nations endowed with rich biodiversity. Such huge number of species in the rain forest and sea are an important asset that need to be properly documented. Responding to these important needs, we have designed and evaluated a content based image retrieval system catered for marine life images. This p...

Research on Content Based Image Retrieval (CBIR) has become popular as it offers solutions to overcome or complement the drawbacks of Text Based Image Retrieval (TBIR). In CBIR, feature extraction and feature matching are two critical processes, which are of high importance to the retrieval performance of the system. This paper introduces a new app...

In this paper, we present a new approach to regional heart functional analysis employing a Hidden Markov Model (HMM) approach for cardiac disease classification. We examine the use of an HMM for local wall motion classification based on stress echocardiography. A wall segment model is developed for a normal and an abnormal heart and the experiments...

In this paper, we represent a new framework that performs automated local wall motion analysis based on the combined information derived from a rest and stress sequence (a full stress echocardiography study). Since cardiac data inherits time-varying and sequential properties, we introduce a Hidden Markov Model (HMM) approach to classify stress echo...