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- PublicationIntegrative Taxonomy Of The Limnonectes Hascheanus-Limborgi (Anura: Dicroglossidae) Complex: Resolving Cryptic Species In Peninsular Malaysia(2025-05)Limnonectes hascheanus and l. Limborgi are two closely related species that are herein referred to as the limnonectes hascheanus–limborgi complex. In the past, l. Hascheanus was the only species reported to be present in peninsular malaysia. Matsui reported one of their examined specimens from janda baik as l. Limborgi. This study aimed to ascertain the status of both species in peninsular malaysia through an integrated approach, combining morphological, bioacoustic and molecular data. The results indicated that in peninsular malaysia there two different lineages of the l. Hascheanus–limborgi complex that can be separated genetically, by their male advertisement calls, as well as by external morphology. Both lineages differ by having a high genetic distance between them of 2.4–7.8% based on both mtdna 16s rrna and nudna tyrosinase genes. One lineage has a morphotype assignable to l. Hascheanus described from penang island and bears the following characteristics: slender body and light or dark–olive–brown color; toe webbing not reaching middle subarticular tubercle on the fourth toe; and the absence of enlarged odontoids in males. Populations from langkawi island; kedah, perlis, and some specimens from bukit larut, perak are genetically and bioacoustically similar to topotypic specimens of l. Hascheanus from penang island.
- PublicationCollaborative-Based Approach Utilizing Ensemble Feature Selection For Detecting Http-Get Ddos Attacks In Cloud Computing Environments(2025-05)Cloud computing environment (cce)-based services present a novel paradigm for remote business management. One of the primary advantages of utilizing cce is the availability of on-demand services, thereby facilitating a pay-per-use model. This makes cce technology a convenient means of facilitating services over the internet. However, security vulnerabilities, such as distributed denial of service (ddos) attacks, particularly http-get ddos attacks at the application layer, pose a significant threat to service availability in cces. This thesis proposes a collaborative approach utilizing ensemble feature selection to detect http-get ddos attacks in cces. The proposed approach comprises six phases. The first phase entails data gathering and pre-processing, responsible for collecting and processing data from multiple sources. The second phase involves dataset generation, comprising the creation of a synthetic cce-specific dataset. The third phase focuses on feature enrichment, aiming to augment the avws access log extracted from vm activity and resource logs to enhance the detection of http-get ddos attacks. The fourth phase entails dataset validation, aimed at validating the dataset to ensure its validity and readiness, and confirming that it meets the requirements of a benchmark dataset. The fifth phase involves ensemble feature selection, aimed at selecting the most crucial and minimal feature set that contributes to detecting http-get ddos attacks. The sixth phase aims to develop a deep learning detection model based on long short-term memory (lstm) to detect http-get ddos attacks on cce accurately.
- PublicationPatient Safety Culture And Hospital Performance: Patient Safety Outcomes And Patient Rights In Public Traditional Chinese Medicine Hospitals In Sichuan Province, China(2025-06)This study examined the influence of patient safety culture on hospital performance within public traditional chinese medicine (tcm) hospitals in sichuan, china, by specifically considering the role of patient rights and patient safety outcomes. The study begins by establishing the importance of hospital performance, emphasising its multi-dimensional nature, which includes financial performance, customer satisfaction, internal business processes, and aspects of learning and growth. The study emphasises the significance of adopting a comprehensive approach, such as a balanced scorecard, for performance evaluation in healthcare settings, particularly public tcm hospitals in sichuan. The study highlights the role of patient safety culture as a critical factor impacting hospital performance. The study provides a detailed understanding of patient safety culture, which is described as the shared values, beliefs, and norms within a healthcare organisation that influence individual and collective behaviours related to patient safety.
- PublicationSynthesis, Characterization, Structure-Property Relationship And Non-Linear Optical (Nlo) Studies Of Halogenated Chalcones(2025-05)The influence of electron-withdrawing (ew) and electron-donating (ed) substituents on chalcone derivatives concerning the photophysical and third-order non-linear optical (nlo) properties was comprehensively studied using a combination of experimental and computational approaches. Three series with a total of eleven halogenated chalcone compounds, which include thienoyl- and benzoyl-based chalcones, were designed and synthesized. Characterization was performed using ftir and nmr spectroscopies, whereas crystal structures were investigated using scxrd analysis. Quantum-chemical studies revealed that changes in electronic properties are driven by substituent effects, including ed/ew character, electron delocalization, aromaticity and polarity govern. Derivatives with strong edgs and extended ring system exhibited higher molecular polarity, cyanine-like bond length alternation (bla) in enone moiety and narrower homo-lumo gap (< 3.7 ev). Uv-vis absorption spectroscopy confirms broad transparency (> 500 nm) across all derivatives, indicating their suitability for optical applications. Hole-electron analysis, ifct spectrum and aatcm heat map revealed excitation mechanisms governed by substituent positioning. Para-substituted edgs on b-ring facilitated long-distance electron redistribution, whereas steric hindrance from substituents proximal to π-conjugation bridge weakened charge delocalization.
- PublicationCorporate Financial Performance, Environmental, Social, And Governance (Esg Performance), Intellectual Capital, And Stock Return In Asean-5 Countries(2025-06)The covid-19 pandemic has significantly reduced the asean-5 stock market index's stock return. Factors such as environmental social governance (esg) performance and intellectual capital (ic) have been studied in developed countries but are rarely used as indicators for purchasing stocks in developing countries. The purpose of this study is to investigate the effect of non-financial (esg performance and ic components) and financial performance on stock return from 2017 to 2021 in asean-5 countries. This study uses signalling theory as an underpinning theory, legitimacy theory, and rbv theories as supporting theories to investigate the effect of non-financial and financial performance on stock return in asean-5 countries. This study is categorized as a quantitative research approach, using unbalanced panel data analysis obtained from the annual financial reports and accounts of selected enterprises from asean-5 nations, both before and during the covid-19 pandemic. The total data observation is 984 companies gathered from purposive sampling method and has two models covering data from 2017 to 2021. Model 1 uses data sample from the year 2017 to 2021 (overall condition). Model 2 uses data sample from the year 2017 to 2019 (before covid-19 pandemic). Model 3 uses data sample from the year 2020 to 2021 (during covid-19 pandemic). In this study, there are five diagnostic or assumption tests. There are multicollinearity, heteroskedasticity,
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- PublicationAn Improved Secure Router Discovery Mechanism To Prevent Fake Ra Attack In Link Local Ipv6 Network(2021-12)In the Internet Protocol Version 6 (IPv6) network, Neighbour Discovery Protocol (NDP) plays a vital role in configuring the IPv6 address for any type of host. The IPv6 host will obtains the IPv6 address using Stateless Address Autoconfiguration (SLAAC). SLAAC was implemented using two types of key ICMPv6 NDP message protocol i.e Neighbour Discovery (ND) and Router Discovery (RD) in the IPv6 network. The RD messages consist of Router Solicitation (RS) and Router Advertisement (RA) messages. The standard RD by design do not have trust mechanism to authenticate the legitimate host and router. This design flaw within RD protocol has led to Fake RA attacks. Studies shows that the standard RD protocol is vulnerable to Fake RA attack where the host will be denied legitimate gateway. In order to address this issue, several prevention techniques have been proposed in the past to secure RD process.
- PublicationPole climbing robot(2022-07-01)This project proposes the design of a pole climbing robot that has an ability to climb pipes, With the establishment of this project, chance of human wounds and death canbe limited while working in the dispersion lines which is the fundamental thought of this project. climbing robots is that kind of robots that can help in areas of work. The benefit of this work is to save human lives as a lot of them are die from electrical injuries almost every year around the world. The weight of the robot in this project does not exceed 4 kg. The design of this robot is very simple, and its mainfunction is to go up and down the pipes. Pole climbing robots are increasingly neededto carry out high-risk tasks for human beings. The main purpose of the project is to have an idea about designing a robot that can be used to work for a specific task whichis to climb a pole which can be used to reduce the risk on human beings. Various of sources related to the project have been found and studied along the implementation of the robot. Each of them had their own specific task and limitations by comparing them to each other. All this information of method and design is altered and combined that to create a new structure and functional robot. By referring to the related works. Various applications can be applied for the pole-climbing robot such as spray painting.
- PublicationIntelligent sequential repeater placement in soc design through reinforcement learning(2024-07)As the computational demands of artificial intelligence (AI) in chips skyrocket, the significance and complexity of interconnections grow notably. Therefore, optimizing the performance of system-on-chip (SoC) designs can be a challenging process, especially when it comes to positioning sequential repeaters. The placement of repeaters often requires strategic planning, as they are crucial for meeting the timing requirements of timing-critical topologies within the chip. They play a key role in strengthening signals to improve the overall power, performance, and area (PPA) of the SoC. In the traditional manual method, commonly used in the design flow, inefficiencies such as susceptibility to human mistakes and time consumption are evident. This project introduces a novel tool flow methodology (TFM) that leverages reinforcement learning (RL) to automate and optimize the placement and number of repeaters to meet the timing requirements in SoC designs. The Fusion Compiler (FC) tool with Python bindings is implemented as the RL environment. Moreover, a set of parameters, actions, states, and feedback (rewards or penalties) resulting from the timing delay after each subsequent placement of the repeaters from the environment are well-defined. In short, the current prototype proves the concept that the RL-driven approach demonstrates comparable performance to human expertise by offering a promising new automated solution for timing-aware sequential repeater placement by integrating RL and Topology Interconnect Planning (TIP) features into the FC tool.
- PublicationArrhythmia electrocardiogram (ECG) signal classification using long short term memory, lstm (deep learning).(2023-07)Arrhythmias, which are irregularities in the heartbeat's rhythm, can seriously harm a person's health. The type of rhythm disruption and the region of the heart where the disturbance arises are used to classify arrhythmias. However, it is nearly impossible to obtain a 100% accurate test due to the complexity of the cardiac conduction system through human eye. Recurrent Neural Networks (RNNs) is a type of artificial neural network that is commonly used to analysing ECG data due to its ability to analyse sequences of inputs. However, the vanishing gradient problem, which affects RNNs, causes delayed convergence and makes learning long term dependencies difficult as the gradient values are very small during backpropagation. Thus, LSTM which is specifically designed to overcome this limitation of traditional RNN is chosen in this project to classified arrhythmias from a dataset. A dataset of ECG signals is collected from PhysioNet Database which contains the recordings of several types of arrhythmias such as sinus arrhythmia, atrial fibrillation, ventricular tachycardia, and ventricular fibrillation. The raw data of ECG signals is first pre-processed to filter out the noise and normalizing the signals. Then, the data is then segmented into individual heartbeat and extracted relevant features for classification. Next, an LSTM model is developed to classify the ECG signals into different classes. Finally, the performance output of the model is evaluated using confusion matrix to assess the effect of different model parameters. Our findings show that LSTM neural networks show better results in classifying ECG arrhythmias compared to traditional RNN.
- PublicationWork-related musculoskeletal disorders (wmsds) and associated risk factors affecting oil palm employees(2024-07-01)The aim of this investigation focuses on the prevalence of work-related musculoskeletal disorders (WMSDs) among oil palm workers and the factors associated with these disorders. The project's background and problem statement are thoroughly studied and investigated. Data on the demographics, work-related activities, ergonomics, medical histories, and instruments and equipment utilized by oil palm workers are acquired via a comprehensive questionnaire form. The upper body region, specifically the lower back, upper arm, upper back and shoulders experiences pain and discomfort with surpassing 70%. The hip region in the upper body is the only body region with statistics surpassing 70%. Hot environment is at 100% when questioned regarding the environment exposed. The presence of musculoskeletal symptoms is assessed, along with feedback on the usability and user experience of the design intervention. A videography session is conducted and analyzed through a software known as KINOVEA and used to evaluate in OWAS scoring. Through the OWAS analysis, the collection and loading task with harvesting task is more rated at risk. Extreme forces are applied on the shoulder region based on the KINOVEA analysis. A smart watch device is used to monitor heartbeat rates of the oil palm workers. The Bangladeshi workers experience a stable heartbeat rate compared to the Indonesia workers. This is due to the role of environment exposed. The video and smart watch device helps validate and further investigates the prevalence of work-related musculoskeletal disorders (WMSDs) among oil palm workers. The collected data is also analysed using statistical techniques to determine WMSDs prevalence rates, identify associated risk factors, and establish correlations between various variables. Overall, this thesis serves as a valuable resource for professionals seeking to gain insights into work-related musculoskeletal disorders (WMSDs) and its broader impact on Oil Palm Industry.