A Lightweight CNN Model Using Depthwise Separable Convolutions for Brain Tumour Classification

Every year, the number of patients with brain cancers (BCs) or brain tumours (BTs) increases. This trend emphasises the necessity of a computerised system for rapid and accurate detection during the diagnosis of BTs. This paper presents a lightweight deep learning (DL) model based on a convolutional neural network (CNN) for a fast and accurate BC detector. The core component of the BC detector is a depthwise separable convolution (DSConv) on top of the 24-layer CNN architectures. The usage of DSConv with Adam’s optimiser achieves comparable effectiveness to conventional convolutional layers, although using fewer parameters. Additionally, L2 regularisation, dropout, and data augmentation were implemented to mitigate the issues of overfitting. The proposed model was trained and tested using the publicly available dataset consisting of MRI images collected from 233 patients in Nanfang Hospital and General Hospital, with 3063 images in total. In summary, the DSConv-based CNN model demonstrates an average accuracy of 97.50% and has an average inference time of 2.1 milliseconds per classification. It consistently surpasses 96.50% accuracy in the classification of the three types of BTs. These findings indicate that the model is well-suited for accurate BTs classification, particularly for glioma, meningioma, and pituitary tumours from MRI images

Exploring the Role of Environment Service Quality in Enhancing Customer Satisfaction During Umrah in Malaysia

The Umrah industry in Malaysia faces numerous challenges, particularly fraud, which leads to high customer dissatisfaction with the services offered by Umrah operators. Environmental service quality difficulties in Umrah refer to the challenges of cleanliness, sustainability, and infrastructure that impact pilgrims\u27 experiences and contentment. To address this, the study examines the relationship between environmental service quality (focused on airline service, hotel service, transportation service, and ambient) and customer satisfaction during the Umrah. This study employed a quantitative, cross-sectional approach, with 219 Umrah pilgrims aged 28 to 58 being the most significant sample of travellers. Respondents are chosen using convenience sampling. Data is gathered via online questionnaires and direct participation at airports like KLIA and KLIA 2. The research objective was achieved via descriptive analysis and multiple regression. This study also helps to understand customer satisfaction and demands, which is essentially the degree to which customers are satisfied with services, particularly those provided by airlines, hotels, transportation, and the environment, which can contribute to people\u27s satisfaction while performing Umrah

Function of Construction Waste as A Replacement Material for Stones in Stone Column

Sustainability is an important aspect of the Civil engineering domain and mainly focuses on the reduction of carbon footprints. One of the major contributors to carbon footprint is construction and demolition (C&D) waste, which increases with urbanisation. With an increase in awareness of recycling, the usage of C&D waste is used as a replacement material for stones in a stone column. The stone column is a conventional ground improvement technique used to stabilise the soil by improving its strength and settlement behaviour. The C&D waste of M30-graded crushed concrete and the brick is considered and used as a replacement material to make columns of two consistencies (0.4 and 0.6). The other waste material of C&D waste is removed and crushed to a size of 5 mm and used as a column material. A load-penetration study is conducted in California Bearing ratio (CBR) mould by developing a column in the center of the mould with 100% of conventional aggregate, crushed concrete and bricks which is compared with the virgin clay.  Due to the interlocking behaviour of the column materials, the initial behaviour of the load-penetration behaviour follows the same pattern. With an increase in load intensity, the load bearing of crushed concrete is similar under higher consistency. As the water retention of brick is more for lower consistency, the load bored by 100% crushed brick is the same as that of stone aggregate.  There is almost 4 times increase in the strength of composite ground compared with virgin clay. Making the zero-value material more useful by creating a circular economy

Implementation of APPGM-SDG Solutions Initiatives and Impact Evaluations in Sarawak\u27s South Region

As public awareness of the Sustainable Development Goals 2030  has grown, the Malaysian Parliament formed the All-Party Parliamentary Group Malaysia on the Sustainable Development Goals (APPGM-SDG). As part of a bipartisan effort to enhance the implementation of SDG targets in Malaysian parliamentary seats, one of the goals of this research is to investigate the SDG implications of beneficiary solution initiatives. A team is tasked with assembling a report that combines the impact evaluation of SDG initiatives in the Sarawak South Region. Through document analysis, focus groups, and site visits, the regional research team investigated, from December 2023 to February 2024, how the knowledge, skills, networks, and systems of these projects as well as psychology affect the beneficiaries of the solution projects. The paper also addresses the efficiency with which solution providers manage and execute projects, the impact on the beneficiaries, the alignment and mapping of the solution providers\u27 and impact evaluation with the SDG goals and APPGM-SDG modules, and the identification of obstacles and recommended solutions. The study also includes the qualitative and quantitative analysis that the assessors conducted using standardised questionnaires to measure the following six (6) crucial factors: Deep, Clear, Wide, High, SDG and Gender. The primary conclusion is that the goal of all solution initiatives is to eradicate poverty. It is intended that this paper\u27s discussion of the difficulties would better assist project implementers’ and policymakers in making decisions, designing programmes, and writing policy papers, especially when it comes to the economic, social, and environmental domains with specific SDG deliverables and target audiences in mind.  With the aim to improve communication between stakeholders and provide more substantial prospects for society after project execution, the research suggests more visitations approaches

Tasawur Pembangunan Masyarakat Madani (T-PMM): Satu Analisis: The Development Worldview (Tasawur) of Madani Society (T-PMM): An Analysis

Malay: Pembangunan masyarakat madani merupakan suatu bukti dinamika intelektual muslim dalam usaha memaknai ajaran Islam dan pengadaptasiannya dalam segenap ruang kehidupan. Wacana masyarakat madani telah terbentuk dalam tempoh waktu yang panjang dan cuba dijelmakan untuk memenuhi ciri-ciri sebuah masyarakat yang bertamadun. Secara umumnya, fokus kepada pembangunan masyarakat madani adalah pembinaan negara kota yang beracuankan tasawur Islam yang tulen. Walau bagaimanapun, perbincangan secara khusus mengenai acuan atau tasawur pembangunan masyarakat madani (T-PMM) ini masih belum dilakukan. Maka timbul persoalan, apakah T-PMM ini? Disebabkan masyarakat madani ini dilihat sebagai salah satu daripada pendekatan pembangunan berteraskan Islam (PBI), maka apakah T-PMM ini patut dibina berdasarkan tasawur PBI? Bagi menjawab semua persoalan ini, kajian kualitatif ini menggunakan kaedah analisis kandungan bagi menganalisis semua data sekunder yang dikumpul untuk mengenal pasti T-PMM yang sebenar. Sebagai hasilnya, kajian ini mendapati T-PMM patut dibina berdasarkan T-PBI. Oleh itu, dicadangkan satu kerangka T-PMM yang sepatutnya diaplikasikan dalam segala aktiviti pembangunan masyarakat madani. English: Development of the Madani society is unquestionable evidence of Muslim intellectual dynamism in efforts to interpret Islamic teachings and its adaptation as well as application in all spheres of life. Discourse on the Madani society has evolved over a long time and it has tried to embody the characteristics of a civilized society. In general, focus on the development of the Madani society basically concerns the construction of a city-state based on a genuine Islamic worldview. However, specific discussions about the mould or worldview concerning the development of the Madani society (T-PMM) has yet to materialise. Hence, what is T-PMM? Since the Madani society is presumed to be one of the Islamic-based development approaches (PBI), thus, should the T-PMM be designed based on the PBI concept? In order to answer these questions, this qualitative study used the content analysis method to analyse all the secondary data collected to identify the real T-PMM. Findings indicate that T-PMM should be built based on T-PBI. Therefore, it is proposed that a T-PMM framework should be applied in all Madani society-related development activities

The Influence of Board Diversity on Directors’ Networks and Corporate Risk-Taking: A Systematic Review

Board diversity is increasingly acknowledged as a cornerstone for enhancing the strategic decision-making capabilities of directors\u27 networks, particularly in the context of corporate risk-taking. This systematic literature review aims to systematically investigate the influence of board diversity on directors’ networks and corporate risk-taking. The key issue highlighted is the need for an updated and structured review to deepen understanding of how board diversity affects directors’ networks and corporate risk-taking. To achieve this objective, an extensive search was conducted for scholarly articles within prominent databases such as Scopus and Web of Science, focusing on studies published between 2024 and 2025. The study adheres to the PRISMA framework for systematic reviews, which yielded twenty-one (n=21) final primary data articles for analysis. The findings are thematically classified into two themes: (1) directors’ networks influence corporate risk-taking, and (2) board diversity influences directors’ networks and corporate risk-taking. The analysis reveals that board gender diversity (BGD) and board ethnicity diversity (BEG) on board diversity moderate the relationship between directors’ networks and corporate risk-taking, depending on the company’s context, governance architecture, and external institutional pressures. In conclusion, strategically integrating board diversity into the corporate risk-taking framework while leveraging directors’ networks cultivates inclusive and accountable decision-making practices.

Adaptive Intrusion Response via Federated Meta-Learning for IIoT Zero-Day Mitigation

The industrial evolution has meant that Industrial Internet of Things (IIoT) devices have exponentially increased, and as such, industrial automation has now become a reality since they are capable of monitoring in real-time, making predictions, and improving efficiency. These environments are fluid and privacy-sensitive environments that need collaborative and privacy-preserving learning models that are able to rapidly adapt to the emerging threats. Federated meta-learning has become a potentially promising method that unites the flexibility of meta-learning and the distributed and privacy-concerned design of federated learning. This document suggests an adaptive security solution to employ Model-Agnostic Meta-Learning (MAML) and Reptile-based federated approaches to intrusion response and zero-day attacks prevention of IIoT systems. The experimental data needed to train and test involves synthetic traffic of IIoT networks that is simulated using stochastic attack generator modules. Indicators of performance, namely detection performance, false positive rate, latency, and convergence efficiency, were provided by means of classification tools in the form of confusion matrix visualization, ROC curves, and loss progression graphs. The framework has been tested and run in a simulated environment of controlled tests in which MATLAB code driven by a matrix has internal data and inbuilt comparative agents. The experiments reveal that MAML-FL has a better result when it comes to generalization and zero-day threats mitigation, whereas Reptile-FL is more efficient in terms of seeking communication and faster convergence rates. In this paper, the authors present a scalable and robust architecture capable of providing a trade-off between real-time learning, adversarial robustness, and communication efficiency, thereby making the IIoT ecosystems intelligent and secure in their automation

Enhanced Image Encryption Using Pixel-Block Permutation and Multi-Chaotic Maps with DNA-Based Diffusion

As differential and statistical attacks become more prevalent, enhancing image encryption is a significant concern. The proposed method in this study implements dual security enhancement by integrating pixel-level techniques with block-level modifications while utilizing Hénon for the red channel and Logistic for both the green and blue channels to achieve encryption. The encryption algorithm begins by dividing the image into four main blocks before performing multiscale scrambling of increasing sub-blocks through permutation. This approach aims to enhance confusion and diffusion by mixing the data through multilevel chaotic scrambling. The encryption process incorporates pixel-level confusion, subsequently followed by block scrambling to maximize the scrambling effect and complexity. During diffusion, the confused image undergoes two operations, including DNA encoding and XOR operations, to create robust data protection methods. Experimental results demonstrate that the proposed algorithm achieves strong encryption, evidenced by a high entropy value, minimal correlation, and key change sensitivity, verifying its resistance to differential and statistical analysis attacks. In conclusion, the method provides both good speed and security, making it a suitable choice for protecting and distributing images.

LSOARP: A Link Stability and Obstacle-Aware Routing Protocol for UAV Networks

As using Unmanned Aerial Vehicles (UAVs) continues to grow across military, environmental, and public safety sectors, we are seeing a fast development of Flying Ad Hoc Networks (FANETs). Despite this progress, creating reliable routing protocols for UAVs remains complex because of their high mobility, constantly changing network topology, frequent link drops, and physical obstacles in the environment. Current protocols often overlook the importance of link stability and obstacle-aware navigation, which can lead to decreased performance in real-world applications.  we present LSOARP: a Link Stability and Obstacle-Aware Routing Protocol customized for UAV networks. This new protocol combines Bézier-curve-based trajectory adjustments for better obstacle avoidance with a multi-criteria link evaluation that considers residual link lifetime, energy efficiency, and route availability. We model UAV movement using a realistic prediction mechanism that captures various states such as high, low, idle, and paused. Routing decisions are then made using a weighted cost function to select the most stable and energy-efficient paths, ensuring strong network performance. Simulation experiments conducted under different conditions—including varying node density, speed, pause times, and traffic loads—show that LSOARP considerably outperforms traditional protocols like RLPR and AODV. It offers higher packet delivery ratios, lower end-to-end delays, reduced energy consumption, and less control overhead. These promising results demonstrate that LSOARP is both scalable and reliable in complex UAV environments, making it a strong candidate for real-time FANET applications

Efficient Kidney Cancer Classification from CT Images Using a Lightweight Convolutional Neural Network Optimized with an Enhanced Crow Swarm Optimization Algorithm

Kidney cancer is among the fifty most common cancers in the global statistics, therefore, early and accurate classification could enhance the prognosis. However, present classification models are more of a challenge to handle with data obtained from CT imaging. Our study proposes a lightweight and automated classification for kidney cancer detection using a hybrid feature extraction approach and a novel lightweight convolutional neural network improved by a hybrid Crow Swam Optimization (CSO) algorithm. Two datasets were used to develop and validate the model: the CT Normal – Kidney dataset containing 6,101 CT images and the CT Cyst, Tumor & Stone Kidney – Normal dataset comprising 6,345 CT images together and the Kidney Cancer dataset with 8,400 images. The technique used for feature extraction involved the use of multiple descriptors where useful image features were obtained. This was followed by optimising the Hybrid CSO algorithm with better results observed on augmented feature selection for better classification. The experiments’ outcomes were an accuracy of 100%, an F1-score of 97.49%, a Precision of 97.97%, a recall of 98.28% fast processing and the model’s successful differentiation of kidney pathologies. This more efficient and accurate framework, based on the application of both deep learning and conventional methods depending on levels of accuracy, opens up a valuable window on real-time kidney cancer classification that should directly assist radiologists in clinical diagnosis and raise detection reliability