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Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniques have been used to mitigate these threats have been discussed. The shortcomings of these state-of-the-art models and how attack patterns have evolved over the past decade have also been presented. Our goal is to assess how effective these machine learning techniques are against the ever-increasing threat of malware that plagues our online community.

Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with redundant or insignificant features increases the computational time and often decreases goodness of fit which is a critical issue in cybersecurity. In this research, we have proposed and implemented an efficient feature selection algorithm to filter insignificant variables. Our proposed Dynamic Feature Selector (DFS) uses statistical analysis and feature importance tests to reduce model complexity and improve prediction accuracy. To evaluate DFS, we conducted experiments on two datasets used for cybersecurity research namely Network Security Laboratory (NSL-KDD) and University of New South Wales (UNSW-NB15). In the meta-learning stage, four algorithms were compared namely Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units, Random Forest and a proposed Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for accuracy estimation. For NSL-KDD, experiments revealed an increment in accuracy from 99.54% to 99.64% while reducing feature size of one-hot encoded features from 123 to 50. In UNSW-NB15 we observed an increase in accuracy from 90.98% to 92.46% while reducing feature size from 196 to 47. The proposed approach is thus able to achieve higher accuracy while significantly lowering number of features required for processing.

This review highlights the recent advancements in the field of nanozymes and their applications in the development of point-of-care biosensors. The use of nanozymes as enzyme-mimicking components in biosensing systems has led to improved performance and miniaturization of these sensors. The unique properties of nanozymes, such as high stability, robustness, and surface tunability, make them an attractive alternative to traditional enzymes in biosensing applications. Researchers have explored a wide range of nanomaterials, including metals, metal oxides, and metal–organic frameworks, for the development of nanozyme-based biosensors. Different sensing strategies, such as colorimetric, fluorescent, electrochemical and SERS, have been implemented using nanozymes as signal-producing components. Despite the numerous advantages, there are also challenges associated with nanozyme-based biosensors, including stability and specificity, which need to be addressed for their wider applications. The future of nanozyme-based biosensors looks promising, with the potential to bring a paradigm shift in biomolecular sensing. The development of highly specific, multi-enzyme mimicking nanozymes could lead to the creation of highly sensitive and low-biofouling biosensors. Integration of nanozymes into point-of-care diagnostics promises to revolutionize healthcare by improving patient outcomes and reducing costs while enhancing the accuracy and sensitivity of diagnostic tools.

It is vital to enforce the distinguished assimilation of sustainability entities in higher education (HE) and establish an academia-wide collective curriculum on sustainability in an efficient manner, in order to disseminate and stimulate thoughts with forthcoming generations. The remit of this paper is to document the available literature and map the research landscape into a coherent taxonomy. This research adds to the available literature; moreover, it could also aid in the community comprehension of education in sustainable development (ESD) implementation and/or integration into higher education institutions (HEIs). We perform an attentive search in relation to education sustainable development (ESD) in three relevant electronic databases, namely, Web of Science (WoS), ScienceDirect and IEEE Xplore. The result of the review is a taxonomy that includes 148 articles published between early 2014 and mid-2019 that were then categorized into six classes, namely, the accomplishment of SD, awareness and commitment, evaluation and commentary, structural transformation, course coordination and university management. Additionally, we acknowledge varied involvement from different levels of communities supporting and promoting education to achieve sustainable development (SD).

Short Message Service (SMS) is becoming the secure medium of communication due to large-scale global coverage, reliability, and power efficiency. As person--to--person (P2P) messaging is less secure than application-to-person (A2P) messaging, anyone can send a message, leading to the attack. Attackers mistreat this opportunity to spread malicious content, perform harmful activities, and abuse other people, commonly known as spam. Moreover, such messages can waste a lot of time, and important messages are sometimes overlooked. As a result, accurate spam detection in SMS and its computational time are burning issues. In this paper, we conduct six different experiments to detect SMS spam from the dataset of 5574 messages using machine learning classifiers such as Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM), considering variations of \textit{Term Frequency-- Inverse Document Frequency (TF--IDF)} features for exploring the trade-off among accuracy, F1-score and computational time. The experiments achieve the best result of the accuracy of 98.50\%, F1--score of 98\%, and area under roc curve (AUC) of 0.97 for multinomial naïve bayes classifier with TF--IDF after stemming.

The leadership roles of green hotels are essential for sustainable development because of their contribution to reducing carbon emission, supplying safe food, and managing water resources. However, sustainability education has not adequately integrated entrepreneurial skills to promote green hotel practices towards sustainable development. Therefore, this study explored the leadership roles of the Frangipani Hotel in Langkawi, Malaysia, because it adopted a circular economy in line with the zero-waste concept to develop the environmental and socio-economic condition of the locality. Extensive literature reviews were conducted to explore the transformational leadership roles of the Frangipani hotel for green practices. Interviews with the managing director, general manager, and the head of 10 operating branches of Frangipani also provided information about the green hotel practices to support its leadership roles. Similarly, interviews with the public, private, business, and community stakeholders also supported the transformational leadership roles of Frangipani to integrated green hotel practices with effective multi-stakeholder collaboration for quality control and quality assurance of its green practices. The patience of Frangipani and its roles as a liaison among government, non-government, business, and private and community sectors have been very effective in promoting green practices in the hospitability industry via training and knowledge-sharing. However, the monitoring and evaluation of these green practices are essential, and they can be completed via developing a special key performance index for the effective promotion of green hotels.

Chemical pollution in the transboundary Langat River in Malaysia is common both from point and non-point sources. Therefore, the water treatment plants (WTPS) at the Langat River Basin have experienced frequent shutdown incidents. However, the Langat River is one of the main sources of drinking water to almost one-third of the population in Selangor state. Meanwhile, several studies have reported a high concentration of Arsenic (As) in the Langat River that is toxic if ingested via drinking water. However, this is a pioneer study that predicts the As concentration in the Langat River based on time-series data from 2005–2014 to estimate the health risk associated with As ingestion via drinking water at the Langat River Basin. Several time-series prediction models were tested and Gradient Boosted Tree (GBT) gained the best result. This GBT model also fits better to predict the As concentration until December 2024. The mean concentration of As in the Langat River for both 2014 and 2024, as well as the carcinogenic and non-carcinogenic health risks of As ingestion via drinking water, were within the drinking water quality standards proposed by the World Health Organization and Ministry of Health Malaysia. However, the ingestion of trace amounts of As over a long period might be detrimental to human health because of its non-biodegradable characteristics. Therefore, it is important to manage the drinking water sources to minimise As exposure risks to human health.

Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

This qualitative study describes the procedures undertaken to explore the Intangible Culture Heritage (ICH) preservation, especially focusing on the inhabitants’ garments of different ethnic groups in Weld Quay, Penang, which was a multi-cultural trading port during the 19th century in Malaysia. Social life and occupational activities of the different ethnic groups formed the two main spines of how different the inhabitants’ garments would be. This study developed and demonstrated a step-by-step conceptual framework of narrative analysis. Therefore, the procedures used in this study are adequate to serve as a guide for novice researchers who are interested in undertaking a narrative analysis study. Hence, the investigation of the material culture has been exemplified by proposing a novel conceptual framework of narrative analysis. This collaborative method has been utilized to ascertain the narrative data collected from an interview with visual and semiotic analysis. The information derived from the narrative interview is about the materials, colors, and elements of the garments of different ethnic groups (i.e., the Chinese, Indian, Malay and British). This collaborative process provides much valuable contextual and historical information to the researcher, as the interpretation and implicit understandings that underlie the stories people tell are beneficial in preserving and safeguarding this ICH. Therefore, this narrative study validates that the inhabitant’s garments are a means of intangible culture heritage (ICH) preservation and suggests guidance about how to conduct narrative analysis for mining historical data in a more explicit manner.

Assessing and modeling the changes in land use and land cover (LULC) patterns and associated ecosystem service values (ESV) has become an important global agenda for formulating sustainable land management policies. Taking the coastal region of Bangladesh (CRB) as a case study, we utilized remote sensing and GIS techniques to analyze Landsat data of 1999 and 2019 to estimate the effect of LULC changes on the ESVs. The LULC classification, ESV quantification, and spatial variations were performed by semi-automated classification, per-unit value transfer, and spatial autocorrelation techniques, respectively. First, between 1999 and 2019, LULC altered dramatically (agricultural land and coastal wetlands decreased, while all other LULC types increased). Second, the total ESV decreased by 1.87%, which was mostly attributed to a 70% growth in both rural settlement and aquaculture/saltpan, and a 30% decline in both agricultural land and wetlands. Third, significant spatial correlation and moderately high spatial clustering were observed, which consisted mostly of mangrove forests, waterbodies, and wetland zones. Both high-high and low-low values increased, but spatial outliers remained unchanged. Conserving agricultural land should be prioritized in future land use plans of CRB to meet the ever-increasing food demands, control natural land conversion, and make land use sustainable.

Prolonged persistence of toxic cadmium (Cd), chromium (Cr) and lead (Pb) in the aquatic environment are due to its nonbiodegradable characteristic. A few studies have reported higher concentrations of these metals in the transboundary Langat River, Malaysia. This study determined the spatial and temporal distributions of Cd, Cr and Pb concentrations (2005&ndash, 2015) in the Langat River along with assessing the status of these metals in the drinking water supply chain at the basin. Water samples were collected once in 2015 from the drinking water supply chain, i.e., from the river, treated water at plants, taps and filtration water at households. Determined mean concentrations of Cd, Cr and Pb by inductively coupled plasma mass spectrometry in the Langat River were within the drinking water quality standard of Malaysia and the WHO, except for the Pb (9.99 &plusmn, 1.40 &micro, g/L) concentration, which was at the maximum limit, 10 &micro, g/L. The spatial and temporal distribution of these metals&rsquo, concentrations indicate dilution of it downstream, along with the increasing trend in rainfall and water flow, especially during the northeast monsoon. Significant correlation and regression analysis of the Cd, Cr and Pb concentrations also indicate that the sources of this metal pollution are mainly the natural weathering of minerals along with anthropogenic activities in the basin. The determined overall water quality of the Langat River is categorized Class IIA (i.e., clean), which requires conventional treatment before drinking, however, the maximum removal efficiency of these metals by the plants at the basin was about 90.17%. Therefore, the proactive leadership roles of the local authorities will be appropriate to reduce the pollution of this river as well as introducing a two-layer water filtration system at the Langat River Basin to accelerate the achievement of a sustainable drinking water supply.