Key insights into recommended SMS spam detection datasets

Short Message Service (SMS) spam poses significant risks, including financial scams and phishing attempts. Although numerous datasets from online repositories have been utilized to address this issue, little attention has been given to evaluating their effectiveness and impact on SMS spam detection models. This study fills this gap by assessing the performance of ten SMS spam detection datasets using Decision Tree and Multinomial Naïve Bayes models. Datasets were evaluated based on accuracy and qualitative factors such as authenticity, class imbalance, feature diversity, metadata availability, and preprocessing needs. Due to the multilingual nature of the datasets, experiments were conducted with two stopword removal groups: one in English and another in the respective non-English languages. The key findings of this research have led to the recommendation of Dataset 5 for future SMS spam detection research, as evidence from the dataset’s high qualitative assessment score of 3.8 out of 5.0 due to its high feature diversity, real-world complexity, and balanced class distribution, and low detection rate of 86.10% from Multinomial Naïve Bayes. Recommending a dataset that poses challenges for high model performance fosters the development of more robust and adaptable spam detection models capable of handling diverse forms of noise and ambiguity. Furthermore, selecting the dataset with the highest qualitative score enhances research quality, improves model generalizability, and mitigates risks related to bias and inconsistencies

On Ordered Weighted Averaging Operator and Monotone Takagi-Sugeno-Kang Fuzzy Inference Systems

—The necessary and/or sufficient conditions for a TakagiSugeno-Kang Fuzzy Inference System (TSK-FIS) to be monotone has been a key research direction in the last two decades. In this paper, we firstly define fuzzy membership functions (FMFs) with single and continuous support; and consider TSK-FIS with a “grid partition” strategy for computing its firing strengths with product T-norm (here after denoted as TSK-FIS-product). We also define a more general joint necessary condition, whereby each constituent itself is a necessary condition for the TSK-FIS-product model. The first necessary condition indicates that the normalized firing strength must not be indeterminate (i.e., 0/0), i.e. susceptible to the “tomato classification problem”. The second necessary condition indicates that all restricted consequents of fuzzy if-then rules must be defined. Based on the principle of the ordered weighted averaging (OWA) operator as well as the concept of increasing orness in OWA and hyperboxes, a general joint sufficient condition for a TSK-FIS-product model to be monotone is derived. Three case studies of the developed methods for undertaking Failure Mode and Effect Analysis (FMEA) and image processing tasks are presented. The results are compared, analyzed, and discussed, demonstrating the usefulness of our developed methods

Gamification and technology acceptance model : a systematic review and future research directions

Technological advancements have popularized "gamification" in recent years, yet few studies have explored its connection to the technology acceptance model (TAM). This paper aims to enhance understanding of the relationship between gamification and TAM by systematically reviewing current research trends. Employing a systematic literature review (SLR) method, we analyzed 72 papers identified via Scopus, focusing on 13 journal papers published between 2016 and 2020 that met our criteria for in-depth analysis. Our findings indicate a significant rise in research on gamification and TAM, with nearly half of the studies (49%) incorporating new external variables into the original TAM framework. The study identifies three key themes for future research. By providing a comprehensive review, this study contributes new knowledge and offers a critical summary for further investigation into the integration of gamification with TAM, highlighting potential avenues for future research and practical application

Climate change, soil health, and governance challenges in Ghana : A review

Climate change poses significant challenges to soil health and agricultural productivity in Ghana, with implications for similar contexts worldwide. This review synthesises existing knowledge on the impacts of climate change on soil properties across Ghana's diverse agro-ecological zones, examines the effectiveness of current governance responses through an adaptive governance lens, and identifies critical research and policy gaps. Ghana provides an instructive case study because of its diverse agroecological zones, agriculture-dependent economy, and dual governance systems that combine formal institutions with traditional authorities. The study employs a comprehensive literature search across multiple databases, qualitative synthesis methods, and conceptual frameworks to analyse the complex interactions between climate factors, soil health parameters, and governance structures. Key findings reveal that increasing temperatures and erratic rainfall patterns contribute to soil moisture depletion, organic matter loss, and reduced fertility. Ghana's soil health governance faces limitations due to policy fragmentation, resource constraints, and insufficient stakeholder collaboration. Successful case studies highlight the potential of integrating traditional knowledge with modern soil conservation practices and emphasize the importance of community-driven approaches. From the review, it is recommended that a comprehensive national soil health policy aligned with climate adaptation strategies be developed, institutional capacity be strengthened, and participatory governance mechanisms be promoted. The findings contribute to a broader understanding of environmental governance under climate change, with relevance to international frameworks including the Sustainable Development Goals and the Global Soil Partnership. Addressing identified research gaps and implementing the proposed adaptive governance framework are crucial for enhancing resilience to climate change impacts on soil health in Ghana and other regions facing similar challenges

Formulation and Mitigation of Soft Error in CMOS Memory System by using Transmission Gate

The downscaling of technology has resulted in increased packing density in CMOS technology and thus, a worrying uptick in single event upset susceptibility in contemporary technology. SRAMs in particular, which now occupy up to 70% of all chip size, are vulnerable to errors from single event upsets, leaving the reliability of its memory storage potentially compromised. The application of submicron electronics in areas of high particle activity in fields such as aerospace calls for the need for technology that is resistant against the occurrence of soft errors, with an increased capacity of mitigating the phenomenon of single event upsets. The current literature has proposed methods such as radiation hardening through methods such as transistor sizing and triple modular redundancy which are unable to keep pace with technology downscaling. This study aims to characterize and model the transient pulse formed from a single event upset, formulate the probability of a state flip in various SRAMs, and produce a transient filter based on transmission gate. The transient pulse, modelled by the double exponential model is injected into the vulnerable memory nodes of the interlocked inverters, Q and QB in the 4T, 6T and 9T SRAMs to observe the amplitude of transient pulse required to incite a state change. The critical charge is then calculated from the readings, and its subsequent probability is calculated further based on the memory node area, technology node of 180nm and the atmospheric cross section per unit area constant. The transmission gate transient filter SRAM achieves an 88% improvement in error probability reduction

Advancing pedestrian safety in the era of autonomous vehicles : A bibliometric analysis and pathway to effective regulations

With the significant transformation in transportation, it is crucial to understand the impact of autonomous vehicles on pedestrian safety. To address this, a bibliometric analysis was carried out on 368 publications from 2013 to 2023, emphasising on autonomous vehicles and pedestrian safety. The study demonstrated a substantial impact with a total of 6366 citations and an average of 17.3 citations per publication. The journal “IEEE Transactions on Intelligent Transportation Systems” was identified as the primary source of contributions, with China and the United States leading in terms of productivity. Three major research clusters related to autonomous vehicles and pedestrian safety were identified: (1) behavioral and interaction studies, (2) technological advancements for collision avoidance, and (3) statistical analysis and risk perception. Many studies have been done on this topic, but there is still a significant gap in the comprehension of how autonomous vehicle works and applies in the real world. For example, most studies only use controlled tests, which raises concerns about their validity. As a result, policymakers need to put pedestrian safety first by addressing behavioural factors and ethical concerns, passing laws, influencing the design of infrastructure, educating the public, making it easier to collect data, and encouraging participation. Decisive action is needed to build a safe and long-lasting ecosystem for autonomous vehicles that protects pedestrians and makes communities safer

A Multi-Criteria Recommendation Technique for Personalized Tourism Experiences

This research aims to design a multi-Criteria recommendation technique for the tourism domain. In the past few decades, the growth of the World Wide Web has led to an unprecedented amount of information available to us. This phenomenon has resulted in what we now call information overload, where the sheer volume of data surpasses our capacity to manage it effectively. In order to address this issue, it is crucial to ensure that accurate information is communicated to the appropriate audience, as proper information dissemination is key. Like other industries, the tourism industry faces the challenge of information overload. The abundance of information can be overwhelming for both tourists and industry stakeholders. Some tourists like guided tours, while others prefer exploring independently, and that is where e-tour guides can be helpful. E-tour guides are digital tools like apps or websites that inform tourists about their destination. In this context, recommendation systems and information dissemination are closely related. Recommendation systems use algorithms to analyze user data and provide personalized recommendations based on their past behaviour and preferences. Both recommendation systems and information dissemination aim to provide users with relevant and useful information. This study aims to develop a multi-criteria recommendation system that effectively addresses the issue of information overload in the tourism industry by delivering pertinent information to the right users. The proposed method combines several techniques, including deep learning and traditional techniques, such as matrix factorization, to address common challenges like scalability and data sparseness. The approach is designed to provide personalized recommendations based on user location, site, and other relevant criteria. By emphasizing the filtering and provision of the most relevant information based on user location and site, the user experience and engagement can be significantly improved. This study utilizes the Convolutional Matrix Factorization (CMF) algorithm due to its compatibility with tourism. The proposed algorithm, CMF with ResNet, combines the power of CMF with the superior performance of ResNet to overcome the limitations of CMF and achieve even better results for recommendation tasks. Using ResNet, the algorithm can learn more complex and nuanced patterns in the data, leading to more accurate recommendations. In the end, compared to all the tested algorithms, the proposed method outperformed and achieved higher scores on error measurement metrics. Additionally, it can handle complex problems like sparse data or very small amounts of training samples. The proposed method also provided more relevant recommendations to users compared to all other tested algorithms. Overall, the proposed method offers a novel and effective solution to the information overload challenge in the tourism industry

A Retrospective View of Discourse-Historical Approach (DHA)

The paper provides a retrospective view of the discourse-historical approach (DHA) by conducting a bibliometric analysis of articles on DHA in the Scopus database for 2002-2023. A total of 335 documents were retrieved, indicating that the field remains relatively new and unsaturated. The United Kingdom was the leading contributor to DHA research because the founder of DHA, Ruth Wodak is from Lancaster University. The focal point of DHA research has been on media and political discourse because of the research interests of Wodak and her former postgraduate students, Boukala and Forchtner, who published prolifically on DHA. However, the most globally cited work is Baker et al.‘s (2008) ―A useful methodological synergy? Combining critical discourse analysis and corpus linguistics to examine discourses of refugees and asylum seekers in the UK press‖. The bibliometric analysis showed that the Journal of Language and Politics has the most publications on DHA while Discourse and Society has the highest total citations. DHA research since 2017 has gravitated towards analysis of ―argumentation‖ in populism, ideology and COVID-19 in social media discourse, and there is an emergence of research focusing on the nomination and predication strategies in new areas such as interpretation and Islamophobia. The study indicates that selectivity in the use of discursive strategies may hamper the potential of DHA to explain how societal changes influence discourse, and for the field to advance, it is essential to revisit a comprehensive framework that necessitates an examination of all five discursive strategies

Optimization of Mechanical and Durability Properties of Manganese Slag Hybrid Fiber Concrete Using an L9 Orthogonal Design and Grey Relational Analysis

Studies have highlighted manganese slag (MnS) mixed-fiber concrete as a construction material, in view of the effects of varying proportions of MnS, steel fibers (SF), and polypropylene fibers (PPF) on the mechanical properties and durability of C30 MnS hybrid fiber-reinforced concrete (MHFC). Using an L9 orthogonal design, ten mix ratios were tested for compressive strength, flexural strength, and chloride ion resistance at 7, 28, 56, and 91 days. A grey relational analysis (GRA) method was employed to comprehensively evaluate nine mix proportioning schemes across four curing ages. By analyzing the grey relational degrees, the optimal mix proportioning scheme was identified. Results indicated that SF had the greatest positive impact on both compressive and flexural strength, followed by MnS, while PPF had a limited effect. The optimal mix—20% MnS, 1.0% SF, and 0.5% PPF—achieved a 23% increase in compressive strength and 33% in flexural strength at 28 days. In terms of the durability of concrete in corrosive environments, the optimal performance was achieved with a mix proportion of 10% MnS, 1.0% SF, and 1.0% PPF. These findings provide guidance for optimizing MHFC and highlight the potential of industrial by-products in enhancing concrete durability. Further research is recommended to refine mix designs and assess long-term field performance

SAR Distribution with Different Water Bolus Shapes for Hyperthermia Breast Cancer Treatment

Hyperthermia is an alternative treatment for breast cancer and involves a high temperature of 41℃ to 45℃ to heat malignant tissues into necrotic tissues. A 915MHz and 2450MHz rectangular microstrip patch inset feed line antenna is designed with SEMCAD X 14.8.4 software simulator. The non-invasive antenna is used in the hyperthermia treatment to destroy the malignant tissues. However, hyperthermia creates unwanted hotspots and causes skin burn problems. Therefore, a water bolus is designed with several shapes coupled with an antenna to cool the treated areas. In this research, the hyperthermia treatment is applied to the three different sizes of malignant tissues labelled as T1, T2 and T3 with diameters of 15mm, 34mm and 59mm respectively. The sizes are based on the data from the mammogram image analysis received from the hospital. The observation is made for the antenna with and without water bolus. A deionized and distilled water bolus is designed with rectangular, circular and sphere shapes and integrated with the antenna. The applicator’s performance is analysed. The simulation results show that the water bolus helps to reduce unwanted hotspots, increase the focus position distance and is able to control delivered heat more uniformly to surrounding malignant tissues. The sphere shape shows better SAR performance than circular and rectangular. Moreover, the procedure can be done in a much shorter time using a sphere shape than two other shapes