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Colorectal cancer (CRC) is a prevalent and life-threatening malignancy, demanding early diagnosis and effective treatment for improved patient outcomes. Accurate segmentation of colon cancer in medical images is a challenging task due to the complexity of its morphology and limited annotated data availability. This paper presents an efficient approach for colon cancer segmentation and image synthesis, combining an Attention U-Net and Pix2Pix Generative Adversarial Network (Pix2Pix-GAN) guided by Sine Cosine Algorithm (SCA) for hyperparameter tuning within the GAN framework. The utilization of SCA plays a pivotal role in optimizing the delicate balance between generator and discriminator dynamics, resulting in enhanced convergence and stability. Our method achieved state-of-the-art results with a mean Dice score of 0.9514, mean Intersection over Union of 0.9123, F beta score of 0.9636, and similarity index of 0.9430 outperforming existing methods. Moreover, the Mean Absolute Error reached a minimal value of 0.01583. This proposed approach shows promise in enhancing the accuracy and robustness of colon cancer diagnosis and treatment which could lead to better diagnosis and treatment of colon cancer.

The field of low-resource sentiment analysis has seen significant developments in recent years. This research review SLR evaluates the approaches and data sources utilized in low-resource sentiment analysis by deep learning. The primary aim is to discover suitable approaches for future sentiment analysis in low-resource. Our studies explore various languages, models, and data sources expressing a desire to create effective approaches. Our emphasis lies in the critical evaluation of the approaches and the datasets utilized, to identify areas where further research is needed. Our analysis study adds to the existing body of literature reviews, encompassing multilingual low-resource sentiment analysis research spanning from 2018 to 2023. The findings indicate that the transfer learning approach is the most frequently used, followed by word embedding learning and machine translation systems. Additionally, the study shows that social media is the most used platform for data collection, followed by product reviews, movies, and hotels. There has been a significant surge in the adoption of pre-trained transformers, indicating a growing interest in exploring the potential of these models for low-resource languages within the natural language processing (NLP) community. This trend is largely attributed to the novel nature of these models and their feature of being non-labour intensive. However, the scarcity of annotated datasets for such languages remains a major hurdle. finally, these research findings are relevant and informative for any researcher working in the field of low-resource multilingual sentiment analysis. The study introduces a conceptual framework for performing sentiment analysis in low-resource. The study provides a valuable resource for future researchers.

Energy is of paramount importance for the world, and it is a fundamental driver of economic growth and development. Industries, businesses, and households rely on energy for even a small task. Due to its high demand, a significant portion of the global population still lacks access to reliable and affordable energy sources. Many industries and sectors continue to waste significant amounts of energy through inefficiencies. While energy is essential, the production and consumption of energy can have significant environmental consequences. Predicting power usage can help to significantly improve energy efficiency, reduce costs, enhance grid reliability, and minimize the environmental impact of energy consumption. In this work, a novel model named, the Optimized Multi-level Multi-type Ensemble (OMME) Forecasting Model is presented to estimate the power consumption. The proposed model was applied to the data set of total power consumption recorded in Austria at each hour after the pre-processing. The model applied bootstrapping at level 1 and a hybrid ensemble at level 2. Both ensemble methods utilized different algorithms for time series forecasting including ARIMA, Exponential smoothing, LSTM, GRU, and MLP. The parameters were tuned using Bayesian and Tabu search optimization. Different experiments were conducted for day and night usage separately. The proposed model was able to estimate the power usage with an error of 22%. This work also learned the most suitable technique for power consumption time series. GRU performed very well in different experiments and gave the forecast with 12% error. Distribution graphs of the OMME prediction further validate the integrity of the results.

The novelty of Federated Learning (FL) has emerged as a promising alternative to centralized machine learning systems in the context of anomaly-based intrusion detection systems (AIDS) deployed on Internet of Things (IoT) devices. Unlike traditional centralized models, FL allows on-device model training and updates, reducing privacy concerns and issues such as single points of failure and high false alarm rates (FAR). This approach, termed ‘Fed-AIDS,’ offers a more secure and efficient solution. However, the development of Fed-AIDS models faces challenges related to limited training data and the diverse nature of IoT datasets. Additionally, FL’s decentralized nature introduces weight divergence issues arising from non-Independently and Identically Distributed (non-IID) clients. To address these challenges and optimize Fed-AIDS modeling, interdisciplinary research efforts are vital. The primary objective of this study is to conduct an up-to-date review by adopting a Systematic Literature Review (SLR) approach to analyze existing studies of Fed-AIDS modeling procedures for IoT devices. Data from the published studies were retrieved from Scopus database, which covered major publishers such as IEEE, Elsevier and others. Specifically, our review conducted from the following Fed-AIDS perspectives: workflow and tools, training dataset, complexities of non-IID data in Fed-AIDS models, classification tasks, aggregation tasks, and model validation metrics. Based on the research findings, the study highlights a series of challenges and proposes potential solutions to stand in future research in Fed-AIDS modeling, aiming to advance the field of IoT device security.

In the context of Enhanced Remote Area Communications (ERAC), Radio over Fiber (RoF) technology plays a crucial role in extending reliable connectivity to underserved and remote areas. This paper explores the significance of fifth-generation (5G) Digital Predistortion (DPD) role in mitigating non-linearities in Radio over Fiber (RoF) systems for enhancing communication capabilities in remote regions. The seamless integration of RoF and 5G technologies requires robust linearization techniques to ensure high-quality signal transmission. In this paper, we propose and exhibit the effectiveness of a machine learning (ML)-based DPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. The study investigates the use of an optimized recurrent neural network (ORNN) based DPD experimentally on a multiband 5G new radio (NR) A-RoF system while maintaining low complexity. The ORNN model is evaluated using flexible-waveform signals at 2.14 GHz and 5G NR signals at 10 GHz transmitted over a 10 km fiber length. The proposed ORNN-based machine learning approach is optimized and is compared with conventional generalized memory polynomial (GMP) model and canonical piecewise linearization (CPWL) methods in terms of Adjacent Channel Power Ratio (ACPR), Error Vector Magnitude (EVM), and in terms of computation complexity including, storage, time and memory consumption. The findings demonstrate that the proposed ORNN model reduces EVM to below 2% as compared to 12% for non-compensated cases while ACPR is reduced by 18 dBc, meeting 3GPP limits.

Maturity models (MMs) have witnessed exponential increase due to their successful application in several domains. However, there is an absence of review that guides researchers in developing, applying and validating Public-Private Partnership maturity models (PPPMMs). This study examines PPPMMs, provides guidance on the topic and highlights gaps in the literature. A literature search on selected electronic databases was conducted, and the study adopted the widely accepted Preferred Reporting Items for Systematic Review and Meta-analysis statement (PRISMA). The study identified a total of four thousand six hundred and eighteen (4,618) studies, and twenty-one studies (21Nr) were rigorously selected. The results revealed PPPMMs as an emerging area of research with a low number – 21 publications since its deployment for about two (2) decades. Similarly, the findings unveiled a lack of uniformity in conceptualising the terms, dimensions used, and methodology adopted. This finding is attributed mainly to the limited use of the theoretical lens, which considerably weakens the model’s theoretical foundation and limits its potential to guide improvement. Additionally, there are more efforts in developing MMs than applying and validating them. Furthermore, there is an unbalanced focus on descriptive models over prescriptive and comparative models, which inhibit the model’s potential to guide improvement. Future work should provide a solid ground to the field using a theoretical lens and focus on prescriptive models with a strong emphasis on application and validation. This research is the first of its kind that synthesises and brings together available PPPMMs literature into one place. It also contributes to the body of knowledge by highlighting areas of research that require immediate attention to enhance the much-needed success of PPP in the post-COVID-19 era.

In response to the rising demand for new and existing use cases of energy efficiency, the telecoms sector is going through a dramatic shift toward 5G technology. High data speeds, extensive coverage provided by dense base station deployment, higher capacity, improved Quality of Service (QoS), and extremely low latency are required for 5G wireless networks. New deployment methods, networking architectures, processing technologies, and storage solutions must be created to satisfy the anticipated service requirements of 5G technologies. These developments further increase the need to secure the security of 5G systems and their functionality as well as energy efficiency problems. Indeed, 5G system security is the target of intense efforts by developers and academics in this industry. Significant security concerns for 5G networks have been identified with extensive research. Attackers can make use of vulnerabilities like traffic and the flow base by introducing malicious code and performing other nefarious deeds to take advantage of the system. On 5G networks, attack techniques such as model node map (MNmap), power depletion assaults, and Man-in-the-Middle (MiTM) assaults can be effectively used. However, this study analyses 5G technology’s current energy efficiency problems. We recommend an unusual intrusion detection system (IDS) that makes use of Traffic Volume methods. Considering this investigation, we propose an enhancing training process by including statistical analysis on Distributed Denial-of-Service (DDoS) threats, which is how prior research recommended using OMNET and NS-3 on IDS for optimization. Additionally, the methodology for incorporating the suggested intrusion detection systems within a typical 5G architecture is presented by our research using NETSIM. The paper also offers a planned system’s correction method, providing a useful implementation after completing analysis.

The existence of soft soil in offshore areas may lead to the amplification of vibration received from offshore facilities, especially from the existing fixed-jacket platforms, which were designed without provision to seismicity, as in Malaysian water. Therefore, this study was designed to develop a seismic microzonation map and a soil amplification factor map according to soil type; we propose horizontal response spectra and site coefficient values (Ca and Cv) for the Malay Basin. A one-dimensional nonlinear analysis of layered soil (NERA) was used in the ground response analysis for six selected seismic events under five return periods of 100, 200, 500, 1000, and 2500 years. Soil amplification factors for soil types D and E showed a decreasing trend from 100 years to 2500 years. Two designed horizontal response spectra are proposed (for soil type D and E) under average and envelope conditions; a comparison with ISO showed that the proposed spectra were higher, especially for soil type E. To summarize, the seismicity effect should be included in the development of offshore industries as findings indicated that soil amplification occurred in soil types D and E at the Malay Basin.

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered.

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

There has been numerous seismic hazard studies so far that includes Malaysian territories. However, there is a need to assess how reliable those studies are. Two main potential contributors to error have been identified: 1) seismic hazard analysis method and 2) ground motion prediction equation (GMPE). The amount of variation in predicting erroneous GMPE is huge. Thus, this paper concentrates on generating new GMPEs due to subduction specified for Malaysia and validated against developed GMPE. Empirical method for GMPE generation was utilized using recorded ground motion data acquired from the Malaysian Meteorological Department. The earthquakes were grouped according to the source, and only source types in the Sumatran subduction area were used due to the availability of enough data to identify a pattern. Three GMPEs were generated for three different source types, namely, shallow subduction earthquake, deep subduction earthquake, and backarc earthquake sources. Sumatran strike slip fault is considered within backarc seismicity. They were compared with the models proposed by Petersen (modified from Young), Atkinson and Boore, and Megawati. The comparison results showed that the proposed models are far superior at predicting the earthquakes in the Sumatran region, with percentage difference between estimates and the recorded values being the lowest. Therefore, the equations should be used in further seismic hazard analyses. Thus, this paper becomes a part of the recent initiatives in Malaysia to assess the hazards posed by earthquakes.

Hydraulic fracturing has made the production of gas more economical. Shale gas possesses the potential to arise as a main natural gas source worldwide. It has been assessed that the top 42 countries, including the U.S., are predicted to own 7299 trillion cubic feet (tcf) of technically recoverable shale gas resources. The main goal of this paper is to serve as a guide of different shale gas extraction methods. The significance of these methods and possible pros and cons are determined. Each technique was explained with the support of literature review. Specifically, this paper revealed that some fracking methods such as pulsed arc electrohydraulic discharges (PAED), plasma stimulation and fracturing technology (PSF), thermal (cryogenic) fracturing, enhanced bacterial methanogenesis, and heating of rock mass are at the concept stage for conventional and other unconventional resources. Thus, these found to be significant for stimulating natural gas wells, which provides very good production results. This paper also discovered that fracking remains the recommended technique used by the oil and gas industries.

In relation to the use of retrofit materials on damaged constructions, application on earthquake-resistant buildings, and for the strengthening and rehabilitation on weakened regions, there is a need for a more superior material than concrete. Application sites include beam-column joints, corbels, link-slabs, deep beams, support regions and dapped-end areas. Fiber reinforced engineered cementitious composites (FR-ECC) can address this issue, because FR-ECC is one of the composite materials that has high strength, ductility and durability. In order to develop FR-ECC, this study was done to investigate the effect of adding quartz powder on the compressive strength capacity and properties of FR-ECC through the use of polyvinyl alcohol (PVA) and steel fibers. The volume fraction of fiber was set to 0%–2%. To support the friendly environment, FR-ECC uses by-product materials such as fly ash and silica fume, with a cement content less than 600 kg/m3. In terms of the experimental investigation on FR-ECC, this work conducted the fresh property tests showing that PVA fibers have quite an influence on ECC workability, due to their hydrophilic behavior. By adjusting the superplasticizer (SP) content, the consistency and high workability of the ECC mixes have been achieved and maintained. The test results indicated that the PVA and steel fibers-based ECC mixes can be classified as self-compacting composites and high early compressive strength composites. Significantly, addition of quartz powder into the ECC mixes increased the compressive strength ratio of the ECC samples up to 1.0747. Furthermore, the steel fiber-based ECC samples exhibited greater compressive strength than the PVA fibers-based ECC samples with the strength ratio of 1.1760. Due to effect of the pozzolanic reaction, the fibers dispersion and orientation in the fresh ECC mixes, so that the cementitious matrices provided the high strength on the FR-ECC samples. During the compression loading, the bulging effect always occurred before the failures of the fibers-based ECC samples. No spalling occurred at the time of rupture and the collapse occurred slowly. Thus, FR-ECC has provided unique characteristics, which will reduce the high cost of maintenance.