The idea of the IOT started way back in 1982 when a bending machine is connected to the internet, then to the concept of Mark Weiser in 1992, then RFID, and so on. A detailed evolution of the IOT and how it was transforming is given. Due to the wide embracement of IOT by industries and home users, it has become the cornerstone of the Information and Communication Technology (ICT) market. The market value of the IOT was worth $1.90 billion in 2018; $25 billion in 2020; $925.2 billion as at 2023; while it is forecasted to hit $6 trillion in 2025 at 15.12% growing rate [1]. This work presented some major technologies integrated with the IOT to achieve a certain goal and improve an existing system, the capability of IOT to create smart applications is demonstrated and some major distinctions between IOT & the Internet were highlighted.

Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent itemsets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent itemsets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent itemsets within a shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed.

Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.

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.