In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal.

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.

Diabetes is one of the most common metabolic diseases that cause high blood sugar. Early diagnosis of such a condition is challenging due to its complex interdependence on various factors. There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. The study consisted of two fundamental parts. Firstly, the study investigated handling missing data adopting data imputation, namely, median value imputation, K‐nearest neighbor imputation, and iterative imputation. Consequently, the study validated the implications of these imputations using various classification algorithms, i.e., linear, tree‐based, and ensemble algorithms, to see how each method affected classification accuracy. Secondly, Artificial Neural Network was employed to model the best performing imputed data, balanced with SMOTETomek ensuring each class is represented fairly. This approach provided the best accuracy of 98% on the test data, outperforming accuracies achieved in prior studies using the same dataset. The dataset used in this study is concerned with gender and population. As a prospect, the study recommends adopting a larger population sample without geographic boundaries. Additionally, as the developed Artificial Neural Network model did not undergo any specific hyperparameter tuning, it would be interesting to explore tuning on top of normalized data to optimize accuracy further.

Conventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. Semantic approaches for QE overcome this limitation, whereby a search query is expanded with meaningful terms that accord with user information needs. This article surveys recent approaches to semantic QE that employ different models and strategies and leverages various knowledge structures. We organize these approaches into a taxonomy that includes linguistic methods, ontology-based methods and mixed-mode methods. We also discuss the strengths and limitations of each type of semantic QE method. In addition, we evaluate various semantic QE approaches in terms of knowledge structure utilization, corpus collection, baseline model adaptation and retrieval performance. Finally, future directions in exploiting personalized social information and multiple ontologies for semantic QE are suggested.

Distributed Denial of Service (DDoS) attack is known to be one of the most lethal attacks in traditional network architecture. In this attack, the attacker uses botnets to overwhelm network resources. Botnets can be randomly compromised computers or IoT devices that are used to generate excessive traffic towards the victim, and as a result, legitimate users cannot access the services. In this research, software-defined networking (SDN) has been suggested as a solution to fight DDoS attacks. SDN uses the idea of centralized control and segregation of the data plane from the control plane. SDN is more flexible, and policy implementation on the centralized controller is easy. SDN is now being widely used in modern network paradigms because it has enhanced security. In this work, an entropy-based statistical approach has been suggested to detect and mitigate TCP SYN flood DDoS attacks. The proposed algorithm uses a three-phased detection scheme to minimize the false-positive rate. Entropy, standard deviation, and weighted moving average have been used for intrusion detection. Multiple experiments were performed, and the results show that the suggested approach is more reliable and lightweight and has a minimal false-positive rate.

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions.