Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes’ important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead.

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The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by understanding virus taxonomy. Alignment-based methods are commonly used for this task, but are computationally expensive and time-consuming, especially when dealing with large datasets or when detecting new virus variants is time sensitive. An alternative approach, the encoded method, has been developed that does not require prior sequence alignment and provides faster results. However, each encoded method has its own claimed accuracy. Therefore, careful evaluation and comparison of the performance of different encoded methods are essential to identify the most accurate and reliable approach for virus taxonomy classification. This study aims to address this issue by providing a comprehensive and comparative analysis of the potential of encoded methods for virus classification and phylogenetics. We compared the vectors generated for each encoded method using distance metrics to determine their similarity to alignment-based methods. The results and their validation show that K-Mer followed by CgrDft encoded methods, perform similarly to state-of-the-art multi-sequence alignment methods. This is the first study to incorporate and compare encoded methods that will facilitate future research in making more informed decisions regarding selection of a suitable method for virus taxonomy.