Aminul Islam

The enhanced functionality of the smart grid depends on the robust interconnection between its physical and cyber-layer components. Two distinct categories of control data packets exist within smart grids: fixed-scheduling (FS) and event-driven (ED). An intelligent routing strategy is required to satisfy latency requirements for FS and ED packets across various quality-of-service (QoS) levels and must be resilient to failures. Our proposed software-defined routing strategy balances requirements by dynamically adjusting decisions based on packet types. It prioritizes paths with lower latency and higher throughput for ED packets while prioritizing paths with higher redundancy and lower congestion for FS packets. This strategy switches between proactive and resilient modes based on network conditions. First, the proactive routing module (PRM) utilizes a graph neural network (GNN) and a Q-learning (QL) algorithm to fix sub-optimal routes for efficient packet delivery under normal conditions. Second, the resilient routing module (RRM) combines a deep Q-network with GNN to select optimal routes that remain viable even during failures, ensuring continued operation and robustness. Both modules update the queue service rate (QSR) using QL-agent while avoiding congestion. The GNN ensures proactive module selection based on excessive congestion violations indicating failure conditions. Given the efficient performance of the PRM in normal conditions and the resilience of the RRM under failures, the proposed strategy presents a dual-mode routing that minimizes overhead with a high level of resilience. The proposed approach, evaluated using the IEEE 39-bus test system cyber-layer, effectively ensures desired QoS routing regardless of the conditions of the cyber-layer.

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh.

DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction–modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution. This research aims to design an efficient and high-throughput discriminative intelligent computational system using the natural language processing method “word2vec” and a multi-configured 1D convolution neural network (1D CNN) to predict 4mC sites. In this article, we propose a grid search-based multi-layer dynamic ensemble system (GS-MLDS) that can enhance existing knowledge of each level. Each layer uses a grid search-based weight searching approach to find the optimal accuracy while minimizing computation time and additional layers. We have used eight publicly available benchmark datasets collected from different sources to test the proposed model’s efficiency. Accuracy results in test operations were obtained as follows: 0.978, 0.954, 0.944, 0.961, 0.950, 0.973, 0.948, 0.952, 0.961, and 0.980. The proposed model has also been compared to 16 distinct models, indicating that it can accurately predict 4mC.

In spite of wide use of projection-based features in handwritten character recognition of several languages, its implementation was somewhat scanty in Bangla handwritten character recognition. This paper introduces the usage of projection profile features in recognizing handwritten Bangla basic characters. Alongside it also demonstrates a qualitative and quantitative analysis to visualize the effect of using projection based features on accuracy of recognition of Bangla handwritten characters through a number of approaches. In fact, this particular effort comprises of five different approaches where first one used longest-run, quad-tree and octant centroid features, second one adopted additional shadow features in association with the features of first approach, third one used longest run, quad-tree, shadow and chain code histogram features, next approach used longest-run, quadratic center of mass, shadow and left projection profile features and finally fifth approach with additional right projection profile features along with other features involved in the fourth approach. Throughout this analysis, neural network (trained via back-propagation algorithm) acted as classifier to observe the change in accuracy of recognition. It is seen that, with the increase in number of projection-based features, percentage of accuracy enhances at a greater rate than in case of inclusion of other features. This effective analysis can certainly assist a researcher to choose the optimal feature vector (consisting of several feature sets) for handwritten Bangla basic characters recognition.