One of the major concerns in wireless sensor networks (WSNs) is most of the sensor nodes are powered through limited lifetime of energy-constrained batteries, which majorly affects the performance, quality, and lifetime of the network. Therefore, diverse clustering methods are proposed to improve energy efficiency of the WSNs. In the meantime, fifth-generation (5G) communications require that several Internet of Things (IoT) applications need to adopt the use of multiple-input multiple-output (MIMO) antenna systems to provide an improved capacity over multi-path channel environment. In this paper, we study a clustering technique for MIMO-based IoT communication systems to achieve energy efficiency. In particular, a novel MIMO-based energy-efficient unequal hybrid clustering (MIMO-HC) protocol is proposed for applications on the IoT in the 5G environment and beyond. Experimental analysis is conducted to assess the effectiveness of the suggested MIMO-HC protocol and compared with existing state-of-the-art research. The proposed MIMO-HC scheme achieves less energy consumption and better network lifetime compared to existing techniques. Specifically, the proposed MIMO-HC improves the network lifetime by approximately 3× as long as the first node and the final node dies as compared with the existing protocol. Moreover, the energy that cluster heads consume on the proposed MIMO-HC is 40% less than that expended in the existing protocol.

The recent advances in sensing and communication technologies such as wireless sensor networks (WSN) have enabled low-priced distributed monitoring systems that are the foundation of smart cities. These advances are also helping to monitor smart cities and making our living environments workable. However, sensor nodes are constrained in energy supply if they have no constant power supply. Moreover, communication links can be easily failed because of unequal node energy depletion. The energy constraints and link failures affect the performance and quality of the sensor network. Therefore, designing a routing protocol that minimizes energy consumption and maximizes the network lifetime should be considered in the design of the routing protocol for WSN. In this paper, we propose an Energy-Efficient Unequal Chain Length Clustering (EEUCLC) protocol which has a suboptimal multihop routing algorithm to reduce the burden on the cluster head and a probability-based cluster head selection algorithm to prolong the network lifetime. Simulation results show that the EEUCLC mechanism enhanced the energy balance and prolonged the network lifetime compared to other related protocols.

The understanding of DNA damage intensityconcentration-level is critical for biological and biomedical research, such as cellular homeostasis, tumor suppression, immunity, and gametogenesis. Therefore, recognizing and quantifying DNA damage intensity levels is a substantial issue, which requires further robust and effective approaches. DNA damage has several intensity levels. These levels of DNA damage in malignant cells and in other unhealthy cells are significant in the assessment of lesion stages located in normal cells. There is a need to get more insight from the available biological data to predict, explore and classify DNA damage intensity levels. Herein, the development process relied on the available biological dataset related to DNA damage signaling pathways, which plays a crucial role in DNA damage in the mammalian cell system. The biological dataset that was used in the proposed model consists of 15000 records intensity -concentration-level for a set of five proteins which regulate DNA damage. This research paper proposes an innovative deep learning model, which consists of an attention-based long short term-memory (AT-LSTM) model for DNA damage multi class predictions. The proposed model splits the prediction procedure into dual stages. For the first stage, we adopt the related feature sequences which are inserted as input to the LSTM neural network. In the next stage, the attention feature is applied efficiently to adopt the related feature sequences which are inserted as input to the softmax layer for prediction in the following frame. Our developed framework not only solves the long-term dependence problem of prediction effectively, but also enhances the interpretability of the prediction methods that was established on the neural network. We conducted a novel proposed model on big and complex biological datasets to perform prediction and multi classification tasks. Indeed, the (AT-LSTM) model has the ability to predict and classify the DNA damage in several classes: No-Damage, Low-damage, Medium-damage, High-damage, and Excessdamage. The experimental results show that our framework for DNA damage intensity level can be considered as state of the art for the biological DNA damage prediction domain.

No abstract

Organism network systems provide a biological data with high complex level. Besides, these data reflect the complex activities in organisms that identifies nonlinear behavior as well. Hence, mathematical modelling methods such as Ordinary Differential Equations model (ODE's) are becoming significant tools to predict, and expose implied knowledge and data. Unfortunately, the aforementioned approaches face some of cons such as the scarcity and the vagueness in the biological knowledge to expect the protein concentrations measurements. So, the main object of this research presents a computational model such as a neural Feed Forward Network model using Back Propagation algorithm to engage with imprecise and missing biological knowledge to provide more insight about biological systems in organisms. Therefore, the model predicts protein concentration and illustrates the nonlinear behavior for the biological dynamic behavior in precise form. Also, the desired results are matched with recent ODE's model and it provides precise results in simpler form than ODEs.