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A radio communication sensor system is a collection of sensor modules that are connected to one another through wireless communication. It is common for them to be battery-powered and responsive to a nearby controller, referred to as the base station. They are capable of doing basic computations and transferring information to the base station in most scenarios. They are also in charge of transporting data from distant nodes, putting a burden on nodes with limited resources, and contributing to the quick depletion of energy in these nodes in the process. Nodes in close proximity to the base station are responsible for more than only detecting and sending data to the base station; they are also responsible for transmitting data from faraway nodes. To reward nodes that perform well, a protocol known as the Improved Fuzzy Inspired Energy Effective Protocol (IFIEEP) employs three separate sorts of nodes in order to provide more energy to those who do not. It takes into account the remaining node energy, the node's proximity to the base station, the node's neighbor concentration, and the node's centrality in a cluster when determining node viability. All of these assumptions are founded on a shaky understanding of the situation. Adaptive clustering must be applied to the most viable nodes in order to identify cluster leaders and transmit data to the base station, in addition to disseminating data across the rest of the network, in order to achieve success. In addition, the research provides proper heterogeneity parameters, which describe, among other things, the number of nodes as well as the starting energy of each node. The percentage gain in-network lifetime when compared to current approaches is minor for smaller numbers of supernodes; however, the percentage gain in the area covered 12.89 percent and 100% when more significant numbers of super nodes are used. These improvements in stability, residual energy, and throughput are accomplished by combining these improvements while also taking into consideration the previously neglected energy-intensive sensing energy aspect. The protocol that has been presented is meant to be used in conjunction with applications that make use of blockchain technology., Competing Interests: All authors declare that they do not have any conflicts of interest., (Copyright © 2022 Randeep Singh et al.)

In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutional neural networks for semantic segmentation. A majority of recent techniques have used maxpooling to increase the receptive field of a network at an expense of decreasing its spatial resolution. Although this idea has shown improved results in object detection applications, however, when it comes to semantic segmentation, a high spatial resolution also needs to be considered. To address this issue, a new deep learning model, the M-Net is proposed in this paper which satisfies both high spatial resolution and a large enough receptive field while keeping the size of the model to a minimum. The proposed network is based on an encoder-decoder architecture. The encoder uses atrous convolution to encode the features at full resolution, and instead of using heavy transposed convolution, the decoder consists of a multipath feature extraction module that can extract multiscale context information from the encoded features. The experimental results reported in the paper demonstrate the viability of the proposed scheme. [ABSTRACT FROM AUTHOR]

The minimal-degree distributed denial-of-service attack takes advantage of flaws in the adaptive mechanisms of network protocols, which could have a big impact on network service quality. It is very hard to find, has a low attack rate, and comes at a set time. Detection methods that have been used before have problems because they only use one type of detection and are not very good at identifying the object. In the end, a way to detect many sorts of minimal DDoS assaults that use deep hybrid learning is suggested. To construct multi-type limited DDoS threat data sets and mimic diverse sorts DDoS assaults and legitimate traffic in varying situations in the 5G setting, collect congestion at the networking entry and extract flow feature info are considered. From a statistical threshold and feature engineering point of view, these data sets show how many sorts of minimal DDoS assaults are there. This study aims to develop a deep hybrid learning-based multi-type low-rate DDoS attack detection solution for 5G networks which is the novel model that is recently deployed, and a hybrid deep learning algorithm was used to train the algorithm offline, and the algorithm's performance was compared to that of the LSTM-Light GBM and LSTM-RF algorithms. The CNN-RF revealing model was then used to detect minimal DDoS assaults at the gateway, so that multiple attacks could be detected at the same time. It can identify 4 sorts of low-rate DDoS assaults like Slow-Headers, Slow-Body, Slow-Read, and Shrew assaults, in a 120-second window. The false intercept rate is 11.03 percent. This means that 96.22 percent of traffic could be found. Using the strategy suggested can help cut down on the traffic concentration of minimal DDoS attacks at the net ingress. It can also be used in real-world situations. [ABSTRACT FROM AUTHOR]

With an exponential increase in consumers' need for electronic products, the world is facing an ever-increasing economic and environmental threat of electronic waste (e-waste). To minimize their adverse effects, e-waste recycling is one of the pivotal factors that can help in minimizing the environmental pollution andto increase recovery of valuable materials. For instance, Printed Circuit Boards (PCBs), while they have several valuable elements, they are hazardous too; and therefore, they form a large chunk of e-waste being generated today. Thus, in recycling PCBs, Electronic Components (ECs) are segregated at first, and separately processed for recovering key elements that could be re-used. However, in the current recycling process, especially in developing nations, humans manually screen ECs, which goes on to affect their health. It also causes losses of valuable materials. Therefore, automated solutions need to be adopted for both to classify and to segregate ECs from waste PCBs. The study proposes a robust EC identification system based on computer vision and deep learning algorithms (YOLOv3) to automate sorting process which would help in further processing. The study uses a publicly available dataset, and a PCB dataset which reflect challenging recycling environments like lighting conditions, cast shadows, orientations, viewpoints, and different cameras/resolutions. The outcome of YOLOv3 detection model based on training of both datasets presents satisfactory classification accuracy and capability of real-time competent identification, which in turn, could help in automatically segregating ECs, while leading towards effective e-waste recycling. • Methodology to automatically identify and classify electronic components from Waste Printed Circuits Boards. • Use of Computer Vision algorithm to detect Integrated Circuits in real-time. • Comparative performance analysis of the trained model using two distinct datasets. • Robust model with consistent performance regarding the recycling conditions. [ABSTRACT FROM AUTHOR]

DNA-binding proteins (DBPs) are highly concerned with several types of cancers (lung, breast, and liver), other fatal diseases (AIDS/HIV, asthma), and are used in the designing of drug. A series of predictors were constructed for identification of DBPs. However, a more accurate computational predictor is still essential for further performance improvement. In this work, a deep learning-based predictor (DBP-DeepCNN) is proposed for improving DBPs prediction. The salient features are derived by a novel method, namely, R-PSSM-DWT (Reduced position-specific scoring matrix-discrete wavelet transform) as well as Lead-BiPSSM (Lead-bigram-position specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), ED-PSSM (Evolutionary difference position specific scoring matrix), and F-PSSM (Filtered position specific scoring matrix). Further, the models are trained with 2D CNN (two-dimensional convolutional neural network), XGB (eXtreme gradient boosting), Adaboost, and ERT (extremely randomized trees). 2D CNN-based model with R-PSSM-DWT produced 6.92% and 1.32% higher accuracies than existing approach on training and independent datasets, respectively. These outcomes verified the superlative success rate of our novel predictor over the existing studies. In addition to being a promising method for large scale prediction of DBPs. DBP-DeepCNN would be fruitful for establishing more promising therapeutic strategies for chronic disease treatment. • Developed a novel deep learning based predictor namely DBP-DeepCNN for accurate prediction of DBPs. • Introduced a novel feature descriptor named R-PSSM-DWT for extraction of discriminative features. • 2D CNN, XGB, ERT, and Adaboost are used as classification algorithms. • Our novel feature encoder R-PSSM-DWT with 2D CNN achieved the best performance. [ABSTRACT FROM AUTHOR]

• Designed a novel predictor named Deep-GHBP for prediction of Growth Hormone-binding Proteins. • The local and global discriminative features are explored by Position Specific Scoring Matrix (PSSM), Composition Transition Distribution (CTD), Geary, Pseudo Amino Acid Composition (PseAAC), and Moran features. • The best features are selected by a novel Multi-Model Consensus (MMC) feature selection algorithm. • Deep Neural Network (DNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) used as classification algorithms. • Deep-GHBP has secured the highest prediction results for GHBP. Growth hormone-binding proteins (GHBPs) are carrier proteins that interact with other growth hormone proteins in a selective and non-covalent fashion. GHBPs perform significant roles in various biological activities including cell growth, granular cellular mechanism, and therapeutic approaches. Considering these crucial functions, an accurate prediction of GHBPs is indispensable. In this connection, wet-laboratory and machine learning (ML) models have been developed. However, these methods have a limited amount of performance, including less informative features or inefficient learning models. This study presents an innovative approach by employing a Position Specific Scoring Matrix (PSSM), Composition Transition Distribution (CTD), Geary, Moran, and Pseudo Amino Acid Composition (PseAAC). The important features of these were selected by a novel Multi-Model Consensus (MMC) feature selection algorithm. The most appropriate feature set was then provided to four classifiers: Deep Neural Network (DNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The DNN on the optimal feature set achieved the highest accuracies of 88.09% and 83.33% on the training and testing datasets, respectively. The achieved results were also superior to existing predictors for the identification of GHBPs. Thus, GHBP-Pred will be significantly helpful for large scale prediction of GHBPs with high precision. [ABSTRACT FROM AUTHOR]

• IoT and deep learning based system for detection, dissemination and monitoring of farm fires • Fusing MobilenetV2 detection, sensory detection and NASA FIRMS provides better results. • Use of fuzzy logic provides real time and accurate location of active fire. • System removes false cases and extracts actual location with land owner details. This paper proposes an Internet of Things (IoT) and deep learning-inspired multi-model system for detection, dissemination, and monitoring of Active Fire Locations(AFL) in agricultural activities. The IoT module of the proposed system works on the fusion of IoT sensors-based detectors and deep learning-based detectors. Fuzzy logic is used for the fusion of various sensors and providing real-time detection and location of AFL. The deep learning detector implements IP camera-based MobilenetV2 architecture for accurate and long-distance detections trained on a novel self-created dataset. The proposed framework also provides a software module for monitoring and tracking of various AFL. The software comes with several features like automatic extraction of fire locations from remote sensing sites, assigning active fire locations to multiple stakeholders, extracting farmers' names indulged in burning, automatic sending a notification to government agencies, and provisions for citizens centric participation. The results of the proposed framework are quite encouraging. [Display omitted] [ABSTRACT FROM AUTHOR]