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Tea leaves’ diseases caused by constant exposure to pathogens lead to significant crop yield loss globally. Diagnosing the tea leave disease at an early stage minimizes the tea yield loss. In this study, a novel approach is presented for automatically detecting tea leaves diseases based on image processing technology. The Non-dominated Sorting Genetic Algorithm (NSGA-II) based image clustering is proposed for detecting the disease area in tea leaves. After that, PCA and multi-class SVM is used for feature reduction and identifying the disease in the tea leaves, respectively. The result shows that the proposed algorithm can detect the type of disease persisting in tea leaves with an average accuracy of 83%. Five different tea leaf diseases are considered here, such as Red Rust, Red Spider, Thrips, Helopeltis, and Sunlight Scorching.

Early prediction of a forest fire is one of the critical research challenges of the wireless sensor network (WSN) to save our ecosystem. In WSN based forest fire detection system, sensor nodes are deployed in the remote forest area for transmitting the sensed data to the base station, which is accessible by the forest department. Though sensor nodes in the forest are localized through GPS connection, the high deployment cost for it motivates the authors of this paper to design a novel localization technique applying the Support Vector Machine. Forest fire prediction in an energy efficient way is another concern of this paper. The semi-supervised classification model is proposed to address this problem by dividing the forest area into different zones [High Active (HA), Medium Active (MA), and Low Active (LA)]. It is designed in such a way that it can be able to predict the state of the (HA, MA, LA) fire zone with 90% accuracy when only one parameter is sensed by sensor nodes due to energy constraints. The greedy forwarding technique is used to transmit the packets from the HA zone to the base station continuously, and the MA zone transmits packets periodically, whereas, LA zone avoids transmitting the sensed data to the base station. This technique of data forwarding enhances network lifetime and reduces congestion during data transmission from the forest area to the base station.Graphic abstract

Forest fires, by disrupting the ecological equilibrium and exacerbating global warming, pose a threat to both wildlife and the overall environmental stability. To safeguard our ecosystems, it is imperative to predict and detect forest fires at an early stage. Wireless Sensor Networks (WSNs) have gained popularity due to their cost-effectiveness, low power consumption, and portability in achieving this goal. This research introduces an innovative method based on mid-point K-means clustering to forecast three forest activity zones: high-active (fire-prone), medium-active, and low-active zones. This system excels in identifying high-active zones with remarkable accuracy (98%). The sensor node at the high-active zone’s center continuously transmits data to the Base Station (BS), promptly notifying the relevant authorities of potential forest fires. In contrast, the medium-active zone’s sensor node periodically shares environmental data, while the low-active zone’s node conserves energy by not transmitting data to the BS, thereby enhancing network longevity and energy efficiency.

Electronic characterization of bio-molecular nanoscale devices is important in the new edge of nanotechnology and the field of nanoelectronics. In this new era, the molecular transmission properties of the Adenine bio-molecular optical switch are predicted using Density Functional Theory along with Non-Equilibrium Green's Function-based First Principle approach and this phenomenon is supported with Machine Learning based algorithms. The photo-induced switching characteristics are observed for the bi-directional bio-molecular switch for both forward and reverse bias conditions. This switching behaviour converts the position of the bio-molecular switch to its straightened and 60° twisted form. The electrical doping process plays an important role in generating p and n regions at the two ends of the switch. In this experiment, gold electrodes are used as the supporting anchor of the Adenine molecules. The HOMO–LUMO gap and I–V characteristics of the bio-molecular switch are analyzed using first principle formalisms and compared with the Machine learning approach. This ML approach helps design the future-generation prediction model for the nano-scale electronic device simulation process. This prediction model can be obtained without knowing the switching characteristics and also without knowing all-time sequence generating waveforms but only observing the waveform prediction for up to a certain period sequence.

SigSense, a mobile crowdsensing-based geospatial video game, has been proposed to survey live signal strength using smartphones.It provides attractive incentives to the contributers. Live data collected as a survey through this game is used to recommend locations for installing the base stations to improve the signal quality using the Greedy Algorithm. A large plot of land is considered a large matrix. We have recursively divided the land into smaller submatrices. Then signal strength survey of each submatrix is performed through Mobile Crowd Sensing using SigSense. The recommendation system advices the locations for installing the network base stations for improving signal strength. An incentive is provided to a player based on the game's rules, making it a win–win situation for both the player and the network service provider. The unique feature of this game is that it can be played even in an area where is low mobile network coverage. A player’s details are hidden from other players through unique masked ids and mixed reality.

These days, 5G wireless communication are being created for different modern IoT (Internet of Things) applications around the world, arising with the IoT. All things considered, it is feasible to send energy efficient innovation in a manner that advances the drawn out sustainability of networks. Next-generation heterogeneous wireless communication is composed of different base stations. In this network, sustainable spectrum allocation is required to maximize the bandwidth utilization along with a reduction in power consumption. This paper proposes an algorithm for allocating an optimized spectrum to clusters in a multi-cluster environment for sustainable 5G environment using particle swarm optimization (PSO). The proposed strategy is applicable for 3G, 4G, and 5G mobile networks. Mobile devices enter and leave the cluster randomly and stay within the cluster for an uncertain amount of time. During that period the user demands may vary. Consequently, various bandwidth allocations are required. For such cases, static allocation might result in inefficient utilization of bandwidth, wastage of power, and degrade user satisfaction. The proposed algorithm will optimize the spectrum allocated to a cluster from time to time to solve this problem and produce an optimized solution within a given deadline. PSO based proposed scalable spectrum allocation method is applicable for the different frequency range for each cluster, hence scalable from 3G telecommunication to 5G-mobile edge technology. The convergence of the strategy is analyzed. From simulation analysis, it is observed that the proposed strategy reduces power consumption by approximately 8%, 11%, and 6% in 3G, 4G, and 5G communications respectively than the existing scheme.

Quantum machine learning (QML) provides the opportunity for the success of an automated medical diagnostic system due to the effective representation of the solution space by quantum entangled states and faster optimisation through quantum superposition. Preserving medical data privacy is crucial while implementing an intelligent and efficient medical service provider system. The federated machine learning model is not only rich in diversified model experiences but also helps to protect patient data privacy. This paper proposes a secure, intelligent Internet of Healthcare Things (IoHT) application with a federated quantum convolutional neural network (FedQCNN) to classify medically significant retinal image patches. Following the gradient descent algorithm, we executed the model optimisation and performed the global aggregation using the weighted average of the local models' parameters. The proposed system achieves an evaluation accuracy of 96.8% on E‐Ophtha retinal image dataset. We suggest over‐the‐air distributed machine learning with wireless multiple access channels to significantly save the scaled‐up radio resource requirements for massive connectivity to ultra dense IoT devices. The research emphasises the significant progress of federated quantum machine learning and its prospects in the coming decades. Quantum machine learning (QML) provides the opportunity for the success of an automated medical diagnostic system due to the effective representation of the solution space by quantum entangled states and faster optimisation through quantum superposition. Preserving medical data privacy is crucial while implementing an intelligent and efficient medical service provider system. The federated machine learning model is not only rich in diversified model experiences but also helps to protect patient data privacy. This paper proposes a secure, intelligent Internet of Healthcare Things (IoHT) application with a federated quantum convolutional neural network (FedQCNN) to classify medically significant retinal image patches.

The rapid escalation of user traffic and service innovation has made the deployment of small cell base stations essential for eventually decreasing energy consumption in future generation wireless network. An energy efficient small cell zooming strategy is proposed using weighted majority cooperative game for two-tier fifth generation (5G) mobile network. The proposed strategy is referred as ‘5G-ZOOM-Game’. Small cells ‘zoom in’ and ‘zoom out’ dynamically according to the proposed ‘5G-ZOOM-Game’ algorithm. Different frequency sets are assigned to small cells based on adjacency for reducing interference. In the proposed approach femtocells are used as small cells. The proposed algorithm is applied between two adjacent femtocells. Out of two adjacent femtocells, higher majority femtocell is selected based on weighted majority game; this femtocell zooms its coverage area. The utility function of the proposed approach is defined to connect maximum possible number of mobile devices by increasing the higher majority femtocell’s coverage area. Higher majority femtocell is chosen based on the load and minimum distance between mobile device and femtocell base station. Proposed 5G-ZOOM-Game network reduces ~ 35% of power consumption and increases signal-to-interference-plus-noise-ratio (SINR) and spectral efficiency by ~ 30% and ~ 60% respectively than the existing approaches.

Doping is the key feature in semiconductor device fabrication. Many strategies have been discovered for controlling doping in the area of semiconductor physics during the past few decades. Electrical doping is a promising strategy that is used for effective tuning of the charge populations, electronic properties, and transmission properties. This doping process reduces the risk of high temperature, contamination of foreign particles. Significant experimental and theoretical efforts are demonstrated to study the characteristics of electrical doping during the past few decades. In this article, we first briefly review the historical roadmap of electrical doping. Secondly, we will discuss electrical doping at the molecular level. Thus, we will review some experimental works at the molecular level along with we review a variety of research works that are performed based on electrical doping. Then we figure out importance of electrical doping and its importance. Furthermore, we describe the methods of electrical doping. Finally, we conclude with a brief comparative study between electrical and conventional doping methods.