A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Amassing the Security:An Enhanced Authentication and Key Agreement Protocol for Remote Surgery in Healthcare Environment

The development of the Internet of Things has facilitated the rapid development of various industries.With the improvement in people’s living standards,people’s health requirements are steadily improving.However,owing to the scarcity of medical and health care resources in some areas,the demand for remote surgery has gradually increased.In this paper,we investigate remote surgery in the healthcare environment.Surgeons can operate robotic arms to perform remote surgery for patients,which substantially facilitates successful surgeries and saves lives.Recently,Kamil et al.proposed a secure protocol for surgery in the healthcare environment.However,after cryptanalyzing their protocol,we deduced that their protocols are vulnerable to temporary value disclosure and insider attacks.Therefore,we design an improved authentication and key agreement protocol for remote surgeries in the healthcare environment.Accordingly,we adopt the real or random(ROR)model and an automatic verification tool Proverif to verify the security of our protocol.Via security analysis and performance comparison,it is confirmed that our protocol is a relatively secure protocol.

Heterogeneous Fault-Tolerant Aggregate Signcryption with Equality Test for Vehicular Sensor Networks

The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-timedetection and operation control of vehicles and real-time transmission of data and information. In the environmentofVSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users,so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, inthis paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTASET).The scheme combines fault-tolerant and aggregate signcryption,whichnot onlymakes up for the deficiency oflow security of aggregate signature, but alsomakes up for the deficiency that aggregate signcryption cannot tolerateinvalid signature. The scheme supports one verification pass when all signcryptions are valid, and it supportsunbounded aggregation when the total number of signcryptions grows dynamically. In addition, this schemesupports heterogeneous equality test, and realizes the access control of private data in different cryptographicenvironments, so as to achieve flexibility in the application of our scheme and realize the function of quick searchof plaintext or ciphertext. Then, the security of HFTAS-ET is demonstrated by strict theoretical analysis. Finally, weconduct strict and standardized experimental operation and performance evaluation, which shows that the schemehas better performance.

PSAP-WSN:A Provably Secure Authentication Protocol for 5G-Based Wireless Sensor Networks

Nowadays,the widespread application of 5G has promoted rapid development in different areas,particularly in the Internet of Things(IoT),where 5G provides the advantages of higher data transfer rate,lower latency,and widespread connections.Wireless sensor networks(WSNs),which comprise various sensors,are crucial components of IoT.The main functions of WSN include providing users with real-time monitoring information,deploying regional information collection,and synchronizing with the Internet.Security in WSNs is becoming increasingly essential because of the across-the-board nature of wireless technology in many fields.Recently,Yu et al.proposed a user authentication protocol forWSN.However,their design is vulnerable to sensor capture and temporary information disclosure attacks.Thus,in this study,an improved protocol called PSAP-WSNis proposed.The security of PSAP-WSN is demonstrated by employing the ROR model,BAN logic,and ProVerif tool for the analysis.The experimental evaluation shows that our design is more efficient and suitable forWSN environments.

Molecular Basis of Aluminium Toxicity in Plants: A Review

Aluminium toxicity in acid soils having pH below 5.5, affects the production of staple food crops, vegetables and cash crops worldwide. About 50% of the world’s potentially arable lands are acidic. It is trivalent cationic form i.e. Al3+ that limits the plant’s growth. Absorbed Aluminium inhibits root elongation and adversely affects plant growth. Recently researches have been conducted to understand the mechanism of Aluminium toxicity and resistance which is important for stable food production in future. Aluminium resistance depends on the ability of the plant to tolerate Aluminium in symplast or to exclude it to soil. Physiological and molecular basis of Aluminium toxicity and resistance mechanism are important to understand for developing genetically engineered plants for Al toxicity resistance. This paper provides an overview of the state of art in this field.

QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

Intuitionistic Neuro-Fuzzy Optimization in the Management of Medical Diagnosis

Diabetes has become a major concern nowadays and its complications are affecting various organs of a diabetic patient. Therefore, a multi-dimensional technique including all parameters is required to detect the cause, its proper diagnostic procedure and its prevention. In this present work, a technique has been introduced that seeks to build an implementation for the intelligence system based on neural networks. Moreover, it has been described that how the proposed technique can be used to determine the membership together with the non-membership functions in the intuitionistic environment. The dataset has been obtained from Pima Indians Diabetes Database (PIDD). In this work, a complete diagnostic procedure of diabetes has been introduced with seven layered structural frameworks of an Intuitionistic Neuro Sugeno Fuzzy System (INSFS). The first layer is the input, in which six factors have been taken as an input variable. Subsequently, a neural network framework has been developed by constructing IFN for all the six input variables, and then this input has been fuzzified by using triangular intuitionistic fuzzy numbers. In this work, we have introduced a novel optimization technique for the parameters involved in the INSFS. Moreover, an inference system has also been framed for the neural network known as INFS. The results have also been given in the form of tables, which describe each concluding factor.

Fixed Point Approximation for Suzuki Generalized Nonexpansive Mapping Using B(δ, μ) Condition

In this paper, we introduce AK' iteration scheme to approximate fixed point for Suzuki generalized nonexpansive mapping satisfying B(δ, μ) condition in the framework of Banach spaces. Also, an example is given to confirm the efficiency of AK' iteration scheme. Our results are generalizations in the existing literature of fixed points in Banach spaces.

A review of Monogenean diversity in India:Pathogens of fish diseases

The monogeneans parasitizing skin,fins,gills of fishes and are pathogenic to cultivated fish and a few have caused epizootic events.This paper presents a catalogue of known species of the class monogenea from the freshwater fishes of five major river systems of India.Data is gathered from all the published records of monogenean species from India and also from NCBI database for molecular studies,including the fish data gathered from Fishbase.Approximately 50 families of freshwater fishes have been reported from five Indian major river systems,having 159 nominal genera.As far as freshwater monogeneans are concerned about,14 families having 44 genera and 208 species have been reported.In all,the present study takes a broad look at monogenean diversity in the freshwater fishes of India.The available information indicates the rich diversity of these parasites in India for that an integrated approach is necessary which should start with morphological characterization followed by molecular characterization of monogenean parasites in all river system of India.So that subsequent comparison of monogenean fauna present in different river systems of India can be made.