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With economies and business becoming knowledge intensive, the role of education industry has immensely risen in significance. This has given rise to a highly competitive landscape in higher education, with faculty retention as a major challenge for the institutions leading to negative consequences in terms of education quality, stakeholder satisfaction and rankings. This paper discusses the factors influencing faculty retention in HEIs in India within the framework of Social Exchange Theory. The sub dimensions of HR climate were explored using Principal Components Analysis. The relative importance of sub dimensions in forming the overall climate was estimated and the effect of HR climate on faculty retention was studied with Organizational Commitment and Organizational Trust as mediators employing a hierarchical components model in the PLSSEM analysis. The results indicate that HR climate has a significant positive effect on faculty retention through organizational commitment and organizational trust as partial complementary mediators. Findings have significant theoretical as well as practical implications in extending the present knowledge regarding the subdimensions and effect of HR climate on faculty retention and the suggested measures to be taken in implementing effective HR Management systems in the Higher Education Institutions. [ABSTRACT FROM AUTHOR]

The fabrication of a Poly (vinylidene fluoride) membrane (PVDF) and ceramic-assisted bismuth vanadate-polyvinylidene fluoride (BiVO4-PVDF) composite membrane was achieved through the utilization of the electrospinning technique. The composition and structure of the fabricated membranes were characterized by X-ray powder diffraction, Raman analysis, scanning electron microscopy, Thermo gravimetric analyzer, Fourier transform infrared spectroscopy, and UV–Vis spectroscopy techniques. The prepared polymeric membranes were then utilized for catalytic investigation and to explore, how structure affects catalytic activity using 5 mg/L, 10 mL methylene blue (MB) dye solution. Ultrasonication, visible light irradiation, and the combination were used to study piezocatalysis, photocatalysis, and piezo-photocatalysis, moreover, degradation intermediates were also explored using scavengers. Electrospun BiVO4-PVDF (BV-PVDF) composite has been found to have better piezocatalytic and photocatalytic properties than PVDF. The experimental findings reveal that the composite of BiVO4-PVDF demonstrates the highest efficiency in dye degradation, achieving a maximum degradation rate of 61% within a processing time of 180 min. The rate of degradation was calculated to be 0.0047 min−1, indicating a promising potential for the composite in the field of dye degradation. [ABSTRACT FROM AUTHOR]

Plant diseases are a significant threat to global food security since they directly affect the quality of crops, leading to a decline in agricultural productivity. Several researchers have employed crop-specific deep learning models based on convolutional neural networks (CNN) to identify plant diseases with better accuracy and faster implementation. However, the use of crop-specific models is unreasonable considering the resource-constrained devices and digital literacy rate of farmers. This work proposes a single light-weight CNN model for disease identification in three major crops, namely, Corn, Rice, and Wheat. The proposed model uses convolution layers of variable sizes at the same level to accurately detect the diseases with various sizes of the infected area. The experimentation results reveal that the proposed model outperforms several benchmark CNN models, namely, VGG16, VGG19, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet201, InceptionV3, and Xception, to achieve an accuracy of 84.4% while using just 387,340 parameters. Moreover, the proposed model validates its efficacy as a multi-functional tool by classifying healthy and infected categories of each crop individually, obtaining accuracies of 99.74%, 82.67%, and 97.5% for Corn, Rice, and Wheat, respectively. The better performance values and light-weight nature of the proposed model make it a viable choice for real-time crop disease detection, even in resource-constrained environments. [ABSTRACT FROM AUTHOR]

WTe2 is one of the wonder layered materials, displays interesting overlapping of electron–hole pairs, opening of the surface bandgap, anisotropy in its crystal structure and very much sought appealing material for room temperature broadband photodection applications. Here we report the photoresponse of WTe2 thin films and microchannel devices fabricated on silicon nitride substrates. A clear sharp rise in photocurrent observed under the illumination of visible (532 nm) and NIR wavelengths (1064 nm). The observed phoresponse is very convincing and repetitive for ON /OFF cycles of laser light illumination. The channel length dependence of photocurrent is noticed for few hundred nanometers to micrometers. The photocurrent, rise & decay times, responsivity and detectivity are studied using different channel lengths. Strikingly microchannel gives few orders of greater responsivity compared to larger active area investigated here. The responsivity and detectivity are observed as large as 29 A/W and 3.6 × 108 Jones respectively. The high performing photodetection properties indicate that WTe2 can be used as a broad band material for future optoelectronic applications. [ABSTRACT FROM AUTHOR]

It is vital to develop an appropriate prediction model and link carefully to measurable events such as clinical parameters and patient outcomes to analyze the severity of the disease. Timely identifying retinal diseases is becoming more vital to prevent blindness among young and adults. Investigation of blood vessels delivers preliminary information on the existence and treatment of glaucoma, retinopathy, and so on. During the analysis of diabetic retinopathy, one of the essential steps is to extract the retinal blood vessel accurately. This study presents an improved Gabor filter through various enhancement approaches. The degraded images with the enhancement of certain features can simplify image interpretation both for a human observer and for machine recognition. Thus, in this work, few enhancement approaches such as Gamma corrected adaptively with distributed weight (GCADW), joint equalization of histogram (JEH), homomorphic filter, unsharp masking filter, adaptive unsharp masking filter, and particle swarm optimization (PSO) based unsharp masking filter are taken into consideration. In this paper, an effort has been made to improve the performance of the Gabor filter by combining it with different enhancement methods and to enhance the detection of blood vessels. The performance of all the suggested approaches is assessed on publicly available databases such as DRIVE and CHASE_DB1. The results of all the integrated enhanced techniques are analyzed, discussed, and compared. The best result is delivered by PSO unsharp masking filter combined with the Gabor filter with an accuracy of 0.9593 for the DRIVE database and 0.9685 for the CHASE_DB1 database. The results illustrate the robustness of the recommended model in automatic blood vessel segmentation that makes it possible to be a clinical support decision tool in diabetic retinopathy diagnosis. [ABSTRACT FROM AUTHOR]

Improvement in the specific capacity of the spinel structures (AxB3−xO4) plays a vital role for creating energy storage devices with excellent electrochemical performance. Here, we synthesized spinel structured CoB2O4 (B = Co, Fe) electrode materials using a simple hydrothermal method. The X-ray Diffraction (XRD) analysis confirms the phase purity and crystallinity of the synthesized materials while morphology, surface area, pore radius, oxidation states and chemical bonding is confirmed by Field Emission Scanning Electron Microscopy (FESEM), Brunauer–Emmett–Teller (BET), X-ray Photoelectron Spectroscopy (XPS) and Fourier Transform Infrared Spectroscopy (FTIR) techniques. Williamson–Hall (W–H) plots and Rietveld refinement are used to find the average crystallite size, strain, and lattice parameter of Co3O4 and CoFe2O4 compounds. The CV analysis confirms a high specific capacity of 1039 F g−1 (415 C g−1) for Co3O4 as compared to 527 F g−1 (169 C g−1) for CoFe2O4 at 2 mV s−1. The dominance of the diffusion process (b = 0.59) and quantitative separation of faradaic and capacitive contribution at different scan rates are calculated for the Co3O4 sample. The Galvanic Charge–Discharge (GCD) analysis confirms a specific capacity of 663 F g−1 (265 C g−1) for Co3O4, which is almost 1.2 times higher than to CoFe2O4. The 86% capacity retention over 5000 GCD cycles and small value of charge transfer resistance (Rct = 45 Ω) for Co3O4 confirm its superiority and potential to be used as a positive electrode material for practical energy storage devices. [ABSTRACT FROM AUTHOR]

With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the process of text classification. We believe that readers would be able to find scope for further improvements in the area of text classification or to propose new techniques of text classification applicable in any domain of their interest. [ABSTRACT FROM AUTHOR]

Software effort estimation is a significant part of software development and project management. The accuracy of effort estimation and scheduling results determines whether a project succeeds or fails. Many studies have focused on improving the accuracy of predicted results, yet accurate estimation of effort has proven to be a challenging task for researchers and practitioners, particularly when it comes to projects that use agile approaches. This work investigates the application of the adaptive neuro-fuzzy inference system (ANFIS) along with the novel Energy-Efficient BAT (EEBAT) technique for effort prediction in the Scrum environment. The proposed ANFIS-EEBAT approach is evaluated using real agile datasets. It provides the best results in all the evaluation criteria used. The proposed approach is also statistically validated using nonparametric tests, and it is found that ANFIS-EEBAT worked best as compared to various state-of-the-art meta-heuristic and machine learning (ML) algorithms such as fireworks, ant lion optimizer (ALO), bat, particle swarm optimization (PSO), and genetic algorithm (GA). [ABSTRACT FROM AUTHOR]

[Display omitted] • The piezoelectric properties of the PVDF has been improved by incorporating Bi 2 ZnB 2 O 7 via electrospinning method. • Electrospun nanofibers exhibit high mechanical strength and a favourable nanoconfinement effect on interfaces, which promotes the synergistic piezo-phototronic effect in piezoelectric heterostructures. • Achieved 74% degradation with a kinetic rate of 7.3 × 10−3 min−1 of methylene blue under combined effect of visible light irradiation and ultrasonication. • The mechanism underlying piezo-photocatalytic dye degradation has been explained with scavenger test. To enhance the piezoelectric capabilities of the β-phase of poly(vinylidene fluoride), a new composite membrane was prepared via electrospinning. This consist of Bi 2 ZnB 2 O 7 and poly(vinylidene fluoride). A comparison of electrospun and composite fibers showed a significant increase in β-phase content. XRD and FTIR were used to characterize the samples' structures. FE-SEM and HR-TEM were also used to examine sample morphology. The optical properties of the BBZO-PVDF membrane were further assessed using UV–visible diffuse reflectance spectroscopy. The composite membrane's effectiveness was tested using photocatalysis, piezocatalysis, and photo-piezocatalysis. Methylene blue (MB) dye was degraded by these methods. The synergy between light and piezoelectric actions improved degradation performance significantly. It achieved a 74% degrading efficiency and a kinetic rate constant of 7.3 × 10−3 min−1. The findings show that the BBZO-PVDF composite membrane has great potential for enhanced environmental remediation and catalytic processes. [ABSTRACT FROM AUTHOR]

A large amount of data is accumulated by the health-care industry. This data contains effective patterns which enable efficient decision-making. These patterns often go unexplored. Various machine learning methods can be incorporated in such situation. This work uses various machine learning methods such as Decision Tree, MARS, Random Forest and TMGA to realize the data mining goals. Results show that the Decision Tree method predicts the diagnosis of heart disease most effectively from patient's data. The dataset used to carry on this research work is taken from the popular UCI repository and is known as the Cleveland Dataset. It is implemented on the R platform. [ABSTRACT FROM AUTHOR]

Plant diseases are a major threat to food security and economic prosperity around the globe. Deep learning models based on Convolution Neural Network (CNN) have shown promising results in dealing with plant disease detection tasks. However, according to the No Free Lunch Theorem, no single model is suitable for all cases. Moreover, the vast diversity of plant diseases makes the model selection process time and resource extensive, using exhaustive search. This work proposes a meta-learning-based framework that recommends top-n suitable models for an unseen plant disease detection dataset using the prior evaluations of benchmark models on plant disease detection tasks. Rank-Biased Overlap (RBO) is used to evaluate the efficacy of the proposed framework by evaluating actual rankings with respect to the predicted rankings. Extensive comparative experiments are carried out with different configurations of meta-extractors and meta-learners. The results obtained demonstrate that the probe network trained for 10 epochs (termed as "intermediate stage") along with standard deviation as meta-extractor and Support Vector Regressor as the meta-learner outperforms the rest with average RBO scores of 0.76, 0.73 and 0.75 for Top-5, Top-3 and Top-1 recommendations, respectively. Overall, this paper presents a viable substitute for the exhaustive search process carried out for choosing the best deep learning model for plant disease detection scenario, leading to better resource utilization and faster implementation procedure. • Meta-learning framework to recommend models for plant disease identification task. • Meta-dataset created using 13 benchmark CNN models on 24 different species of plants. • RBO score used to evaluate combinations of meta-extractors and meta-learners. [ABSTRACT FROM AUTHOR]

Trend of social networking sites is at peak, so ocean of data is being created and generated. Micro blogging sites are used by billions of individuals to express their opinions on different topics related to different fields. Micro blogging has become a dynamic platform of communication between Internet users. Twitter a micro blogging service, has become the main portal where users prompt their views, emotions and opinions. Twitter is the fundamental social networking website. Opinion mining and sentiment analysis is most trending hot topic with numerous multi applications ranging from simple to complex. With the speedy improvement and progress of social network websites, sentiment analyses have become the main central talk of every conversation. This paper presents a review of various research works done in this field. [ABSTRACT FROM AUTHOR]

As traffic and population is increasing, it's monitoring and automation is finding a scope for development and it is emerging as a lucrative field for research and development. Intelligent transportation systems (ITS) are advanced applications which aim to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks. Realizing its enormous potential in automation, this review work was built around ITS and its applications. As ITS majorly involves the Wireless Sensor Networks, it was also explored to some extent. Various architectures were explored and compared to deduce the strengths and weaknesses of proposed solution. [ABSTRACT FROM AUTHOR]

Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G–BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively. [ABSTRACT FROM AUTHOR]

Groups of sensor nodes are called as sensor networks. They transmit sensed data to base station in cooperation with one another. An efficient use of power is important for increased network lifespan. Wireless sensor networks are tiny, light in weight, battery powered sensor nodes. Various parameters like clustering overhead, lifetime of network, energy hole, FND (first node dies) LND (last end dies) are optimized in WSN with the help of various proposed hybrid unequal clustering protocol. Another issue for data gathering in WSN is the hot spot or energy hole that are formed near sink node are also addressed. Various new emerging strategies laid stress on the best optimal usage of constrain resources like energy, memory and various other processing abilities. This paper addresses a review of different research works done in this area. [ABSTRACT FROM AUTHOR]

In VANET environment the data of consecutive vehicle position truly are discrete, so the key to the moving vehicle modeling is to efficiently reduces update frequency of the position data to progress the Major Attributes regarding Security like communication and database management load Privacy, authentication and confidentiality for all applications of VANET. Geograhically VANET might be of two types either Fully connected or sparsly connected. Its type depends on day time or saturation rate of market devices. Many solutions of security Problems in VANET exist but its implementation is very costly for both user and for Service Provider. [ABSTRACT FROM AUTHOR]

Hydrolases, being most prominent enzymes used in industrial processes have left no stone unturned in fascinating the pharmaceutical industry. Lipases, being a part of acyl hydrolases are the ones that function similarly to esterases (except an interfacial action) wherein they generally catalyze the hydrolysis of ester bonds. Be it in terms of stereoselectivity or regioselectivity, lipases have manifested their promiscuous proficiency in rendering biocatalytic drug synthesis and intermediates thereof. Industrial utilization of lipases is prevalent since decades ago, but their distinctive catalytic competencies have rendered them suitable for maneuverability in various tides of biocatalytic industrial process development. Numbers of exquisite catalysts have been fabricated out of lipases using nanobiotechnology whereby enzyme reusability and robustness have been conferred to many of the organic synthesis procedures. This marks a considerable achievement of lipases in the second wave of biocatalysis. Furthermore, in the third wave an advent of genetic engineering has fostered an era of customized lipases for suitable needs. Be it stability or an enhanced efficacy, genetic engineering techniques have ushered an avenue for biocatalytic development of drugs and drug intermediates through greener processes using lipases. Even in the forthcoming concept of co-modular catalytic systems, lipases may be the frontiers because of their astonishing capability to act along with other enzymes. The concept may render feasibility in the development of cascade reactions in organic synthesis. An upcoming wave demands fulfilling the vision of tailored lipase whilst a far-flung exploration needs to be unveiled for various research impediments in rendering lipase as a custom fit biocatalyst in pharmaceutical industry. [ABSTRACT FROM AUTHOR]

Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature. [ABSTRACT FROM AUTHOR]

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level. [ABSTRACT FROM AUTHOR]

In Mobile Ad hoc Network (MANET), network topology changes as devices/users/nodes move and nodes can serve as a source, destination, or router for the information. One of the challenging tasks in MANET is secure routing because of the change of network topology, open nature of wireless communications and frequent breakage in communication links. Thus, it is critically important to develop a robust and secure routing protocol for sending data from the source to the destination in MANET. In this paper, we investigate black hole attacks where compromised nodes utilize routing protocols for self-exposing which has the shortest route for data forwarding to the destination node. Specifically, the proposed approach enhances the Ad hoc On-Demand Distance Vector routing protocol for combating black hole attacks by leveraging the Firefly Algorithm with Artificial Neural Network. Performance evaluation is carried out using numerical results obtained from several experiments and considering different metrics such as computation overhead, packet delivery rate, throughput and delay. Numerical results show that the proposed approach outperforms the traditional approaches. [ABSTRACT FROM AUTHOR]

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT) based solutions are gaining prominence and innovative Peer To Peer (P2P) energy trading at micro-level are being deployed. Such advancement, however leave traditional security models vulnerable and pave the path for Blockchain, an Distributed Ledger Technology (DLT) with its decentralized, open and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, massive volumes of micro transactions are required to be supported, which become a performance bottleneck with existing Blockchain solution such as Hyperledger, Ethereum and so on. In this paper, a lightweight 'Tangle' based framework, namely IOTA (Third generation DLT) is employed for designing an energy trading market that uses Directed Acyclic Graph (DAG) based solution that not only alleviates the reward overhead for micro-transactions but also provides scalability, quantum-proof, and high throughput of such transactions at low confirmation latency. Furthermore the Masked Authentication Messaging (MAM) protocol is used over the IOTA P2P energy trading framework that allows energy producer and consumer to share the data while maintaining the confidentiality, and facilitates the data accessibility. The Raspberry Pi 3 board along with voltage sensor (INA219) used for the setting up light node and publishing and fetching data from the Tangle. The results of the obtained benchmarking indicate low confirmation latency, high throughput, system with Hyperledger Fabric and Ethereum. Moreover, the effect of transaction rate decreases when the IOTA bundle size increases more than 10. For bundle size 5 and 10 it behaves absolutely better than any other platform. The speedy confirmation time of transactions in IOTA, is most suitable for peer to peer energy trading scenarios. This study serves as a guideline for deploying, end-to-end transaction with IOTA Distributed Ledger Technology (DLT) and improving the performance of Blockchain in the energy sector under various operating conditions. [ABSTRACT FROM AUTHOR]

As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

• CuO nanobeads was synthesized by simple Co-precipitation method. • CV and GCD analysis confirms the faradic nature of synthesized material. • A high value of specific capacity 69.1 mA hg−1 (771 Fg−1). • Capacitance retention of 82.1% was achieved up to 1000 cycles. Here we report, a simple co-precipitation method for the synthesis of copper oxide (CuO) nanobeads. Many characterization techniques such as Powder X-ray diffraction (P-XRD), Field Emission Scanning Electron Microscope (FESEM) and Fourier Transform Infrared Spectroscopy (FTIR) confirms the purity of CuO nanostructure while electrochemical studies exhibit a specific capacity of 69.1 mA hg−1 (771 Fg−1) at 0.83 Ag−1 current density which is higher than other reported bare CuO nanostructure. Furthermore capacity retention of 82.1% over 1000 charge/discharge cycles and low value of charge transfer resistance (R ct) confirms its potential to be used as an electrode material for energy storage devices. [ABSTRACT FROM AUTHOR]

Summary: Internet of things (IoT) has evolved as an innovation of next generation in this world of smart devices. IoT tends to provide services for data collection, data management, and data and device security required for application development. Things or devices in IoT communicate and compute to make our lives comfortable and safe. In inventory automation, real‐time check on items, their information management, and status management, monitoring can be carried out using IoT. The huge amount of data that flows among the devices in the network demands for a security framework that ensures authentication, authorization, integrity, and confidentiality of data. The existing security solutions like SIMON or SPECK offer lightweight security solutions but are vulnerable to differential attack because of their simplicity. Moreover, existing solutions do not offer inbuilt authentication. Therefore, this research work contributes a secure and lightweight IoT‐based framework using wireless sensor network (WSN) as a technology. The existing security solutions SPECK and SIMON are compared with the proposed security approach using COOJA simulator. The results show that proposed approach outstands others by 2% reduction in number of CPU cycles, 10% less execution time, 4% less memory requirements of security approach, and with minimum 10% more security impact. [ABSTRACT FROM AUTHOR]

With the per capita growth of energy demand, there is a significant need for alternative and sustainable energy resources. Efficient electrochemical catalysis will play an important role in sustaining that need, and nanomaterials will play a crucial role, owing to their high surface area to volume ratio. Electrospun nanofiber is one of the most promising alternatives for producing such nanostructures. A section of key nano-electrocatalysts comprise of transition metals (TMs) and their derivatives, like oxides, sulfides, phosphides and carbides, etc., as well as their 1D composites with carbonaceous elements, like carbon nanotubes (CNTs) and carbon nanofiber (CNF), to utilize the fruits of TMs' electronic structure, their inherent catalytic capability and the carbon counterparts' stability, and electrical conductivity. In this work, we will discuss about such TM derivatives, mostly TM-based ceramics, grown on the CNF substrates via electrospinning. We will discuss about manufacturing methods, and their electrochemical catalysis performances in regards to energy conversion processes, dealing mostly with water splitting, the metal–air battery fuel cell, etc. This review will help to understand the recent evolution, challenges and future scopes related to electrospun transition metal derivative-based CNFs as electrocatalysts. [ABSTRACT FROM AUTHOR]

In the realm of optoelectronic applications, the incorporation of a metal interlayer between layers of a topological material holds significant potential. The interlayer introduces benefits such as improved charge transfer efficiency, tunable band alignment, plasmonics effect, and enhanced light‐matter interactions, paving the way for more efficient and versatile optoelectronic devices. This work explores the incorporation of gold between Bi2Se3, resulting in an enhancement in the response time of photoresponse of interlayered systems (Bi2Se3‐Au‐Bi2Se3). The introduction of a gold interlayer between Bi2Se3 significantly influences the overlayer structure, crystallinity, crystallite size, and even phonon behavior. Moreover, the responsivity of the interlayered device at a bias (2 V) is found to have high responsivity with fast response and decay time (τr = 0.1 ms, and τd = 24 ms of interlayered systems) as compared to intrinsic Bi2Se3(τr = 0.6 ms, and τd = 28 ms). This change is attributed to the bandgap alteration of the Bi2Se3/Au interface, which is monitored through transient spectroscopy performed at different electric biases. This study suggests that incorporating a gold interlayer holds potential benefits for various optoelectronic applications. [ABSTRACT FROM AUTHOR]

Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7–1.9db) while (35.83–47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978. [ABSTRACT FROM AUTHOR]

There are errors in the Funding section. The correct Funding statement is: This research was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0012724, The Competency Development Program for Industry Specialist) and the Soonchunhyang University Research Fund. [Extracted from the article]

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings. [ABSTRACT FROM AUTHOR]

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-18603-z, published online 25 August 2022 The original version of this Article contained an error in Affiliation 1, which was incorrectly given as 'School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vijayawada 522237, Andhra Pradesh, India'. The correct affiliation is listed below: School of Computer Science and Engineering (SCOPE), VIT-AP University, Vijayawada 522237, Andhra Pradesh, India. [Extracted from the article]

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'. [ABSTRACT FROM AUTHOR]

New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the heterogeneous nature of devices involved in communication. Among other challenges, security is the most daunting challenge that contributes, at least in part, to the impeded momentum of the CPS realization. Designers assume that CPSs are themselves protected as they cannot be accessed from external networks. However, these days, CPSs have combined parts of the cyber world and also the physical layer. Therefore, cyber security problems are large for commercial CPSs because the systems move with one another and conjointly with physical surroundings, i.e., Complex Industrial Applications (CIA). Therefore, in this paper, a novel data security algorithm Dynamic Hybrid Secured Encryption Technique (DHSE) is proposed based on the hybrid encryption scheme of Advanced Encryption Standard (AES), Identity-Based Encryption (IBE) and Attribute-Based Encryption (ABE). The proposed algorithm divides the data into three categories, i.e., less sensitive, mid-sensitive and high sensitive. The data is distributed by forming the named-data packets (NDPs) via labelling the names. One can choose the number of rounds depending on the actual size of a key; it is necessary to perform a minimum of 10 rounds for 128-bit keys in DHSE. The average encryption time taken by AES (Advanced Encryption Standard), IBE (Identity-based encryption) and ABE (Attribute-Based Encryption) is 3.25 ms, 2.18 ms and 2.39 ms, respectively. Whereas the average time taken by the DHSE encryption algorithm is 2.07 ms which is very much less when compared to other algorithms. Similarly, the average decryption times taken by AES, IBE and ABE are 1.77 ms, 1.09 ms and 1.20 ms and the average times taken by the DHSE decryption algorithms are 1.07 ms, which is very much less when compared to other algorithms. The analysis shows that the framework is well designed and provides confidentiality of data with minimum encryption and decryption time. Therefore, the proposed approach is well suited for CPS-IoT. [ABSTRACT FROM AUTHOR]

Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed. [ABSTRACT FROM AUTHOR]

The paper presents a new security aspect for a Mobile Ad-Hoc Network (MANET)-based IoT model using the concept of artificial intelligence. The Black Hole Attack (BHA) is considered one of the most affecting threats in the MANET in which the attacker node drops the entire data traffic and hence degrades the network performance. Therefore, it necessitates the designing of an algorithm that can protect the network from the BHA node. This article introduces Ad-hoc On-Demand Distance Vector (AODV), a new updated routing protocol that combines the advantages of the Artificial Bee Colony (ABC), Artificial Neural Network (ANN), and Support Vector Machine (SVM) techniques. The combination of the SVM with ANN is the novelty of the proposed model that helps to identify the attackers within the discovered route using the AODV routing mechanism. Here, the model is trained using ANN but the selection of training data is performed using the ABC fitness function followed by SVM. The role of ABC is to provide a better route for data transmission between the source and the destination node. The optimized route, suggested by ABC, is then passed to the SVM model along with the node's properties. Based on those properties ANN decides whether the node is a normal or an attacker node. The simulation analysis performed in MATLAB shows that the proposed work exhibits an improvement in terms of Packet Delivery Ratio (PDR), throughput, and delay. To validate the system efficiency, a comparative analysis is performed against the existing approaches such as Decision Tree and Random Forest that indicate that the utilization of the SVM with ANN is a beneficial step regarding the detection of BHA attackers in the MANET-based IoT networks. [ABSTRACT FROM AUTHOR]

Machine learning has grown in popularity in recent years as a method for evaluating financial text data, with promising results in stock price projection from financial news. Various research has looked at the relationship between news events and stock prices, but there is little evidence on how different sentiments (negative, neutral, and positive) of such events impact the performance of stocks or indices in comparison to benchmark indices. The goal of this paper is to analyze how a specific banking news event (such as a fraud or a bank merger) and other co-related news events (such as government policies or national elections), as well as the framing of both the news event and news-event sentiment, impair the formation of the respective bank's stock and the banking index, i.e., Bank Nifty, in Indian stock markets over time. The task is achieved through three phases. In the first phase, we extract the banking and other co-related news events from the pool of financial news. The news events are further categorized into negative, positive, and neutral sentiments in the second phase. This study covers the third phase of our research work, where we analyze the impact of news events concerning sentiments or linguistics in the price movement of the respective bank's stock, identified or recognized from these news events, against benchmark index Bank Nifty and the banking index against benchmark index Nifty50 for the short to long term. For the short term, we analyzed the movement of banking stock or index to benchmark index in terms of CARs (cumulative abnormal returns) surrounding the publication day (termed as D) of the news event in the event windows of (−1,D), (D,1), (−1,1), (D,5), (−5,−1), and (−5,5). For the long term, we analyzed the movement of banking stock or index to benchmark index in the event windows of (D,30), (−30,−1), (−30,30), (D,60), (−60,−1), and (−60,60). We explore the deep learning model, bidirectional encoder representations from transformers, and statistical method CAPM for this research. [ABSTRACT FROM AUTHOR]

Summary: In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work. [ABSTRACT FROM AUTHOR]

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare. [ABSTRACT FROM AUTHOR]

Peas are highly seasonal and perishable in nature, thereby requiring suitable preservation methods to be available throughout the year. Osmotic drying is one of the suitable methods for preserving perishable and semi‐perishable produce. Osmotic drying of green peas was carried out in ternary solutions of water, sodium chloride (NaCl), and sucrose having different solution concentrations and osmotic temperatures. Three‐stage drying of osmotically pretreated green peas was carried out in convective tray dryer at air temperatures of 70°C (for first 3 hr), 60°C (for next 2 hr), and 50°C (till final moisture content of below 10% wb). The samples were analyzed for moisture content, color change, sphericity, hardness, and rehydration ratio (RR). The osmotic drying process was optimized using response surface methodology (RSM). The optimized process conditions for maximum sphericity, hardness, and RR and for minimum moisture content and color change were determined as sodium chloride solution (10% w/w) and osmotic temperature of 34.41°C. Practical applications: Osmotic drying of foods is advantageous when used in combination with other drying techniques as it reduces total drying time, inhibits enzymatic browning, and also reduces energy costs. This study would help to understand the effect of osmotic solution concentration and osmotic temperature on quality of osmo‐convective dried peas. Optimization of process parameters would provide the best combination of osmotic solution concentration and osmotic temperature for achieving better product quality in terms of moisture content, color, hardness, rehydration ratio, and sphericity. The high‐quality product hence obtained using optimized process parameters will find utilization not only in soup mixes, ready‐to‐eat snack foods but also as fresh‐like peas upon rehydration during off‐seasons. [ABSTRACT FROM AUTHOR]

• A new hybrid deep learning based framework for photovoltaic power forecasting is proposed. • The framework integrates long short term memory layer with vanishing time series gradient and maximal overlap discrete wavelet transform series. • MODWT is implemented using a multiresolution pyramidal hierarchical decomposition technique. • The proposed method outperforms previous models and establishes its efficacy even for longer intervals. During this era of the energy crisis, when the non-renewable sources are rapidly diminishing, efforts are being taken to utilize renewable sources predominantly. This manuscript presents a hybrid deep learning framework using long short term memory (LSTM) Layer with vanishing time series gradient and maximal overlap discrete wavelet transform (MODWT) model for photovoltaic (PV) power forecasting through time series decomposition. The proposed framework is implemented on the dataset collected from Yulara Solar System, Australia. During the experimental evaluation, obtained results demonstrate short term temporal dependence of PV power forecasting on solar power magnitudes as well as weather conditions. Moreover, the proposed model outperforms existing state-of-the-art models in terms of mean average percentage error (MAPE) by 14.17%, 3.01%, and 16.49% for 1 day, 10 days, and 1 month, respectively, establishing its efficacy even for longer intervals. Proposed Ensemble Model divided into three stages viz. Time series decomposition and reconstruction, forecasting phase, and weighted aggregation of predicted results [Display omitted] [ABSTRACT FROM AUTHOR]

The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user's quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain. [ABSTRACT FROM AUTHOR]

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality. [ABSTRACT FROM AUTHOR]

Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques. [ABSTRACT FROM AUTHOR]