DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

Suppression and synchronization of chaos in uncertain time-delay physical system

The mechanical horizontal platform(MHP)system exhibits a rich chaotic behavior.The chaotic MHP system has applications in the earthquake and offshore industries.This article proposes a robust adaptive continuous control(RACC)algorithm.It investigates the control and synchronization of chaos in the uncertain MHP system with time-delay in the presence of unknown state-dependent and time-dependent disturbances.The closed-loop system contains most of the nonlinear terms that enhance the complexity of the dynamical system;it improves the efficiency of the closed-loop.The proposed RACC approach(a)accomplishes faster convergence of the perturbed state variables(synchronization errors)to the desired steady-state,(b)eradicates the effect of unknown state-dependent and time-dependent disturbances,and(c)suppresses undesirable chattering in the feedback control inputs.This paper describes a detailed closed-loop stability analysis based on the Lyapunov-Krasovskii functional theory and Lyapunov stability technique.It provides parameter adaptation laws that confirm the convergence of the uncertain parameters to some constant values.The computer simulation results endorse the theoretical findings and provide a comparative performance.

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

Intelligent Systems and Photovoltaic Cells Empowered Topologically by Sudoku Networks

A graph invariant is a number that can be easily and uniquely calculated through a graph.Recently,part of mathematical graph invariants has been portrayed and utilized for relationship examination.Nevertheless,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computer science,physics,and chemistry.In this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computer science like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical compounds.These networks are generally utilized in disorder,fractals,recursive groupings,and complex frameworks.Our outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced forms.The study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in them.The calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.

Effective desalination and anti-biofouling performance via surface immobilized MWCNTs on RO membrane

Desalination is considered a viable method to overcome the issue of water scarcity either from waste water or seawater. For this purpose, this study employed a facile approach to develop surface immobilized oxidized-MWCNTs(o-MWCNTs) onto crosslinked polyvinyl alcohol(PVA) membrane. Firstly, modified polysulphone substrate was synthesized on to which crosslinked PVA layer was spread onto it. PVA layer act as active layer for surface immobilization of o-MWCNTs in varying concentration. The functional group analysis, morphology and roughness of membranes surface was conducted out using FTIR, SEM and AFM respectively. The results showed that modified membranes, immobilized o-MWCNTs enhanced the salt rejection(Na_(2)SO_(4)) upto 99.8%. After contacting with Escherichia coli and Staphylococcus aureus for 2.5 h the bacteria mortalities of the fabricated membrane could reach 96.9%. Furthermore, the antibiofouling tests showed that OP-MWCNTs(1-5) modified membranes have higher anti-biofouling property than the control membrane.

Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.

Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted.

ETL Maturity Model for Data Warehouse Systems:A CMMI Compliant Framework

The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions.The Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW system.The more mature the ETL process the more reliable the DW system.In this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced.The EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and Reliable.Each level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL system.Quality Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL process.Each KPA has its own set of QOs,the execution of which meets the requirements of that KPA.Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim.This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system.This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.

Performance Evaluation of Virtualization Methodologies to Facilitate NFV Deployment

The development of the Next-Generation Wireless Network(NGWN)is becoming a reality.To conduct specialized processes more,rapid network deployment has become essential.Methodologies like Network Function Virtualization(NFV),Software-Defined Networks(SDN),and cloud computing will be crucial in addressing various challenges that 5G networks will face,particularly adaptability,scalability,and reliability.The motivation behind this work is to confirm the function of virtualization and the capabilities offered by various virtualization platforms,including hypervisors,clouds,and containers,which will serve as a guide to dealing with the stimulating environment of 5G.This is particularly crucial when implementing network operations at the edge of 5G networks,where limited resources and prompt user responses are mandatory.Experimental results prove that containers outperform hypervisor-based virtualized infrastructure and cloud platforms’latency and network throughput at the expense of higher virtualized processor use.In contrast to public clouds,where a set of rules is created to allow only the appropriate traffic,security is still a problem with containers.

Adapted Speed System in a Road Bend Situation in VANET Environment

Today,road safety remains a serious concern for governments around the world.In fact,approximately 1.35 million people die and 2–50 million are injured on public roads worldwide each year.Straight bends in road traffic are the main cause of many road accidents,and excessive and inappropriate speed in this very critical area can cause drivers to lose their vehicle stability.For these reasons,new solutions must be considered to stop this disaster and save lives.Therefore,it is necessary to study this topic very carefully and use new technologies such as Vehicle Ad Hoc Networks(VANET),Internet of Things(IoT),Multi-Agent Systems(MAS)and Embedded Systems to create a new system to serve the purpose.Therefore,the efficient and intelligent operation of the VANET network can avoid such problems as it provides drivers with the necessary real-time traffic data.Thus,drivers are able to drive their vehicles under correct and realistic conditions.In this document,we propose a speed adaptation scheme for winding road situations.Our proposed scheme is based on MAS technology,the main goal of which is to provide drivers with the information they need to calculate the speed limit they must not exceed in order to maintain balance in dangerous areas,especially in curves.The proposed scheme provides flexibility,adaptability,and maintainability for traffic information,taking into account the state of infrastructure and metering conditions of the road,as well as the characteristics and behavior of vehicles.

Detecting Android Botnet Applications Using Convolution Neural Network

The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e.,games applications,entertainment,online banking,social network sites,etc.,and also allow the end users to perform a variety of activities.Because of activities,mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information,phishing,spamming,Distributed Denial of Services(DDoS),and malware dissemination.Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area.A botnet is a collection of infected devices that are managed by a botmaster and communicate with each other via a command server in order to carry out malicious attacks.With the rise in malicious attacks,detecting botnet applications has become more challenging.Therefore,it is essential to investigate mobile botnet attacks to uncover the security issues in severe financial and ethical damages caused by a massive coordinated command server.Current state of the art,various solutions were provided for the detection of botnet applications,but in general,the researchers suffer various techniques of machine learning-based methods with static features which are usually ineffective when obfuscation techniques are used for the detection of botnet applications.In this paper,we propose an approach by exploring the concept of a deep learning-based method and present a well-defined Convolutional Neural Network(CNN)model.Using the visualization approach,we obtain the colored images through byte code files of applications and perform an experiment.For analysis of the results of an experiment,we differentiate the performance of the model from other existing research studies.Furthermore,our method outperforms with 94.34%accuracy,92.9%of precision,and 92%of recall.

Ontology-Based News Linking for Semantic Temporal Queries

Daily newspapers publish a tremendous amount of information disseminated through the Internet.Freely available and easily accessible large online repositories are not indexed and are in an un-processable format.The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed.There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora,especially for South Asian languages.The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.Lack of indexing,primarily semantic indexing of the daily news items,makes news items impracticable for any querying.Moreover,the most straightforward search facility does not support these unindexed news resources.Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages,i.e.,Urdu.The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler.The corpus is then semantically linked and annotated with daily news items.Two techniques are proposed for image annotation,free annotation and fixed cross examination annotation.The second technique got higher accuracy.Build news ontology in protégéusing OntologyWeb Language(OWL)language and indexed the annotations under it.The application is also built and linked with protégéso that the readers and journalists have an interface to query the news items directly.Similarly,news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.

Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration(VLSI)technology and design software have significantly broadened the scope of digital signal processing(DSP)applications.The use of application-specific integrated circuits(ASICs)and programmable digital signal processors for many DSP applications have changed,even though new system implementations based on reconfigurable computing are becoming more complex.Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation(DWT)and sophisticated computerized design techniques,which are much needed in today’s modern world.New research and commercial efforts to sustain power optimization,cost savings,and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged.Hence,in this paper,it is proposed that theDWTmethod can be implemented on a fieldprogrammable gate array in a digital architecture(FPGA-DA).We examined the effects of quantization on DWTperformance in classification problems to demonstrate its reliability concerning fixed-point math implementations.The Advanced Encryption Standard(AES)algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks(ANN)method.By reducing hardware area by 57%,the proposed system has a higher throughput rate of 88.72%,reliability analysis of 95.5%compared to the other standard methods.

Ontology-Based Crime News Semantic Retrieval System

Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,and journals.From such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies.Keyword-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant results.They are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given word.Therefore,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the data.An ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal cases.The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries.In this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two experiments.In the first experiment,we implemented a keyword-based textual IR systemusing Apache Lucene.In the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries.The keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.

Identification and Visualization of Spatial and Temporal Trends in Textile Industry

The research volume increases at the study rate,causing massive text corpora.Due to these enormous text corpora,we are drowning in data and starving for information.Therefore,recent research employed different text mining approaches to extract information from this text corpus.These proposed approaches extract meaningful and precise phrases that effectively describe the text’s information.These extracted phrases are commonly termed keyphrases.Further,these key phrases are employed to determine the different fields of study trends.Moreover,these key phrases can also be used to determine the spatiotemporal trends in the various research fields.In this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend analysis.Therefore,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific period.This study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research trend.This data was collected from various open access journals.Further,this research determined the spatiotemporal trends using quality phrasemining.This research also focused on finding the research collaboration of different countries in a particular research subject.The research collaborations of other countries’researchers show the impact on import and export of those countries.The visualization approach is also incorporated to understand the results better.

Translation of English Language into Urdu Language Using LSTM Model

English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation.In order tomake knowledge available to the masses,there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion.Machine translation has achieved this goal with encouraging results.When decoding the source text into the target language,the translator checks all the characteristics of the text.To achieve machine translation,rule-based,computational,hybrid and neural machine translation approaches have been proposed to automate the work.In this research work,a neural machine translation approach is employed to translate English text into Urdu.Long Short Term Short Model(LSTM)Encoder Decoder is used to translate English to Urdu.The various steps required to perform translation tasks include preprocessing,tokenization,grammar and sentence structure analysis,word embeddings,training data preparation,encoder-decoder models,and output text generation.The results show that the model used in the research work shows better performance in translation.The results were evaluated using bilingual research metrics and showed that the test and training data yielded the highest score sequences with an effective length of ten(10).

Fault Tolerant Optical Mark Recognition

OpticalMark Recognition(OMR)systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use,OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires.The accuracy of OMR systems is very important due to the environment inwhich they are used.TheOMRalgorithm relies on pixel projection or Hough transform to determine the exact answer in the document.These techniques rely on majority voting to approximate a predetermined shape.The performance of these systems depends on precise input from dedicated hardware.Printing and scanning OMR tables introduces artifacts that make table processing error-prone.This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques.Depending on the type of artifact introduced,accuracy is affected differently.We classified the types of errors and their frequency according to the artifacts in the OMR system.As a major contribution,we propose an improved algorithm that fixes errors due to skewness.Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents.As a minor contribution,our proposal also improves the accuracy of detecting markers in OMR documents.The results show an improvement in accuracy over existing algorithms in each of the identified problems.This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.

Impediments of Cognitive System Engineering in Machine-Human Modeling

A comprehensive understanding of human intelligence is still an ongoing process,i.e.,human and information security are not yet perfectly matched.By understanding cognitive processes,designers can design humanized cognitive information systems(CIS).The need for this research is justified because today’s business decision makers are faced with questions they cannot answer in a given amount of time without the use of cognitive information systems.The researchers aim to better strengthen cognitive information systems with more pronounced cognitive thresholds by demonstrating the resilience of cognitive resonant frequencies to reveal possible responses to improve the efficiency of human-computer interaction(HCI).Apractice-oriented research approach included research analysis and a review of existing articles to pursue a comparative research model;thereafter,amodel development paradigm was used to observe and monitor the progression of CIS during HCI.The scope of our research provides a broader perspective on how different disciplines affect HCI and how human cognitive models can be enhanced to enrich complements.We have identified a significant gap in the current literature on mental processing resulting from a wide range of theory and practice.

Temporal Preferences-Based Utility Control for Smart Homes

The residential sector contributes a large part of the energy to the global energy balance.To date,housing demand has mostly been uncontrollable and inelastic to grid conditions.Analyzing the performance of a home energy manage-ment system requires the creation of various profiles of real-world residential demand,as residential demand is complex and includes multiple factors such as occupancy,climate,user preferences,and appliance types.Average Peak Ratio(A2P)is one of the most important parameters when managing an efficient and cost-effective energy system.At the household level,the larger relative magni-tudes of certain energy devices make managing this ratio critical,albeit difficult.Various Demand Response(DR)and Demand Side Management(DSM)systems have been proposed to reduce this ratio to 1.The main ways to achieve this are economic incentives,user comfort modeling and control,or preference-based.In this study,we propose a unique opportunistic social time approach called the Time Utility Based Control Feature(TUBCF),which uses the concept of a utility function from economics to model and control consumer devices.We propose a DR model for residential customers to reduce Peak-to-Average Ratio(PAR)and improve customer satisfaction by eliminating Appliance Wait Time(WTA)during peak periods.For PAR reduction and WTA,we propose a system architecture and mathematical formulation.Our proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational characteristics.Simulation results show that using this strategy can reduce A2P by 80%and improve user comfort during peak hours.

Sustainable Learning of Computer Programming Languages Using Mind Mapping

In the current era of information technology,students need to learn modern programming languages efficiently.The art of teaching/learning program-ming requires many logical and conceptual skills.So it’s a challenging task for the instructors/learners to teach/learn these programming languages effectively and efficiently.Mind mapping is a useful visual tool for establishing ideas and connecting them to solve problems.This research proposed an effective way to teach programming languages through visual tools.This experimental study uses a mind mapping tool to teach two programming environments:Text-based Programming and Blocks-based Programming.We performed the experiments with one hundred and sixty undergraduate students of two public sector universities in the Asia Pacific region.Four different instructional approaches,including block-based language(BBL),text-based languages(TBL),mind map with text-based language(MMTBL)and mind mapping with block-based(MMBBL)are used for this purpose.The results show that instructional approaches using a mind mapping tool to help students solve given tasks in their critical thinking are more effective than other instructional techniques.