A Model for Measuring Geographic Information Systems Success

Geographic Information Systems (GIS) have become a fact of our life as they are being used by more people and organizations for more complex decision problems than ever before. The use of GIS can achieve valuable benefits for individuals, organizations and society;however, the achievement of these benefits depends on the success of GIS. While information systems (IS) success models have received much attention among researchers, there is a general scarcity of research conducted to measure the GIS success. This paper proposes a success model for measuring GIS success by extending and modifying previous IS success models. The developed success model consists of two main levels: GIS project diffusion success, and GIS post-implementation success. The first level identifies the critical success factors (CSFs) that influence the success of GIS adoption at each stage of the diffusion process. The second level of the proposed model identifies and organizes the success dimensions (outcome measures) of GIS in temporal and causal relationships. In order to assess the relationships among the success dimensions, 11 hypotheses were tested. Data were collected through a questionnaire that was distributed to 252 GIS users/managers in Egypt and abroad. The empirical results support 6 hypotheses and reject 5 hypotheses.

An Information Systems Project Management Course Using a Service-Learning Model

This paper describes the implementation of an Information Systems （IS） capstone project management course that is a requirement for graduating seniors in an undergraduate Computer Information Systems （CIS） program at a regional university. The description provides a model which includes the culmination of students＇ academic training in an IS curriculum which is part of a Bachelor of Business Administration （BBA） program in an accredited college of business. The course requires an application of technical and business skills, as well as systems development and project management skills--while students are working on an actual IS project for an external sponsoring organization. Rationale for implementing this type of course includes the benefits it provides to the students, the project sponsors, and the IS department providing the course. Feedback from the course is used as integral part of the C1S curriculum assessment process used for accreditation purposes.

BIoMT:A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architectures are vulnerable to multiple security and privacy problems.The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks.This article presents a private and easily expandable blockchain-based framework for the IoMT.The proposed framework contains several participants,including private blockchain,hospitalmanagement systems,cloud service providers,doctors,and patients.Data security is ensured by incorporating an attributebased encryption scheme.Furthermore,an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network.The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner.The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency.Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations.The average latency is 61 ms and 16 ms for read and write operations,respectively.

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Solving the Generalized Traveling Salesman Problem Using Sequential Constructive Crossover Operator in Genetic Algorithm

The generalized travelling salesman problem(GTSP),a generalization of the well-known travelling salesman problem(TSP),is considered for our study.Since the GTSP is NP-hard and very complex,finding exact solutions is highly expensive,we will develop genetic algorithms(GAs)to obtain heuristic solutions to the problem.In GAs,as the crossover is a very important process,the crossovermethods proposed for the traditional TSP could be adapted for the GTSP.The sequential constructive crossover(SCX)and three other operators are adapted to use in GAs to solve the GTSP.The effectiveness of GA using SCX is verified on some GTSP Library(GTSPLIB)instances first and then compared against GAs using the other crossover methods.The computational results show the success of the GA using SCX for this problem.Our proposed GA using SCX,and swap mutation could find average solutions whose average percentage of excesses fromthe best-known solutions is between 0.00 and 14.07 for our investigated instances.

Using IoT to Smart Security Door System with Face Tracking Camera

Security is a serious concern, whether it may be the security of assets, data and human life. Providing humans with security and safety for their belongings and items is an important need. A smart lock door project/ with different types of methods for entry, like fingerprint and authentication PIN code is suggested with an unnoticeable face tracking camera capturing a photo in case of error data entry. It is to be controlled via the user’s smartphone using Blynk with the implementation of IoT. This technology is made with two microcontrollers. ESP32 is used to control the solenoid lock, fingerprint sensor and keypad. ESP32-CAM is used to capture a photo and send it to the owner’s smartphone to be viewed on Telegram application. Many conclusions are extracted from system results, as well as suggested ideas for future work.

Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.

Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches

This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing,?where 80% and 20% of the Cleveland data set were randomly selected for training and testing?purposes respectively. Each system also has an additional module known as case-based module,?where the user has to input values for 13 required attributes as specified by the Cleveland data set,?in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.

A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries.To overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection framework.The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions.The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations.To optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy.The efficacy of the suggested framework is evaluated via two standard and open-source datasets.The proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class scenarios.The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes.Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.

Fully Automated Density-Based Clustering Method

Cluster analysis is a crucial technique in unsupervised machine learning,pattern recognition,and data analysis.However,current clustering algorithms suffer from the need for manual determination of parameter values,low accuracy,and inconsistent performance concerning data size and structure.To address these challenges,a novel clustering algorithm called the fully automated density-based clustering method(FADBC)is proposed.The FADBC method consists of two stages:parameter selection and cluster extraction.In the first stage,a proposed method extracts optimal parameters for the dataset,including the epsilon size and a minimum number of points thresholds.These parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find clusters.The proposed method was evaluated on different benchmark datasets andmetrics,and the experimental results demonstrate its competitive performance without requiring manual inputs.The results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method,k-means,spectral clustering,DBSCAN,FCDCSD,Gaussian mixtures,and density-based spatial clustering methods.It can handle any kind of data set well and perform excellently.

Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously.Several studies used Sentiment Analysis(SA)to analyze the emotions expressed through tweets upon COVID-19.Therefore,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven SA(ABCMLSA)model is developed for conducting Sentiment Analysis of COVID-19 Twitter data.The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made uponCOVID-19.It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors.For identification and classification of the sentiments,the Support Vector Machine(SVM)model is exploited.At last,the ABC algorithm is applied to fine tune the parameters involved in SVM.To demonstrate the improved performance of the proposed ABCML-SA model,a sequence of simulations was conducted.The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.

Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion traffic.The vast network traffic volume also complicates most network monitoring systems and algorithms.Several intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these incidents.This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms.The stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression algorithm.Evaluated using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,respectively.The obtained results demonstrate improvements in other measurement criteria.

Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

Road congestion,air pollution,and accident rates have all increased as a result of rising traffic density andworldwide population growth.Over the past ten years,the total number of automobiles has increased significantly over the world.In this paper,a novel method for intelligent traffic surveillance is presented.The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes.To improve the results,mean-shift clustering was applied to the segmented images.Afterward,the pixels given the label for the vehicle were extracted and blob detection was applied to mark each vehicle.For the validation of each detection,a vehicle verification method based on the structural similarity index is proposed.The tracking of vehicles across the image frames is done using the Identifier(ID)assignment technique and particle filter.Also,vehicle counting in each frame along with trajectory estimation was done for each object.Our proposed system demonstrated a remarkable vehicle detection rate of 0.83 over Vehicle Aerial Imaging from Drone(VAID),0.86 over AU-AIR,and 0.75 over the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)dataset during the experimental evaluation.The proposed system can be used for several purposes,such as vehicle identification in traffic,traffic density estimation at intersections,and traffic congestion sensing on a road.

Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.