A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

DeepIoT.IDS:Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points.Recently,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks.However,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks.Therefore,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of attacks.Consequently,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(DeepIoT.IDS)model to detect existing and novel cyberattacks.The HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)classifier.The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced classes.We have compared the performance of the DeepIoT.IDS model with three recent models.The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,respectively.Furthermore,it can detect the occurrence of low-frequency attacks that are undetectable by other models.

Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

Mental health signifies the emotional,social,and psychological well-being of a person.It also affects the way of thinking,feeling,and situation handling of a person.Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life.Mental health problems are rising globally and constituting a burden on healthcare systems.Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated.The machine learning models are highly prevalent for medical data analysis,disease diagnosis,and psychiatric nosology.This research addresses the challenge of detecting six major psychological disorders,namely,Anxiety,Bipolar Disorder,Conversion Disorder,Depression,Mental Retardation and Schizophrenia.These challenges are mined by applying decision level fusion of supervised machine learning algorithms.A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the models.Furthermore,to reduce the impact of a conflicting decision,a voting scheme Shrewd Probing Prediction Model(SPPM)is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine(RF+GBM).This research provides an intuitive solution for mental disorder analysis among different target class labels or groups.A framework is proposed for determining the mental health problem of patients using observations of medical experts.The framework consists of an ensemble model based on RF and GBM with a novel SPPM technique.This proposed decision level fusion approach by combining RF+GBM with SPPM-MIN significantly improves the performance in terms of Accuracy,Precision,Recall,and F1-score with 71\%,73\%,71\%and 71\%respectively.This framework seems suitable in the case of huge and more diverse multiclass datasets.Furthermore,three vector spaces based on TF-IDF(unigram,bi-gram,and tri-gram)are also tested on the machine learning models and the proposed model.

A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM_(2.5)) Concentration Forecasting

Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment.Particulate Matter(PM_(2.5))is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m.For risk assessment and epidemiological investigations,a better knowledge of the spatiotemporal variation of PM_(2.5) concentration in a constant space-time area is essential.Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression,ignoring a plethora of hidden but crucial manipulating aspects.Many advanced deep learning approaches have been proposed to forecast Particulate Matter(PM_(2.5)).Recurrent neural network(RNN)is one of the popular deep learning architectures which is widely employed in PM_(2.5) concentration forecasting.In this research,we proposed a Two-State Gated Recurrent Unit(TS-GRU)for monitoring and estimating the PM_(2.5) concentration forecasting system.The proposed algorithm is capable of considering both spatial and temporal hidden affecting elements spontaneously.We tested our model using data from daily PM_(2.5) dimensions taken in the contactual southeast area of the United States in 2009.In the studies,three evaluation matrices were utilized to compare the overall performance of each algorithm:Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE).The experimental results revealed that our proposed TS-GRU model outperformed compared to the other deep learning approaches in terms of forecasting performance.

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。

COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Coronavirus(COVID-19)epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide.This newly recognized virus is highly transmissible,and no clinically approved vaccine or antiviral medicine is currently available.Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus.Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and followup.Here,a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray(CX-R)images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation.First,Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images,respectively.Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused.Parallel architecture,which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people,was considered.The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%,sensitivity of 99.90%,specificity of 100%,precision of 100%,F1-score of 99.93%,MSE of 0.021%,and RMSE of 0.016%in a large-scale dataset.This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

Improved binary similarity measures for software modularization

目的:各种各样的二元相似度测量在聚类方法中被用来确定数据中的相似实体的同类组。这些相似度测量大多数仅基于特征的存在或缺失。二元相似度测量在软件模块化中亦能与不同的聚类方法一起用于提高软件系统的可理解性与可管理性。每种相似度测量都有其优势与不足,分别能使聚类结果优化或恶化。创新点:本文强调了软件模块化中一些已有的著名的二元相似度测量的优势。此外,基于这些已有的相似度测量,新提出了几种改进的相似度测量。方法:首先,介绍了一些软件模块化中已有的著名的二元相似度测量的优势。接着,提出了几种改进的新的相似度测量。结合具体例子,说明这些新方法整合了JCJNM和RR这几种已有的二元相似度测量的优势。最后,通过实验比较新方法与已有方法的结果,验证所提新方法的有效性。结论:实验结果表明相较于已有的相似度测量,本文所提出的新的二元相似度测量结果的可信度更高。这些新方法能减少任意决策的数量,增加聚类过程中聚类的数量。尽管这些新方法仅基于数据的二元特征向量表达,它们能被用来测试任何编程语言编写的软件系统。

How New Individuals Behave in a Heterogeneous Community: A Computational Approach to Norm Assimilation Using Agent-Based Systems

Heterogeneous community entails a number of social groups that adopt similar/different social norms.In such community,new individuals who join a new social group should be able to decide with which group they could assimilate based on their capabilities/values/manners.Otherwise,they would be penalized by other members in the group for violating some norms which they cannot comply.Using this approach,software agents would have better reasoning in simulating human society.In this paper,the authors propose a norms assimilation theory,in which a new agent attempts to assimilate with a social group’s norms.This theory builds an approach to norm assimilation,analyzes the cases for an agent to decide to assimilate with a social group and develops a mathematical model to measure the assimilation cost and the agent’s ability.The approach is developed based on the agent’s internal belief about its ability and desire,and its external belief about the cost of assimilating with a number of social groups.The significance of this research is two-fold.Firstly,the study paves the way to future design of intelligent systems,i.e.,software agents or robots,to closely mimic human social interactions.Secondly,the norm assimilation using agent-based system could be potentially utilized to simulate some social issues such as immigrants,new students,expatriate etc.The experiments that have been conducted demonstrate that an agent in the domain is able to calculate the assimilation cost and decide which social group to join.

Enhancing the dynamic load balancing technique for cloud computing using HBATAABC algorithm

Cloud computing brings incipient transmutations in different fields of life and consists of different characteristics and virtualization is one of them.Virtual machine(VM)is one of the main elements of virtualization.VM is a process in which physical server changes into the virtual machine and works as a physical server.When a user sends data or request for data in cloud data center,a situation can occur that may cause the virtual machines to underload data or overload data.The aforementioned situation can lead to failure of the system or delay the user task.Therefore,appropriate load balancing techniques are required to surmount the above two mentioned problems.Load balancing is a technique utilized in cloud computing for management of the resource by a condition such that a maximum throughput is achieved with slightest reaction time and additionally dividing the traffic between different servers or VM so that it can get data without any delay.For the amelioration of load balancing technique in this study,a novel technique is used which is coalescence of BAT and ABC algorithms both of which are nature-inspired algorithms.When the ABC algorithm local search section changes with BAT algorithm local search section,a second modification takes place in the fitness function of BAT algorithm.The proposed technique is known as HBATAABC algorithm.The novel technique implemented by utilizing transfer strategy policy in VM improves the performance of data allocation system of VM in the cloud data center.To check the performance of the proposed algorithm,three main parameters are used which are network average time,network stability and throughput.The performance of the proposed novel technique is verified and tested with the help of cloudsim simulator.The result shows that the suggested modified algorithm increases performance by 1.30%of network average time,network stability and throughput as compared with BAT algorithm,ABC algorithm and RRA algorithm.Nevertheless,the proposed algorithm is more precise and expeditious as compared with the three models.

Formulating layered adjustable autonomy for unmanned aerial vehicles

Purpose–The purpose of this paper is to propose a layered adjustable autonomy(LAA)as a dynamically adjustable autonomy model for a multi-agent system.It is mainly used to efficiently manage humans’and agents’shared control of autonomous systems and maintain humans’global control over the agents.Design/methodology/approach–The authors apply the LAA model in an agent-based autonomous unmanned aerial vehicle(UAV)system.The UAV system implementation consists of two parts:software and hardware.The software part represents the controller and the cognitive,and the hardware represents the computing machinery and the actuator of the UAV system.The UAV system performs three experimental scenarios of dance,surveillance and search missions.The selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant results.Findings–The results of the UAV system tests prove that segregating the autonomy of a system as multidimensional and adjustable layers enables humans and/or agents to perform actions at convenient autonomy levels.Hence,reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents,increasing humans’workload and exposing the system to disturbances.Originality/value–The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable autonomy.Assessing the autonomy within three phases of agents run cycle(taskselection,actions-selection and actions-execution)is an original idea that aims to direct agents’autonomy toward performance competency.The agents’abilities are well exploited when an incompetent agent switches with a more competent one.