Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

An Integrated Bipolar Picture Fuzzy Decision Driven System to Scrutinize Food Waste Treatment Technology through Assorted Factor Analysis

Food Waste(FW)is a pressing environmental concern that affects every country globally.About one-third of the food that is produced ends up as waste,contributing to the carbon footprint.Hence,the FW must be properly treated to reduce environmental pollution.This study evaluates a few available Food Waste Treatment(FWT)technologies,such as anaerobic digestion,composting,landfill,and incineration,which are widely used.A Bipolar Picture Fuzzy Set(BPFS)is proposed to deal with the ambiguity and uncertainty that arise when converting a real-world problem to a mathematical model.A novel Criteria Importance Through Intercriteria Correlation-Stable Preference Ordering Towards Ideal Solution(CRITIC-SPOTIS)approach is developed to objectively analyze FWT selection based on thirteen criteria covering the industry’s technical,environmental,and entrepreneurial aspects.The CRITIC method is used for the objective analysis of the importance of each criterion in FWT selection.The SPOTIS method is adopted to rank the alternative hassle-free,following the criteria.The proposed model offers a rank reversal-free model,i.e.,the rank of the alternatives remains unaffected even after the addition or removal of an alternative.In addition,comparative and sensitivity analyses are performed to ensure the reliability and robustness of the proposed model and to validate the proposed result.

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.

Rock Mass Quality Rating Based on the Multi-Criteria Grey Metric Space

Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy.This study develops the novel Gromov-Hausdorff distance for rock quality(GHDQR)methodology for rock mass quality rating based on multi-criteria grey metric space.It usually presents the quality of surrounding rock by classes(metric spaces)with specified properties and adequate interval-grey numbers.Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study.The Gromov-Hausdorff distance is an especially useful discriminant function,i.e.,a classifier to calculate these distances,and assess the quality of the surrounding rock.The efficiency of the developed methodology is analyzed using the Mean Absolute Percentage Error(MAPE)technique.Seven existing methods,such as the Gaussian cloud method,Discriminant method,Mutation series method,Artificial neural network(ANN),Support vector machine(SVM),Grey wolf optimizer and Support vector classification method(GWO-SVC)and Rock mass rating method(RMR)are used for comparison with the proposed GHDQR method.The share of the highly accurate category of 85.71%clearly indicates compliance with actual values obtained by the compared methods.The results of comparisons showed that the model enables objective,efficient,and reliable assessment of rock mass quality.

Calculations and Sensitivity Analysis of Chlorine-,NO_(x)-,and Bromine-Depleting Cycles of Stratospheric Ozone

This paper presents an engineering system approach using a 2D model of conservation of mass to study the dynamics of ozone and concerned chemical species in the stratosphere.By considering all fourteen photolysis,ozone-generating,and-depleting chemical reactions,the model calculated the transient,spatial changes of ozone under different physical-chemical-radiative conditions.Validation against the measured data demonstrated good accuracy,close match of our model with the observed ozone concentrations at both 20°S and 90°N locations.The deviation in the average concentration was less than 1% and in ozone profiles less than 17%.The impacts of various chlorine-(Cl),nitrogen oxides-(NO_(x)),and bromine-(Br)depleting cycles on ozone concentrations and distribution were investigated.The chlorine catalytic depleting cycle was found to exhibit the most significant impact on ozone dynamics,confirming the key role of chlorine in the problem of ozone depletion.Sensitivity analysis was conducted with levels of 25%,50%,100%,200%,and 400% of the baseline value.The combined cycles(Cl+NO_(x)+Br)showed the most significant influence on ozone behavior.The total ozone abundance above the South Pole could decrease by a small 3%,from 281 DU(Dubson Units)to 273 DU for the 25% level,or by a huge thinning of 60%to 114 DU for the 400% concentration level.When the level of chlorine gases increased beyond 200%,it would cause ozone depletion to a level of ozone hole(below 220 DU).The 2D Ozone Model presented in this paper demonstrates robustness,convenience,efficiency,and executability for analyzing complex ozone phenomena in the stratosphere.

A Stable Fuzzy-Based Computational Model and Control for Inductions Motors

In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism.According to the dynamic changes of the system,in addition to the parameters of the SMC,the parameters of the type-2 fuzzy neural network are also updated online.The conditions for guaranteeing the convergence and stability of the control system are provided.In the simulation part,in order to test the proposed method,several uncertain models and load torque have been applied.Also,the results have been compared to the SMC based on the type-1 fuzzy system,the traditional SMC,and the PI controller.The average RMSE in different scenarios,for type-2 fuzzy SMC,is 0.0311,for type-1 fuzzy SMC is 0.0497,for traditional SMC is 0.0778,and finally for PI controller is 0.0997.

Fuzzy Multi-Criteria Decision Support System for the Best Anti-Aging Treatment Selection Process through Normal Wiggly Hesitant Fuzzy Sets

This socialized environment among educated and developed people causes themto focusmore on their appearance and health,which turns them towards medical-related treatments,leading us to discuss anti-aging treatment methods for each age group,particularly for urban people who are interested in this.Some anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative effects.Five anti-aging therapies such as microdermabrasion or dermabrasion,laser resurfacing anti-aging skin treatments,chemical peels,dermal fillers for aged skin,and botox injections are considered in this study.Based on the criteria of safety risk,investment cost,customer happiness,and side effects,the optimal alternative is picked.As a result,a NormalWiggly Hesitant Pythagorean Fuzzy Set(NWHPFS)is constructed and used in Multi-Criteria Decision-Making(MCDM)using traditional wavy mathematical approaches.The entropy approach is utilized to determine weight values,and the Normal Wiggly Hesitant Pythagorean-VlseKriterijumska Optimizacija I Kompromisno Resenje(NWHPF-VIKOR)method is utilized to rank alternatives using MCDM methodologies.Sensitivity analysis and comparative analysis were performed to ensure the robustness and reliability of the proposed method.The smart final choice will undoubtedly assist Decision Makers(DM)in making the right judgments,and the MCDM approach will undoubtedly assist individuals in understanding the medicine.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

2-D Modeling and Calculations of Stratospheric Ozone and Influences of Convection, Diffusion, and Time

An engineering system approach of 2-D cylindrical model of transient mass balance calculations of ozone and other concerned chemicals along with fourteen photolysis, ozone-generating and ozone-depleting chemical reaction equations was developed, validated, and used for studying the ozone concentrations, distribution and peak of the layer, ozone depletion and total ozone abundance in the stratosphere. The calculated ozone concentrations and profile at both the Equator and a 60˚N location were found to follow closely with the measured data. The calculated average ozone concentration was within 1% of the measured average, and the deviation of ozone profiles was within 14%. The monthly evolution of stratospheric ozone concentrations and distribution above the Equator was studied with results discussed in details. The influences of slow air movement in both altitudinal and radial directions on ozone concentrations and profile in the stratosphere were explored and discussed. Parametric studies of the influences of gas diffusivities of ozone DO3 and active atomic oxygen DO on ozone concentrations and distributions were also studied and delineated. Having both influences through physical diffusion and chemical reactions, the diffusivity (and diffusion) of atomic oxygen DO was found to be more sensitive and important than that of ozone DO3 on ozone concentrations and distribution. The 2-D ozone model present in this paper for stratospheric ozone and its layer and depletion is shown to be robust, convenient, efficient, and executable for analyzing the complex ozone phenomena in the stratosphere. .

The use of aflibercept in ophthalmology:a review of randomized controlled trials

We reviewed randomized controlled trials associated with the intravitreal use of aflibercept for this article. These studies proved that aflibercept is an effective anti-vascular endothelial growth factor agent for the treatment of neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), diabetic macular edema (DME), and macular edema associated with retinal vein occlusion. The incidence of severe ocular or systemic complications after intravitreal administration of aflibercept was low.

Unusual capitate fracture with dorsal shearing pattern and concomitant carpometacarpal dislocation with a 6-year follow-up:A case report

BACKGROUND Isolated capitate fractures are rare carpal fractures.Following high-energy injuries,capitate fractures are usually associated with other carpal fractures or ligament injuries.The management of capitate fractures depends on the fracture pattern.Here,we report an unusual capitate fracture with a dorsal shearing pattern and concomitant carpometacarpal dislocation,with a 6-year follow-up.To the best of our knowledge,this fracture pattern and surgical management have not been previously reported.CASE SUMMARY A 28-year-old man presented with left-hand volar tenderness and decreased grip strength that persisted for one month after a traffic accident.Radiography showed a distal capitate fracture with carpometacarpal joint incongruence.Computed tomography(CT)revealed a distal capitate fracture with carpometacarpal joint dislocation.The distal fragment was rotated by 90°in the sagittal plane,and an oblique shearing fracture pattern was noted.Open reduction and internal fixation(ORIF)with a locking plate were performed using the dorsal approach.The imaging studies performed 3 mo and 6 years following surgery revealed a healed fracture,and the Disabilities of the Arm,Shoulder,and Hand and visual analog scale scores were significantly improved.CONCLUSION CT can detect capitate fractures with dorsal shearing pattern and concomitant carpometacarpal dislocation.ORIF using a locking plate are possible.

Emergent single-balloon enteroscopy for overt bleeding of small intestinal vascular malformation

A 28-year-old man presented with anemia symptoms and intermittent tarry stool passage for three days. No stigmata of hemorrhage were identified using esophagogastroduodenoscopy, ileocolonoscopy, and contrast-enhanced computed tomography. He then developed massive tarry stool passage with profound hypovolemic shock and hypoxic respiratory failure. Emergent angiography revealed active bleeder, probably from the jejunal branches of the superior mesenteric artery, but embolization was not performed due to possible subsequent extensive bowel ischemia. His airway was secured via endotracheal intubation with ventilator support, and emergent antegrade singleballoon enteroscopy was performed at 8 h after clinical overt bleeding occurrence; the procedure revealed a 2-cm pulsating subepithelial tumor with a protrudingblood plug at the distal jejunum. Laparoscopic segmental resection of the jejunum with end-to-end anastomosis was performed after emergent endoscopic tattooing localization. Pathological examination revealed a vascular malformation in the submucosa with an organizing thrombus. He was uneventfully discharged 5 d later. This case report highlights the benefit of early deep enteroscopy for the treatment of small intestinal bleeding.

Microbial diversity of soil bacteria in agricultural field contaminated with heavy metals

In this study we evaluated the bacterial diversity in a soil sample from a site next to a chemical industrial factory previously contaminated with heavy metals. Analysis of 16S rDNA sequences amplified from DNA directly extracted from the soil revealed 17 different bacterial types （genera and/or species）. They included Polyangium spp., Sphingomonas spp., Variovorax spp., Hafina spp., Clostridia, Acidobacteria, the enterics and some uncultured strains. Microbes able to tolerate high concentrations of cadmium （500μmol/L and above） were also isolated from the soil. These isolates included strains of Acinetobacter （strain CD06）, Enterobacter sp. （strains CD01, CD03, CD04 and CD08） （similar strains also identified in culture-independent approach） and a strain of Stenotrophomonas sp. The results indicated that the species identified from direct analysis of 16S rDNA of the soil can be quite different from those strains obtained from enrichment cultures and the microbial activities for heavy metal resistance might be more appropriately addressed by the actual isolates.

Strain measurement of fiber optic sensor surface bonding on host material

Fiber optic sensor has been widely used as a structural health monitoring device by either embedding into or surface bonding onto the structures. The strain of optic fiber induced by the host material is strongly dependent on the bonding characteristics which include the protective coating, adhesive layer and the length of bonding. The strains between the fiber optics and host structure are not exact the same. The existence of the protective coating and adhesive layer would affect the strain measured by the surface bonding optic sensor. The analytical expression of the strain in the optic fiber induced by the host material was presented. The results were validated by the finite element method. The theoretical predictions reveal that the strain in the optical fiber is lower than the strain of host material. Parametric study shows that a long bonding length and high modulus of protective coating would increase the percentage of strain transferring into the optical fiber. Experiments were conducted by using Mach-Zehnder interferometer to measure the strain of the surface bonding optic fiber induced by the host structure. Good agreements were observed in comparison with the experimental results and theoretical predictions.

Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

The Role of R&D Leverage as a Dynamic Capability in Performance Creation:Evidence from SMEs in Taiwan

This study addresses the role of R&D leverage in SMEs’performance creation.The authors do so by considering SMEs’high resource dependence due to isomorphism.We propose that R&D leverage,with a presence of dynamic capabilities,plays a moderating role in the relation between resource investments and performance.This study,which focused on Taiwan’s SMEs,conducts a questionnaire survey using the hierarchical sampling technique,across various industries and geographic areas in Taiwan.The empirical findings reveal that R&D leverage as an essential leveler in resource management enhances resource advantages.

A Review Study of Wireless Sensor Networks and Its Security

The confluence of cheap wireless communication, sensing and computation has produced a new group of smart devices and by using thousands of these kind of devices in self-organizing networks has formed a new technology that is called wireless sensor networks (WSNs). WSNs use sensor nodes that placed in open areas or in public places and with a huge number that creates many problems for the researchers and network designer, for giving an appropriate design for the wireless network. The problems are security, routing of data and processing of large amount of data etc. This paper describes the types of WSNs and the possible solutions for tackling the listed problems and solution of many other problems. This paper will deliver the knowledge about the WSN and types with literature review so that a person can get more knowledge about this emerging field.

A Study of Participation Motivation, Experience and Satisfaction in Camping Tourists

In recent years, camping is popular in Taiwan, but the research on campers’ participation behavior is still insufficient. The purpose of this research is to analyze the structural relationship between camping tourists’ motivation, experience, and satisfaction. The survey was conducted in a convenient sampling method, and a total of 464 valid questionnaires were collected. After descriptive statistics and partial least squares (PLS) statistics analysis, the results show that the higher the motivation of camping tourists, the more positive their experience and the higher their satisfaction. In addition, this research also confirms that experience has a mediating effect on motivation and satisfaction in terms of academic theory.

Media Sharing Scheme with Cheater Identification and Content Reconstruction

For digital communication, distributed storage and management of media contents over system holders are critical issues. In this article, an efficient verifiable sharing scheme is proposed that can satisfy significant essentials of distribution sharing and can achieve a iossless property of host media. Verifiability allows holders to detect and identify counterfeited shadows during cooperation in order to prevent cheaters. Only authorized holders can reveal the lossless shared content and then reconstruct the original host image. Shared media capacity is adjustable and proportional to the increase of the number of the distributed holders t. The more distributed holders, the larger the shared media capacity is. Moreover, the ability to reconstruct the image preserves the fidelity of valuable host media, such as military and medical images. According to the results, the proposed approach can achieve superior performance to that of related sharing schemes for effectively providing distributed media management and storage.

Multi scale risk measurement in electricity market:a wavelet based value at risk approach

Value at risk (VaR) is adopted to measure the risk level in the electricity market. To estimate VaR at higher accuracy and reliability, the wavelet variance decomposed approach for value at risk estimates (WVDVaR) is proposed. Empirical studies conduct in five Australian electricity markets, which evaluate the performances of both the proposed approach and the traditional ARMA-GARCH approach using the Kupiec backtesting procedure. Experimental results suggest that the proposed approach measures electricity market risks at higher accuracy and reliability than the bench mark ARMA-GARCH approach, as indicated by the higher p values during the Kupiec backtesting procedure. In addition, the new approach also provides more insight into the risk evolution process over time and helps in adjusting VaR estimates to the time horizons that best suit investor interests. The distribution of risk according to investor preferences is shown by decomposing VaR across different time horizons. This also provides important information for the appropriate aggregation of risk measures based on investor investment preferences.