Test Case Optimization and Redundancy Reduction Using GA and Neural Networks

More than 50% of software development effort is spent in testing phase in a typical software development project. Test case design as well as execution consume a lot of time. Hence, automated generation of test cases is highly required. Here a novel testing methodology is being presented to test object-oriented software based on UML state chart diagrams. In this approach, function minimization technique is being applied and generate test cases automatically from UML state chart diagrams. Software testing forms an integral part of the software development life cycle. Since the objective of testing is to ensure the conformity of an application to its specification, a test “oracle” is needed to determine whether a given test case exposes a fault or not. An automated oracle to support the activities of human testers can reduce the actual cost of the testing process and the related maintenance costs. In this paper, a new concept is being presented using an UML state chart diagram and tables for the test case generation, artificial neural network as an optimization tool for reducing the redundancy in the test case generated using the genetic algorithm. A neural network is trained by the back-propagation algorithm on a set of test cases applied to the original version of the system

Proposed T-Model to cover 4S quality metrics based on empirical study of root cause of software failures

There are various root causes of software failures. Few years ago, software used to fail mainly due to functionality related bugs. That used to happen due to requirement misunderstanding, code issues and lack of functional testing. A lot of work has been done in past on this and software engineering has matured over time, due to which software’s hardly fail due to functionality related bugs. To understand the most recent failures, we had to understand the recent software development methodologies and technologies. In this paper we have discussed background of technologies and testing progression over time. A survey of more than 50 senior IT professionals was done to understand root cause of their software project failures. It was found that most of the softwares fail due to lack of testing of non-functional parameters these days. A lot of research was also done to find most recent and most severe software failures. Our study reveals that main reason of software failures these days is lack of testing of non-functional requirements. Security and Performance parameters mainly constitute non-functional requirements of software. It has become more challenging these days due to lots of development in the field of new technologies like Internet of things (IoT), Cloud of things (CoT), Artificial Intelligence, Machine learning, robotics and excessive use of mobile and technology in everything by masses. Finally, we proposed a software development model called as T-model to ensure breadth and depth of software is considered while designing and testing of software.

RESDEN: A Novel Deep Unified Model for Face Recognition System

The Face Recognition technology plays a significant role in the field of Computer Vision in contemporary times. The research article is centered on a Facial attendance system that utilizes a deep learning technique to recognize face photos. To execute face identification and classification via the use of deep learning processes, many Convolutional Neural Network (CNN) models are taken into account. Previous studies have mostly focused on either the ResNet or DenseNet-based convolutional neural network model. The present research utilizes the merging of ResNet and DenseNet to propose a hybrid model. The proposed work is expected to provide enhanced efficiency and accuracy. In the training and testing stages of the simulation, considerations are made for both binary and category classifications. The current research focuses on the use of the LFW dataset. The pictures undergo an initial step of the noise reduction process. The evaluation of picture quality is conducted by taking into account metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). After the proposed model has undergone training, it generates photographs of superior quality. Finally, the proposed system incorporates the RESDEN framework, which integrates DenseNet with a noise reduction technique, a segmentation mechanism, and a CNN based on ResNet. A comparative analysis has been conducted to evaluate the accuracy of several filtered picture sets across different convolutional neural network (CNN) models. The simulation results indicate that the suggested model exhibited a good level of performance and accuracy

Optimized stacking ensemble for early-stage diabetes mellitus prediction

This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures

Human Face Recognition and Age Estimation with Machine Learning: A Critical Review and Future Perspective

Face Recognition (FR) applications are becoming more and more common these days. Face recognition, techniques, tools, and performance are all shown in this work, along with a literature review and gaps in many areas. Some of the most common uses of the FR include medical and government sectors as well as educational institutions. The FR technique can identify an appropriate individual through a camera. Online courses, online FDPs, and Webinars are becoming more interactive nowadays. Using Machine Learning, it is possible to quickly and securely determine a student\u27s unique id to administer virtual online tests. The paper is an analysis of Machine learning and deep learning algorithms as well as tools such as Matlab and Python. The paper covers a survey of different aspects such as face detection, face recognition, face expressions, and age estimation. Hence, this is helpful for researchers to choose the right direction for their research. Future face recognition research is also considered in the paper which is now trending in face recognition systems. Data from recent years are used to evaluate the performance

Securing IoT Networks for Detection of Cyber Attacks using Automated Machine Learning

Cybercriminals are always developing innovative strategies to confound and frustrate their victims. Therefore, maintaining constant vigilance is essential if one wishes to protect the availability, confidentiality, and integrity of digital systems. Machine learning (ML) is becoming an increasingly powerful technique for doing intelligent cyber analysis, which enables proactive defenses. Machine learning (ML) has the potential to thwart future assaults by studying the recurring patterns that have already been successful. Nevertheless, there are two significant drawbacks associated with the utilization of ML in security analysis. To begin, the most advanced machine learning systems have significant problems with their computing overheads. Because of this constraint, firms are unable to completely embrace ML-based cyber strategies. Second, in order for security analysts to make advantage of ML for a wide variety of applications, they will need to develop specialized frameworks. In this study, we aim to put a numerical value on the degree to which a hub can improve the safety of an ecosystem. Typical cyberattacks were carried out on an Internet of Things (IoT) network located within a smart house in order to validate the hub. Further investigation of the intrusion detection system's (IDS) resistance to adversarial machine learning (AML) assaults was carried out. In this method, models can be attacked by supplying adversarial samples that attempt to take advantage of the defects in the detector that are present in the pre-trained model

A Parallel Fuzzy C-Mean algorithm for Image Segmentation

This paper proposes a parallel Fuzzy C-Mean (FCM) algorithm for image segmentation. The sequential FCM algorithm is computationally intensive and has significant memory requirements. For many applications such as medical image segmentation and geographical image analysis that deal with large size images, sequential FCM is very slow. In our parallel FCM algorithm, dividing the computations among the processors and minimizing the need for accessing secondary storage, enhance the performance and efficiency of image segmentation task as compared to the sequential algorithm. such as medical image segmentation and geographical image analysis that deal with large size images, sequenrial FCM is very slow. In our parallel FCM algorithm, dividing the computations among the processors and minimizing the need for accessing secondary storage, enhance the performance and efficiency of image segmentation task as compared to the sequential algorith

ANTIBACTERIAL, ANTIOXIDANT, CHEMICAL CONSTITUENTS, AND CYTOTOXICITY EVALUATION OF TERMINALIA ARJUNA (ROXB. EX DC.) WIGHT AND ARN

ABSTRACTObjective: The objective of this study is to evaluate the in vitro antibacterial and antioxidant prospective of Terminalia arjuna (leaves). The most activeextracts were examined for their chemical composition and cytotoxicity.Methods: The antibacterial activity of five different extracts were examined against 8 bacterial strains (5 Gram-positive and 3 Gram-negative) usingresazurin-based microtiter dilution assay (RMDA) and disk-diffusion assay. The antioxidant potential of five extracts was demonstrated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay and superoxide radical scavenging assay. Chemical composition and cytotoxicity were assessed using gaschromatography-mass spectrometry (GC-MS) and hemolytic assay, respectively.Results: According to RMDA, the acetone extract (AE) exhibited highest antibacterial activity. The AE showed highest activity against Salmonellaenterica ser. typhi and Bacillus cereus with minimum inhibitory concentration, i.e., 195.31 μg/ml. In DPPH assay, AE showed the highest radicalscavenging activity with inhibition concentration50 23.09 μg/ml. In GC-MS analysis, the principal compound in AE was celidoniol (8.72 %). Accordingto the results of hemolytic assay, the AE showed non-toxic behavior upto 500 μg/ml.Conclusion: The present investigation represents T. arjuna as an incredible herb. The AE was found to possess promising antibacterial and antioxidantproperties.Keywords: Antibacterial, Antioxidant, Terminalia arjuna, Chemical composition, Cytotoxicity

IN VITRO EVALUATION OF ANTIFUNGAL POTENTIAL AND ELECTRON MICROSCOPIC STUDIES OF BACILLUS AMYLOLIQUEFACIENS AGAINST ASPERGILLUS SPECIES

Objective: Bacteria are able to synthesize a wide range of metabolites with fungicidal properties. The present study focused on the in vitro evaluation of antifungal potential of amyloliquefaciens DSM-1067 against Aspergillus spp. and its electron microscopic studies. Methods: An invitro evaluation of antifungal activity of bacterial secreted and cellular proteins was determined by microbroth dilution, disc diffusion and spore germination inhibition assays (SGIA). The cytotoxicity of these bacterial proteins was determined by hemolytic assay, and the effect of Bacillus amyloliquefaciens DSM 1067 lysate proteins on Aspergillus fumigatus was visualized by scanning electron microscopy (SEM).Results: Bacillus amyloliquefaciens DSM-1067 lysates showed the highest activity by inhibiting the growth of A. fumigatus, A. flavus completely at a concentration of 31.25 µg/ml. In vitro toxicity experiments resulted that the lysate of B. amyloliquefaciens DSM-1067was non-toxic against human erythrocytes even at high concentrations. These findings thus emphasize its usefulness in the development of new antifungal therapies. SEM analysis demonstrated the in vitro inhibition of A. fumigatus growth by B. amyloliquefaciens DSM-1067cytosolic proteins leading to wrinkled hyphae, irregular branching patterns. It also showed disruption of conidiophores development. These cytological effects of B. amyloliquefaciens DSM-1067on the hyphal growth of A. fumigatus can explain its potent anti-Aspergillus activity.Conclusion: The present investigation revealed that B. amyloliquefaciens DSM-1067 lysate protein can act as a potential candidate for exploration in the development of effective and non-toxic treatments against Aspergillus induced diseases. Its effect on the development of conidiophores and hyphal growth are studied in the present study. Â