Software Defect Prediction using Deep Learning by Correlation Clustering of Testing Metrics

The software industry has made significant efforts in recent years to enhance software quality in businesses. The use of proactively defect prediction in the software will assist programmers and white box testing in detecting issues early, saving time and money. Conventional software defect prediction methods focus on traditional source code metrics such as code complexities, lines of code, and so on. These capabilities, unfortunately, are unable to retrieve the semantics of source code. In this paper, we have presented a novel Correlation Clustering fine-tuned CNN (CCFT-CNN) model based on testing Metrics. CCFT-CNN can predict the regions of source code that contain faults, errors, and bugs. Abstract Syntax Tree (AST) tokens are extracted as testing Metrics vectors from the source code. The correlation among AST testing Metrics is performed and clustered as a more relevant feature vector and fed into Convolutional Neural Network (CNN). Then, to enhance the accuracy of defect prediction, fine-tuning of the CNN model is performed by applying hyperparameters. The result analysis is performed on the PROMISE dataset that contains samples of open-source Java applications such as Camel Dataset, Jedit dataset, Poi dataset, Synapse dataset, Xerces dataset, and Xalan dataset. The result findings show that the CCFT- CNN model increases the average F-measure by 2% when compared to the baseline model

Higher signal harmonics, LISA's angular resolution, and dark energy

It is generally believed that the angular resolution of the Laser Interferometer Space Antenna (LISA) for binary supermassive black holes (SMBH) will not be good enough to identify the host galaxy or galaxy cluster. This conclusion, based on using only the dominant harmonic of the binary SMBH signal, changes substantially when higher signal harmonics are included in assessing the parameter estimation problem. We show that in a subset of the source parameter space the angular resolution increases by more than a factor of 10, thereby making it possible for LISA to identify the host galaxy/galaxy cluster. Thus, LISA's observation of certain binary SMBH coalescence events could constrain the dark energy equation of state to within a few percent, comparable to the level expected from other dark energy missions.Comment: 15 pages, no figures. Final version to appear in Phys. Rev.

Comments on the Refractive Index of Tin Sulphide Nano-crystalline Thin Films

The refractive indices of nano-crystalline thin films of Tin (IV) Sulphide (SnS) were investigated here. The experimental data conformed well with the single oscillator model for refractive indices. Based on the this, we explain the increasing trend of refractive index to the improvement in crystal ordering with increasing grain size.Comment: Nine figure

FUZZY MULTI-OBJECTIVE LINEAR PROGRAMMING APPROACH FOR SOLVING PROBLEM OF FOOD INDUSTRY

Enterprises and industrial centers need current decision for making products in fast changing market. Uncertainty and yield defined goals make decision making more difficult. In this situation fuzzy logic is used for coping surrounding environment. This paper deals with a fuzzy linear programming model for a problem of food industry. The different types of achievement function such as compensatory and weighted compensatory form&nbsp

Effectiveness of Brahmi in Various Illnesses: Review Paper

Plants have been used as treatments for thousands of years, based on experience and folk remedies and continue to draw wide attention for their role in the treatment of mild and chronic diseases. In these eras, focus on plant research has increased all over the world and a large body of evidence has been accumulated to highlight the immense potential of medicinal plants used in various traditional systems of medicine. In various medicinal plants Centella asiatica is one of the most useful plants seen in Ayurveda medicines. Centella asiatica (commonly known as Brahmi in India) is an imperative medicinal drug which possesses significant medicinal properties, especially those involving cognition. It has been extensively known as a brain tonic that promotes cerebrum development. This herb is recommended for the treatment of various skin conditions such as leprosy, lupus, varicose ulcers, eczema, psoriasis, diarrhea, fever, amenorrhea, diseases of the female genitourinary tract and also for relieving anxiety and improving cognition. The present paper reviews Brahmi (Centella asiatica) as a medicinal plant and highlights its benefits in various health problems

Energy as the basis of harvest index

Harvest index has become a character used in plant breeding programmes and in evaluation of responses to agronomic treatments. Donald defined harvest index as the ratio between weight of grains and the weight of total dry matter, and later described it as a measure of partitioning of photo-synthates (Donald, 1968)

Smart Embedded Systems

1. The book provides a comprehensive coverage of various aspects of smart embedded systems, exploring their design, implementation, optimization, and a range of applications. This is further enhanced by in-depth discussions on hardware and software optimizations aimed at improving overall system performance. 2. A detailed examination of machine learning techniques specifically tailored for data analysis and prediction within embedded systems. This complements the exploration of cutting-edge research on the use of AI to enhance wireless communications. 3. Real-world applications of these technologies are extensively discussed, with a focus on areas such as seizure detection, noise reduction, health monitoring, diabetic care, autonomous vehicles, and communication systems. This includes a deep-dive into different wireless protocols utilized for data transfer in IoT systems. 4. This book highlights key IoT technologies and their myriad applications, extending from environmental data collection to health monitoring. This is underscored by case studies on the integration of AI and IoT in healthcare, spanning topics from anomaly detection to informed clinical decision-making. Also featured is a detailed evaluation and comparison of different system implementations and methodologie