Abstract A healthy life is essential for a happy society, however it fact that seemingly invisible diseases plague our families and people suffer. The thyroid disease falls in such category. Thyroid disorders are long-term with carefully handled illnesses, may also live stable normal lives. diagnosis, particularly an inexperienced clinician, difficult proposal. Many researchers have established various methods the diagnosis of several models prediction been developed. As other domains,...

Brain magnetic resonance images (MRI) convey vital information for making diagnostic decisions and are widely used to detect brain tumors. This research proposes a self-supervised pre-training method based on feature representation learning through contrastive loss applied unlabeled data. Self-supervised aims understand features using the raw input, which is helpful since labeled data scarce expensive. For loss-based pre-training, augmentation dataset, positive negative instance pairs fed...

The software engineering community is working to develop reliable metrics improve quality. It estimated that understanding the source code accounts for 60% of maintenance effort. Cognitive informatics important in quantifying degree difficulty or efforts made by developers understand code. Several empirical studies were conducted 2003 assign cognitive weights each possible basic control structure software, and these are used several researchers evaluate complexity systems. In this paper, an...

Software development effort estimation is one of the most major activities in software project management. A number models have been proposed to make estimations but still no single model can predict accurately. The need for accurate industry a challenge. Artificial Neural Network be used carrying out developing & this field Soft Computing suitable estimations. present paper concerned with comparing results various artificial neural network predicting estimation. available MATLAB tools were...

Deep neural network models built by the appropriate design decisions are crucial to obtain desired classifier performance. This is especially when predicting fault proneness of software modules. When correctly identified, this could help in reducing testing cost directing efforts more towards modules identified be prone. To able build an efficient deep model, it important that parameters such as number hidden layers, nodes each layer, and training details learning rate regularization methods...

The majority of conventional alcohol detection approaches are based on traditional machine learning and unable to retrieve the deeply hidden attributes alcohol-based electroencephalography (EEG) signals. intention this research is evolve a deep learning-based method for detecting alcohol-related EEG signals automatically. It also investigates whether alcoholism classification effective without using any explicit feature extraction selection steps. To assess this, paper implements compares...

Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers predict code. To achieve desired classifier performance, appropriate design decisions required for deep neural network (DNN) convolutional (CNN) models. This especially important when predicting module fault proneness....

Transfer learning attempts to use the knowledge learned from one task and apply it improve of a separate but similar task. This article proposes evaluate this technique’s effectiveness in classifying images medical domain. The presents model TrFEMNet (Transfer Learning with Feature Extraction Modules Network), for images. representations General Module (GFEM) Specific (SFEM) are input projection head classification module learn target data. aim is extract at different levels hierarchy them...

Electroencephalogram signals capture the brain electrical activity and provide factual cues to examine current condition of a person which can be efficacious understand analyze performance brain's functioning. EEG signal is used in diagnosis monitoring many brain-related diseases mental disorders such as seizure detection, sleep disorders, alcoholism, etc. The incessant uncontrolled alcohol consumption critically affect functionality inevitably lead an Alcoholic Disorder (AD). prime...

Despite recent achievements in generative image modeling, generating better quality samples from complex datasets such as ImageNet remains an illusory goal. The objective of this paper is to train Deep Convolutional Generative Adversarial Network at the well-known CIFAR10 dataset and study instabilities specific scale then test large-scale for establishment proposed DCGAN. We find that applying a pre-trained DCGAN can remove complexity also learn prior details images improve generated image....