TLDR: Yoga has become essential for health, but human pose estimation, particularly with the complex Yoga-82 dataset, poses challenges. By fine-tuning VGG-16, ResNet, and DenseNet-121 models and applying Neural Architecture Search, DenseNet-121 achieved the best results with 85% top-1 accuracy and 96% top-5 accuracy, outperforming the current state-of-the-art.

Abstract:
Yoga has recently become an essential aspect of human existence for maintaining a healthy body and mind. People find it tough to devote time to the gym for workouts as their lives get more hectic and they work from home. This kind of human pose estimation is one of the notable problems as it has to deal with locating body key points or joints. Yoga-82, a benchmark dataset for large-scale yoga pose recognition with 82 classes, has challenging positions that could make precise annotations impossible. We have used VGG-16, ResNet-50, ResNet-101, and DenseNet-121 and finetuned them in different ways to get better results. We also used Neural Architecture Search to add more layers on top of this pre-trained architecture. The experimental result shows the best performance of DenseNet-121 having the top-1 accuracy of 85% and top-5 accuracy of 96% outperforming the current state-of-the-art result.

TLDR: The study introduces SCGNet, a deep learning architecture for intrusion detection, achieving 99.76% accuracy in network attack detection and 98.92% in attack type classification on the NSL-KDD dataset. The proposed system addresses the limitations of traditional IDSs in detecting complex and low-frequency attacks. A general data preprocessing pipeline is also introduced, applicable to similar datasets, alongside comparisons with conventional machine-learning methods.

Abstract:
Intrusion detection system (IDS) is a piece of hardware or software that looks for malicious activity or policy violations in a network. It looks for malicious activity or security flaws on a network or system. IDS protects hosts or networks by looking for indications of known attacks or deviations from normal behavior (Network-based intrusion detection system, or NIDS for short). Due to the rapidly increasing amount of network data, traditional intrusion detection systems (IDSs) are far from being able to quickly and efficiently identify complex and varied network attacks, especially those linked to low-frequency attacks. The SCGNet (Stacked Convolution with Gated Recurrent Unit Network) is a novel deep learning architecture that we propose in this study. It exhibits promising results on the NSL-KDD dataset in both task, network attack detection, and attack type classification with 99.76% and 98.92% accuracy, respectively. We have also introduced a general data preprocessing pipeline that is easily applicable to other similar datasets. We have also experimented with conventional machine-learning techniques to evaluate the performance of the data processing pipeline.

TLDR:This paper develops a smart music recommendation system that uses both demographic data and psychological factors, like mood and personality, to suggest music genres. Ensemble algorithms performed best for "Happy" and "Gloomy" moods, while semi-supervised algorithms excelled for "Stressed" and "Relaxed" moods.

Abstract:
Individual music preferences can generally depend on severalfactors, namely demographic data such as gender, age, etc., and morespecific psychological factors like mood and personality. However, whilemainstream music applications tend to consider the former, they usuallyignore psychological factors that would otherwise allow for more accu-rate recommendations. In this paper, we attempt to develop a smartmusic recommendation system by comparing various machine-learningalgorithms to recommend a genre based on the user’s current mood,which is determined using a psychological scale, along with their basicdemographic data. A custom dataset was built, where the TIPI scalewas used to identify users’ personality types, which we then fed into themodels to generate recommendations. Based on the results, ensemble al-gorithms performed the best for the moods of “Happy” and “Gloomy”,while semi-supervised algorithms achieved the best scores for the moodsof “Stressed” and “Relaxed (PDF) Music Recommendation System Using Psychological Scale.

TLDR: This research utilizes transfer learning with ResNet-50v2 and DenseNet-201 CNN architectures to detect potato diseases, particularly early and late blight. The study achieved state-of-the-art results across three different datasets, showcasing the potential for automated disease detection in potato crops.

Abstract:
Potato, a globally significant food crop ranking as the fourth largest by production, is cultivated in various regions worldwide. However, potato crops are notably susceptible to fungal infections, leading to the occurrence of early blight and late blight diseases. Timely disease control and management measures are pivotal in augmenting crop yields and mitigating agricultural losses for farmers. The capacity to automatically discern diseased crops holds substantial promise for farmers. Consequently, this research endeavors to present the power of transfer learning in SoTA Convolutional Neural Network (CNN) architecture ResNet-50v2 and DenseNet-201 which are finetuned to the task of potato disease detection. In our experimental endeavors, we employed three distinct datasets, and in each instance, we attained state-of-the-art results.