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In today's world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. In the proliferation of such issues, social media has proven to be a great platform for people to express themselves. Thus, a user's social media can speak a great deal about his/her emotional state and mental health. Considering the high pervasiveness of the disease, this paper presents a novel framework for depression detection from textual data, employing Natural Language Processing and deep learning techniques. For this purpose, a dataset consisting of tweets was created, which were then manually annotated by the domain experts to capture the implicit and explicit depression context. Two variations of the dataset were created, on having binary and one ternary labels, respectively. Ultimately, a deep-learning-based hybrid Sequence, Semantic, Context Learning (SSCL) classification framework with a self-attention mechanism is proposed that utilizes GloVe (pre-trained word embeddings) for feature extraction; LSTM and CNN were used to capture the sequence and semantics of tweets; finally, the GRUs and self-attention mechanism were used, which focus on contextual and implicit information in the tweets. The framework outperformed the existing techniques in detecting the explicit and implicit context, with an accuracy of 97.4 for binary labeled data and 82.9 for ternary labeled data. We further tested our proposed SSCL framework on unseen data (random tweets), for which an F1-score of 94.4 was achieved. Furthermore, in order to showcase the strengths of the proposed framework, we validated it on the "News Headline Data set" for sarcasm detection, considering a dataset from a different domain. It also outmatched the performance of existing techniques in cross-domain validation.

Background Recently many new deep learning–based variant-calling methods like DeepVariant have emerged as more accurate compared with conventional variant-calling algorithms such as GATK HaplotypeCaller, Sterlka2, and Freebayes albeit at higher computational costs. Therefore, there is a need for more scalable and higher performance workflows of these deep learning methods. Almost all existing cluster-scaled variant-calling workflows that use Apache Spark/Hadoop as big data frameworks loosely integrate existing single-node pre-processing and variant-calling applications. Using Apache Spark just for distributing/scheduling data among loosely coupled applications or using I/O-based storage for storing the output of intermediate applications does not exploit the full benefit of Apache Spark in-memory processing. To achieve this, we propose a native Spark-based workflow that uses Python and Apache Arrow to enable efficient transfer of data between different workflow stages. This benefits from the ease of programmability of Python and the high efficiency of Arrow's columnar in-memory data transformations. Results Here we present a scalable, parallel, and efficient implementation of next-generation sequencing data pre-processing and variant-calling workflows. Our design tightly integrates most pre-processing workflow stages, using Spark built-in functions to sort reads by coordinates and mark duplicates efficiently. Our approach outperforms state-of-the-art implementations by >2 times for the pre-processing stages, creating a scalable and high-performance solution for DeepVariant for both CPU-only and CPU + GPU clusters. Conclusions We show the feasibility and easy scalability of our approach to achieve high performance and efficient resource utilization for variant-calling analysis on high-performance computing clusters using the standardized Apache Arrow data representations. All codes, scripts, and configurations used to run our implementations are publicly available and open sourced; see  https://github.com/abs-tudelft/variant-calling-at-scale. [ABSTRACT FROM AUTHOR]

VANET is the spontaneous evolving creation of a wireless network, and clustering in these networks is a challenging task due to rapidly changing topology and frequent disconnection in networks. The cluster head (CH) stability plays a prominent role in robustness and scalability in the network. The stable CH ensures minimum intra- and intercluster communication, thereby reducing the overhead. These challenges lead the authors to search for a CH selection method based on a weighted amalgamation of four metrics: befit factor, community neighborhood, eccentricity, and trust. The stability of CH depends on the vehicle's speed, distance, velocity, and change in acceleration. These all are included in the befit factor. Also, the accurate location of the vehicle in changing the model is very vital. Thus, the predicted location with the Kalman filter's help is used to evaluate CH stability. The results have shown better performance than the existing state of the art for the befit factor. The change in dynamics and frequent disconnection in communication links due to the vehicle's high speed are inevitable. To comprehend this problem, a graphing approach is used to evaluate the eccentricity and the community neighborhood. The link reliability is calculated using the eigengap heuristic. The last metric is trust; this is one of the concepts that has not been included in the weighted approach to date as per the literature. An adaptive spectrum sensing is designed for evaluating the trust values specifically for the primary users. A deep recurrent learning network, commonly known as long short-term memory (LSTM), is trained for the probability of detection with various signals and noise conditions. The false rate has drastically reduced with the usage of LSTM. The proposed scheme is tested on the real map of Chengdu, southwestern China's Sichuan province, with different vehicular mobilities. The comparative study with the individual and weighted metric has shown significant improvement in the cluster head stability during high vehicular density. Also, there is a considerable increase in network performance in energy, packet delay, packet delay ratio, and throughput. [ABSTRACT FROM AUTHOR]