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Background and objective: Postpartum Depression (PPD) is a frequently ignored birth-related consequence. Social network analysis can be used to address this issue because social media network serves as a platform for their users to communicate with their friends and share their opinions, photos, and videos, which reflect their moods, feelings, and sentiments. In this work, the depression of delivered mothers is identified using the PPD score and segregated into control and depressed groups. Recently, to detect depression, deep learning methods have played a vital role. However, these methods still do not clarify why some people have been identified as depressed. Methods: We have developed Attribute Selection Hybrid Network (ASHN) to detect the postpartum depression diagnoses framework. Later analysis of the post of mothers who have been confirmed with the score calculated by the experts of the field using physiological questionnaire score. The model works on the analysis of the attributes of the negative Facebook posts for Depressed user Diagnosis, which is a large general forum. This framework explains the process of analyzing posts containing Sentiment, depressive symptoms, and reflective thinking and suggests psycho-linguistic and stylistic attributes of depression in posts. Results: The experimental results show that ASHN works well and is easy to understand. Here, four attribute networks based on psychological studies were used to analyze the different parts of posts by depressed users. The results of the experiments show the extraction of psycho-linguistic markers-based attributes, the recording of assessment metrics including Precision, Recall and F1 score and visualization of those attributes were used title-wise as well as words wise and compared with daily life, depression and postpartum depressed people using Word cloud. Furthermore, a comparison to a reference with Baseline and ASHN model was carried out. Conclusions: Attribute Selection Hybrid Network (ASHN) mimics the importance of attributes in social media posts to predict depressed mothers. Those mothers were anticipated to be depressed by answering a questionnaire designed by domain experts with prior knowledge of depression. This work will help researchers look at social media posts to find useful evidence for other depressive symptoms. Highlights: A novel hybrid attribute selection model has been proposed for the prediction of Post-Partum Depression. Attribute Hybrid Networks have been tested on a unique dataset that includes both the PDSS questionnaire and social media posts of the recruited individuals. The model applies a depression theory to select each attribute using interconnected neural networks and a post-level attention layer. The experimental results demonstrate that the Attribute Selection Hybrid Network outperforms other baseline models. The study employs title- and word-wise word clouds visualization to compare daily life, depression, and postpartum depression. The proposed Attribute Selection Hybrid Networks model is also capable of predicting various mood disorders. [ABSTRACT FROM AUTHOR]

As the networking infrastructure is becoming complex, there is a parallel increase in the networking scenario to become more vulnerable to various types of sophisticated malicious attacks. This leads to the data and information loss. The most feasible way to reduce the vulnerable and malicious attacks is by evaluating trust of the interacting agents in a network. The primary objective of this paper is to analyze the flooding attacks caused due to the communication protocols like TCP and UDP in software defined spine leaf topology. The trust analysis is done using Bayesian networks for a reliable communication network. The probability and the causal effect of the denial of service in the proposed distributed networking scenario are reasoned out. The trust analysis will help to increase the confidence over a dedicated communication at the time of malicious attack over the distributed network. This analysis will enable the software defined control plane to make a crucial and needed decision over its communication with other nodes during compromised security attack through communication protocols. [ABSTRACT FROM AUTHOR]

Proactive fault diagnosis is a burning issue in condition monitoring of machines. Intelligent methods prove to be promising solutions for designing predictive analytic frameworks for fault diagnosis and machine state classification. The competency of machine learning algorithms in handling large voluminous data has marked them as a natural solution for developing intelligent framework that proactively classifies the machine states. The paper proposes a novel Context Based Multi-layered Bayesian Inferential (CBMBI) predictive analytic framework, which is motivated by MisMatch Negativity (MMN) and Predictive Coding. The CBMBI framework is augmented with a new hyperparameter (context) that greatly reduces the misclassification rate. The performance of the framework is analysed with Case Western Reserve University 6205-2RS JEM SKF dataset. The profound results reveal that the proposed framework shows 97% accuracy and 94% F1-score which is relatively higher than the state of art technique. [ABSTRACT FROM AUTHOR]

Cloud file storage systems are the current trend of enterprises and also of individual users. Due to the malicious or unauthorized users, file sharing among the cloud users has become the biggest challenge in the recent times. Attribute-based signcryption (ABSC) is known as the versatile cryptographic primitive which achieves the fine-grained access control over robust cloud storage. ABSC combines attribute-based encryption (ABE) and attribute-based signatures to achieve privacy-oriented confidentiality along with the authenticity. Unfortunately, most of the present ABE and ABSC schemes leverage heavy computational overheads against the key length, the ciphertext size or the expressive access structures used. In this paper, a new ABSC scheme is devised to reduce computational overheads, more particularly, at cloud by introducing the more expressive hypergraph access structure, Attribute HyperGraph. On a positive note, the proposed system outperforms and shows less cloud computational timing without exponentiations unlike Deng et al. (IEEE Access 6:39473–39486, 2018. 10.1109/ACCESS.2018.2843778), Liu et al. (Future Gener Comput Syst 52:67–76, 2015. 10.1016/j.future.2014.10.014) and Li et al. (IEEE Trans Parallel Distrib Syst 25(8):2201–2210, 2014. 10.1109/TPDS.2013.271) schemes. Subsequently, the system does not incur any cryptographic computations associated with designcryption at cloud unlike Deng et al. (2018). [ABSTRACT FROM AUTHOR]

Data sharing plays an essential role in many of the mobile ad-hoc network (MANET) applications that exhibits collaborative behavior. In such applications, replication is used as a foremost and fundamental technique to improve data availability. However, due to the dynamic nature of the network, data replication becomes more intricate in MANET. To alleviate this problem, we have proposed a mechanism which not only enhances data accessibility, replicates data in a minimum number of nodes, relocates shared data on the prediction of mobility of replica holder and in addition, data can be accessed by any node in a minimum number of hops. In our approach, we have prefaced mathematical concept known as minimum dominating set and sub graph centrality principle to decide the number of replicas both in static and dynamic environment. Simulation results when compared with the existing mechanisms shows that the response time or data access delay is reduced, client can access the data from the server in a minimum number of hops, and consequently the number of forwarded messages to access the data are greatly reduced thus making our network energy efficient. [ABSTRACT FROM AUTHOR]