The poultry industry contributes majorly to the food industry. The demand for poultry chickens raises across the world quality concerns of the poultry chickens. The quality measures in the poultry industry contribute towards the production and supply of their eggs and their meat. With the increasing demand for poultry meat, the precautionary measures towards the well-being of the chickens raises the concerns of the industry stakeholders. The modern technological advancements help the poultry industry in monitoring and tracking the health of poultry chicken. These advancements include the identification of the chickens’ sickness and well-being using video surveillance, voice observations, ans feces examinations by using IoT-based wearable sensing devices such as accelerometers and gyro devices. These motion-sensing devices are placed over a chicken and transmit the chicken’s movement data to the cloud for further analysis. Analyzing such data and providing more accurate predictions about chicken health is a challenging issue. In this paper, an IoT based predictive service framework for the early detection of diseases in poultry chicken is proposed. The proposed study contributes by extending the dataset through generating the synthetic data using Generative Adversarial Networks (GAN). The experimental results classify the sick and healthy chicken in a poultry farms using machine learning classification modeling on the synthetic data and the real dataset. Theoretical analysis and experimental results show that the proposed system has achieved an accuracy of 97%. Moreover, the accuracy of the different classification models are compared in the proposed study to provide more accurate and best performing classification technique. The proposed study is mainly focused on proposing an Industrial IoT-based predictive service framework that can classify poultry chickens more accurately in real time.

In COVID-19 related infodemic, social media becomes a medium for wrongdoers to spread rumors, fake news, hoaxes, conspiracies, astroturf memes, clickbait, satire, smear campaigns, and other forms of deception. It puts a tremendous strain on society by damaging reputation, public trust, freedom of expression, journalism, justice, truth, and democracy. Therefore, it is of paramount importance to detect and contain unreliable information. Multiple techniques have been proposed to detect fake news propagation in tweets based on tweets content, propagation on the network of users, and the profile of the news generators. Generating human-like content allows deceiving content-based methods. Network-based methods rely on the complete graph to detect fake news, resulting in late detection. User profile-based techniques are effective for bots or fake accounts detection. However, they are not suited to detect fake news from original accounts. To deal with the shortcomings in existing methods, we introduce a source-based method focusing on the news propagators’ community, including posters and re-tweeters to detect such contents. Propagators are connected using follower-following relations. A feature set combining the connectivity patterns of news propagators with their profile features is used in a machine learning framework to perform binary classification of tweets. Complex network measures and user profile features are also examined separately. We perform an extensive comparative analysis of the proposed methodology on a real-world COVID-19 dataset, exploiting various machine learning and deep learning models at the community and node levels. Results show that hybrid features perform better than network features and user features alone. Further optimization demonstrates that Ensemble’s boosting model CATBoost and deep learning model RNN are the most effective, with an AUC score of 98%. Furthermore, preliminary results show that the proposed solution can also handle fake news in the political and entertainment domain using a small training set.

Social media microblogs are extensively used to get news and other information. It brings the real challenge to distinguish that what particular information is credible. Especially when user authenticity is hidden, due to the microblog’s anonymity feature. Low credibility content creates an imbalance in society. Therefore many research studies are conducted to assess automatic microblog’s credibility but the majority of them offer different concepts of credibility and the problem seems unresolved. Credibility is multi-disciplinary, hence there is no generalized or accepted credibility concept with all its necessary and detailed constructs/components. Therefore, it is necessary to understand the complete anatomy of information credibility from different disciplines. It is accomplished here through an in-depth and organized study of all the problem dimensions for the identification of comprehensive and necessary credibility constructs. The framework is also proposed based on the identified constructs. It adheres to these constructs and presents their inter-relationships. It is believed that the framework would provide the necessary building blocks for implementing an effective automatic credibility assessment system. The framework is generic to social media and specifically implemented for microblogs. It is completely transformed up to features level, in the context of microblogs. Regarding automatic credibility assessment, it is proposed after detailed analysis that the attempt should be made for hybrid models combining feature-based and graph-based approaches. It is observed that quite a few surveys in the literature focus on some limited aspects of microblogs credibility but no literature survey and fundamental study exists that consolidates the work done. To understand the broader domain of credibility and consolidate the work in this area that can lead us to a suitable framework, we explored the existing literature from different disciplines for the said objectives. We categorized them along various dimensions, developed taxonomy, identified gaps and challenges, proposed a solution, developed a theory-driven framework with its transformation to microblogs, and suggested key areas of research.

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs’ fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R²). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R² of 0.929.