Design and Implementation of Machine Learning Models and Algorithms for Flood, Drought and Frazil Prediction

Natural calamities like floods and droughts pose a significant threat to humanity, impacting millions of people each year and incurring substantial economic losses to society. In response to this challenge, this thesis focuses on developing advanced machine learning techniques to improve water height prediction accuracy that can aid municipalities in effective flood mitigation. The primary objective of this study is to evaluate an innovative architecture that leverages Long Short Term Networks - neural networks to predict water height accurately in three different environmental scenarios, i.e., frazil, droughts and floods due to snow spring melt. A distinguishing feature of our approach is the incorporation of meteorological forecast as an input parameter into the prediction model. By modeling the intricate relationships between water level data, historical meteorological data and meteorological forecasts, we seek to evaluate the impact of meteorological forecasts and if any inaccuracies could impact water-level prediction. We compare the outcomes obtained by incorporating next-hour, next-day and next-week meteorological data into our novel LSTM model. Our results indicate a comprehensive comparison of the usage of various parameters as input and our findings suggest that accurate weather forecasts are crucial in achieving reliable water height predictions. Additionally, this study focuses on the utilization of IoT sensor data in combination with ML models to enhance the effectiveness of flood prediction and management. We present an online machine learning approach that performs online training of the model using real-time data from IoT sensors. The integration of live sensor data provides a dynamic and adaptive system that demonstrates superior predictive capabilities compared to traditional static models. By adopting these advanced techniques, we can mitigate the adverse impacts of natural catastrophes and work towards building more resilient and disaster-resistant communities

Explainable Misinformation Detection Across Multiple Social Media Platforms

In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN mode. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets, namely CoAID and MiSoVac, and compared results with and without DANN implementation. DANN significantly improves the accuracy measure F1 classification score and increases the accuracy and AUC performance. The results obtained show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation enabling trustworthy information processing and extraction to combat misinformation effectively.Comment: 28 pages,4 figure

Post-colonoscopy appendicitis: a rare entity

A 35-year-old woman was admitted to the surgical ward complaining of right-sided lower abdominal pain. She had undergone colonoscopy a week previously. She was diagnosed with acute appendicitis following colonoscopy and laparoscopic appendectomy was performed via the 2-port technique. Post colonoscopy appendicitis is very rare with 14 cases reported since 1988

Upper gastrointestinal bleeding: audit of a single center experience in Western India

Upper gastrointestinal (GI) bleeding is defined as bleeding proximal to the ligament of Treitz. The most important aspect of management of GI bleeding is to locate the site and cause of bleeding. The aim of the study is to find out the common etiology, presentation and management, including the role of upper GI endoscopy. Recent advances have meant that endoscopic hemostatic methods are now associated with a reduced rate of re-bleeding, cost, blood transfusion, length of hospital stay and mortality. A prospective study of 50 cases was carried out between August 2001 and July 2003. Patients with signs and symptoms suggestive of upper GI bleeding (UGIB) such as hematemesis, melena, aspirated blood from nasogastric tubes, profuse hematochezia, etc., were included in the study. The patients were selected randomly. The most common cause of UGIB in the present study was acute erosive gastritis (34%) followed by portal hypertension (24%) and peptic ulcer (22%). All 50 patients underwent upper GI endoscopy, of whom 39 patients were treated conservatively and 11 patients underwent endotherapy to control bleeding. Out of 39 patients treated non-endoscopically, 6 cases required laparotomy to control UGIB. 8 of 50 cases had past history of UGIB, 5 of whom had a previous history of endotherapy. One case was treated with devascularization as routine hemostatic methods failed. So, initial method of choice to control the bleeding was endotherapy and surgery was undertaken if an endoscopic method failed. The most common cause of hematemesis in our setting was acute erosive gastritis followed by portal hypertension. Endoscopy is a valuable minimal invasive method to diagnose and treat upper GI bleeding

CD19-negative B-lineage acute lymphoblastic leukemia: A diagnostic and therapeutic challenge

B-lineage acute lymphoblastic leukemia (B-ALL) is an aggressive neoplasm of B-lymphocyte precursors that express the pan B-cell marker CD19 in all the cases. Rarely, a case may be assigned as B-lineage even if CD19 is negative. Here, a 16-year-old male presented with complaints of pain abdomen, on and off fever, joint pain, and hepatosplenomegaly for 2 months. Bone marrow examination was suggestive of acute leukemia with numerous leukoblasts on aspiration. On flow cytometry, gated blast population was negative for CD19, cytoCD3, and myeloperoxidase MPO and positive for CD34, TdT, HLA-DR, CD22, CD79a, and CD10. Immunohistochemistry study showed positivity for TdT, CD34, CD10 (focal), and PAX 5 and negativity for CD20, CD3, MPO, CD117, and CD68. Lack of awareness of negative CD19 expression in B-ALL can lead to incorrect immunophenotypic diagnosis, treatment, and monitoring of B-ALL. Proper diagnosis should be based on clinical features, immunophenotypic profiles, immunohistochemistry findings, and molecular analysis