Review of ML and AutoML Solutions to Forecast Time-Series Data.

Time-series forecasting is a significant discipline of data modeling where past observations of the same variable are analyzed to predict the future values of the time series. Its prominence lies in different use cases where it is required, including economic, weather, stock price, business development, and other use cases. In this work, a review was conducted on the methods of analyzing time series starting from the traditional linear modeling techniques until the automated machine learning (AutoML) frameworks, including deep learning models. The objective of this review article is to support identifying the time-series forecasting challenge and the different techniques to meet the challenge. This work can be additionally an assist and a reference for researchers and industries demanding to use AutoML to solve the problem of forecasting. It identifies the gaps of the previous works and techniques used to solve the problem of forecasting time series.

An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding.

The automated identification of toxicity in texts is a crucial area in text analysis since the social media world is replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which caused an inaccurate classification of toxic words in context. In this paper, several approaches for locating toxicity in texts are assessed and presented aiming to enhance the overall quality of text classification. General unsupervised methods were used depending on the state-of-art models and external embeddings to improve the accuracy while relieving bias and enhancing F1-score. Suggested approaches used a combination of long short-term memory (LSTM) deep learning model with Glove word embeddings and LSTM with word embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT), respectively. These models were trained and tested on large secondary qualitative data containing a large number of comments classified as toxic or not. Results found that acceptable accuracy of 94% and an F1-score of 0.89 were achieved using LSTM with BERT word embeddings in the binary classification of comments (toxic and nontoxic). A combination of LSTM and BERT performed better than both LSTM unaccompanied and LSTM with Glove word embedding. This paper tries to solve the problem of classifying comments with high accuracy by pertaining models with larger corpora of text (high-quality word embedding) rather than the training data solely.

A diagnostic testing for people with appendicitis using machine learning techniques.

Appendicitis is a common disease that occurs particularly often in childhood and adolescence. The accurate diagnosis of acute appendicitis is the most significant precaution to avoid severe unnecessary surgery. In this paper, the author presents a machine learning (ML) technique to predict appendix illness whether it is acute or subacute, especially between 10 and 30 years and whether it requires an operation or just taking medication for treatment. The dataset has been collected from public hospital-based citizens between 2016 and 2019. The predictive results of the models achieved by different ML techniques (Logistic Regression, Naïve Bayes, Generalized Linear, Decision Tree, Support Vector Machine, Gradient Boosted Tree, Random Forest) are compared. The covered dataset are 625 specimens and the total of the medical records that are applied in this paper include 371 males (60.22%) and 254 females (40.12%). According to the dataset, the records consist of 318 (50.88%) operated and 307 (49.12%) unoperated patients. It is observed that the random forest algorithm obtains the optimal result with an accurately predicted result of 83.75%, precision of 84.11%, sensitivity of 81.08%, and the specificity of 81.01%. Moreover, an estimation method based on ML techniques is improved and enhanced to detect individuals with acute appendicitis.

Cochlear Schwannoma: A Usual Pathology in an Unusual Site.

Intra-labyrinthine schwannomas are rare. We present a case of a 50-year-old male with non-serviceable unilateral sensorineural hearing loss and tinnitus. CE-MRI revealed an enhancing signal in the basal turn of left cochlea suggestive of a schwannoma. A trans-mastoid standard facial recess approach was used for tumor excision. At one year follow up, patient is disease free.

Experience with Systemic Bevacizumab in Advanced Juvenile Recurrent Respiratory Papillomatosis.

Vascular Endothelial Growth Factor has been demonstrated in squamous papillomas of Recurrent Respiratory Papillomatosis patients. This case series aimed at studying the feasibility and efficacy of systemic use of VEGF inhibitor Bevacizumab in advanced Juvenile Onset Recurrent Respiratory Papillomatosis (JORRP) patients. Three pediatric patients with advanced RRP were included in this study. A detailed bronchoscopic and radiological follow-up is presented. All patients responded well to the treatment. We conclude that systemic Bevacizumab can be tried as a feasible and rational adjuvant treatment in advanced JORRP patients.

Isolated Laryngeal Leishmaniasis: A Diagnostic Dilemma.

Isolated laryngeal Leishmaniasis is a rare entity in the Indian subcontinent. We describe a case of a 45 year old male with hoarseness and noisy breathing. Patient's initial histological and serological workup was inconclusive. Final biopsy findings (suggestive of Leishmania donovani), positive rK-39 serology and his native place being Bihar (endemic for Leishmaniasis) led us to the diagnosis. He was treated with high dose liposomal Amphotericin B to which he responded well. This case report highlights the importance of remaining aware of rare infectious causes of laryngitis. Timely diagnosis and intervention are crucial.

Scedosporium apiospermum as a rare cause of fungal rhinosinusitis.

Fungal rhinosinusitis is not very uncommon in diabetic patients, but Scedosporium apiospermum as a cause of this infection is rare. We report a case of fungal rhinosinusitis by Scedosporium spp. in a diabetic male along with literature review. The patient is on voriconazole, with adequate therapeutic response after 6 months of follow up.

Invasive Mucormycosis in a Case of Aluminium Phosphide Poisoning.

Mucormycosis is a rapidly progressive disease with high mortality reported mostly in immunocompromised individuals. We report a case of Rhino-orbital mucormycosis (Lichtheimia corymbifera) in an immunocompetent individual with history of consumption of Aluminium Phosphide (ALP) tablets. We postulated the following effects of ALP poisoning that would have increased the chances of mucormycosis in this patient: 1) Metabolic acidosis; 2) Acute Kidney Injury (AKI); and 3) Liberation of free oxygen radicals.

The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization.

Liver cirrhosis is considered as one of the most common diseases in healthcare. The widely accepted technology for the diagnosis of liver cirrhosis is via ultrasound imaging. This paper presents a technique for detecting the cirrhosis of liver through ultrasound images. The region of interest has been selected from these ultrasound images and endorsed from a radiologist. The identification of liver cirrhosis is finally detected through modified local binary pattern and OTSU methods. Experimental results from the proposed method demonstrated its feasibility and applicability for high performance cirrhotic liver identification.