Performance of machine learning algorithms for lung cancer prediction: a comparative approach.

Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stage. Hence, clinical records may play a vital role to diagnose this disease at the correct stage for suitable medication to cure it. To detect lung cancer an accurate prediction method is needed which is significantly reliable. In the digital clinical record era with advancement in computing algorithms including machine learning techniques opens an opportunity to ease the process. Various machine learning algorithms may be applied over realistic clinical data but the predictive power is yet to be comprehended for accurate results. This paper envisages to compare twelve potential machine learning algorithms over clinical data with eleven symptoms of lung cancer along with two major habits of patients to predict a positive case accurately. The result has been found based on classification and heat map correlation. K-Nearest Neighbor Model and Bernoulli Naive Bayes Model are found most significant methods for early lung cancer prediction.

Subacute invasive pulmonary aspergillosis in a nondiabetic immunocompetent female suffering from allergic broncho pulmonary aspergillosis.

A 55-year-old female presented with recent exacerbation of the chronic cough, dyspnea, and copious expectoration. The symptoms worsened during the winter months. In the past, she was misdiagnosed with pulmonary tuberculosis. A computed tomography scan revealed bronchiectasis changes, high attenuated mucus, and hypereosinophilia. The diagnosis of Allergic Bronchopulmonary Aspergillosis (ABPA) with subacute invasion was confirmed through bronchoscopy and fungal culture. Treatment with oral voriconazole significantly improved lung function and quality of life. This case highlights the importance of considering invasive pulmonary aspergillosis in patients with exacerbations of asthma and bronchiectasis. Early diagnosis and appropriate treatment are essential for improved outcomes in such cases.

Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter.

The satellite communication is embellished constantly by providing information, ensuring security, and enables the communication among huge at a particular time efficiently. The satellite navigation helps in determining the people's location. Global development, natural disasters, change in climatic conditions, agriculture crop growth, etc., are monitored using satellite observation. Hence, the satellite includes detailed information data, and it must be protected confidentially. The field of the satellite is enhanced at an astonishing pace. Satellite data play an important role in this modern world; hence, the onboard-satellite data must secure through the proper selection of error detection and estimation schema. Lightweight deep learning algorithm based on Extended Kalman Filter (KFK) is proposed to detect and estimate onboard pointing error such as an error in attitude and orbit. The Extended Kalman Filter (EKF) is widely used in the satellite system. EKF is utilized in this proposed model to detect the onboard pointing error such as attitude and orbit determination. An autonomous estimation of orbit position is possible through space-borne gravity. The information obtained through the observation of satellite data is compared with the accurate gravity model in detecting the error. The utilization of EKF reduces the dependence of the ground tracking system in satellite determination. The orbital altitude and orbital position are the most important challenges faced in the satellite determination system. The satellite model using the Extended Kalman Filter is an optimum method in estimating the orbital parameters. The errors in the linearization process are detected, and this can be overcome through the proper selection of linear expansion point with the EKF algorithmic model with the Jacobian matrix calculation. The results show that the EKF implementation helps in attaining better accuracy than other methodologies. Its contribution is enormous to many space missions, autonomous rendezvous and docking for manned and unmanned missions (e.g., ISS operations and beyond, in-orbit servicing, and in-orbit refueling), routine satellite OD operations, orbital debris removal systems, Space Situational Awareness (SSA) operations, and others.

Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model.

Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method's accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.

A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.

Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human's life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis. (1) Background: To capture these sporadic events, an electrocardiogram (ECG), a register containing the heart's electrical function, is considered the gold standard. However, since ECG carries a vast amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. As a result, designing an efficient (automated) system to analyse the enormous quantity of data possessed by ECG is critical. (2) Method: This paper proposes a hybrid deep learning-based approach to automate the detection and classification process. This paper makes two-fold contributions. First, 1D ECG signals are translated into 2D Scalogram images to automate the noise filtering and feature extraction. Then, based on experimental evidence, by combining two learning models, namely 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) network, a hybrid model called 2D-CNN-LSTM is proposed. (3) Result: To evaluate the efficacy of the proposed 2D-CNN-LSTM approach, we conducted a rigorous experimental study using the widely adopted MIT-BIH arrhythmia database. The obtained results show that the proposed approach provides ≈98.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR), respectively. Moreover, it provides an average sensitivity of the proposed model of 98.33% and a specificity value of 98.35%, for all three arrhythmias. (4) Conclusions: For the classification of arrhythmias, a robust approach has been introduced where 2D scalogram images of ECG signals are trained over the CNN-LSTM model. The results obtained are better as compared to the other existing techniques and will greatly reduce the amount of intervention required by doctors. For future work, the proposed method can be applied over some live ECG signals and Bi-LSTM can be applied instead of LSTM.

Designing Efficient Sinkhole Attack Detection Mechanism in Edge-Based IoT Deployment.

The sinkhole attack in an edge-based Internet of Things (IoT) environment (EIoT) can devastate and ruin the whole functioning of the communication. The sinkhole attacker nodes ( S H A s) have some properties (for example, they first attract the other normal nodes for the shortest path to the destination and when normal nodes initiate the process of sending their packets through that path (i.e., via S H A ), the attacker nodes start disrupting the traffic flow of the network). In the presence of S H A s, the destination (for example, sink node i.e., gateway/base station) does not receive the required information or it may receive partial or modified information. This results in reduction of the network performance and degradation in efficiency and reliability of the communication. In the presence of such an attack, the throughput decreases, end-to-end delay increases and packet delivery ratio decreases. Moreover, it may harm other network performance parameters. Hence, it becomes extremely essential to provide an effective and competent scheme to mitigate this attack in EIoT. In this paper, an intrusion detection scheme to protect EIoT environment against sinkhole attack is proposed, which is named as SAD-EIoT. In SAD-EIoT, the resource rich edge nodes (edge servers) perform the detection of different types of sinkhole attacker nodes with the help of exchanging messages. The practical demonstration of SAD-EIoT is also provided using the well known NS2 simulator to compute the various performance parameters. Additionally, the security analysis of SAD-EIoT is conducted to prove its resiliency against various types of S H A s. SAD-EIoT achieves around 95 . 83 % detection rate and 1 . 03 % false positive rate, which are considerably better than other related existing schemes. Apart from those, SAD-EIoT is proficient with respect to computation and communication costs. Eventually, SAD-EIoT will be a suitable match for those applications which can be used in critical and sensitive operations (for example, surveillance, security and monitoring systems).

Correlation of Severity of Functional Gastrointestinal Disease Symptoms with that of Asthma and Chronic Obstructive Pulmonary Disease: A Multicenter Study.

INTRODUCTION: There is a growing clinical awareness about the influence of gut-lung axis on lung injury and coexisting manifestations of disease processes in both the intestine and lungs. Patients of chronic lung diseases such as chronic obstructive pulmonary disease (COPD) and asthma very often present with coexistent gut symptoms. In the present study, we have tried to establish the correlation of severity of pulmonary pathology of COPD and asthma patients with functional gastrointestinal (GI) symptoms of the patients. MATERIALS AND METHODS: This is a prospective, questionnaire-based study comprising patients with asthma and COPD. After following strict inclusion and exclusion criteria, a total of 200 patients (100 patients of bronchial asthma and 100 patients of COPD) were included in the study. Functional GI symptom questionnaire [Annexure 1-Bowel Disease Questionnaire] is based on ROME III diagnostic criteria. On the basis of GOLD (Global Initiative for Obstructive Lung Disease) guidelines, COPD patients were divided into 4 categories (mild - GOLD 1, moderate - GOLD2, severe - GOLD3 and very severe - GOLD4). Asthma patients were divided into three categories (well controlled, partly controlled, uncontrolled) on the basis of GINA (Global Initiative for Asthma) guidelines. RESULTS: Highest percentage of patients with maximum GI symptoms was found in "GOLD-4" group among COPD patients and "uncontrolled" group among asthma patients. Highest percentage of patients with least GI symptoms was found in "GOLD-1" group among COPD patients and "well controlled" group among asthma patients. CONCLUSION: We can conclude from our study that the phenomenon of gut-lung axis not only exists but also the severity of symptoms of one system (gut) carries a high degree of concordance with severity of other (lung).

A rare case of primary tuberculous osteomyelitis of skull vault.

In recent years, there has been a substantial increase in number of reports involving uncommon sites involving in tuberculosis. Rise in number of HIV positive patients has made the scenario worse. Calvarial tuberculosis has beenreported very rarely in world literature till now. We are reporting a case of primary tuberculous osteomyelitis of frontal bone in a 15-year-old female. With prompt as well as careful diagnostic workup and treatment, we were able to halt the disease progression and excellent response to treatment was observed in follow up.