Predicting catastrophic temperature changes based on past events via a CNN-LSTM regression mechanism

Neural Computing and Applications - The modelling and prediction of extreme temperature changes in enclosed compartments is a domain with applications ranging from residential fire alarms,...     

Deep Learning Approach for Analysis and Characterization of COVID-19

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images of patients, developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases, while 397 belong to negative cases of COVID-19. Our experiment resulted in an accuracy of 98.4%, sensitivity of 98.5%, specificity of 98.3%, precision of 97.1%, and F1-score of 97.8%. The additional parameters of classification error, mean absolute error (MAE), root-mean-square error (RMSE), and Matthew’s correlation coefficient (MCC) are used to evaluate our proposed work. The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.     

Influence of gamma irradiation on structural,optical, and electrical characterization of Bi2S3 thin films

The induced effects of the gamma rays on properties of bismuth sulfide (Bi₂S₃) thin films synthesized using successive ionic layer adsorption and reaction (SILAR) have been investigated in details in this work. The Bi₂S₃ thin films are prepared on glass substrate and then exposed with low gamma radiation dose in the range of 0–1000 Gy. X-ray diffraction (XRD) confirmed the orthorhombic structural phase. Also, it was noticed in the XRD result that the crystallite size decreased from 115.29 to 73.63 nm with increasing gamma rays doses. For surface properties as well as stoichiometry of the prepared and irradiated thin film have been studied by field emission scanning electron microscope (FESEM). The optical transmission of irradiated samples increased and the energy band gap (E) decreased from 2.78 to 2.52 eV with gamma dose. Photoluminescence (PL) spectra revealed the improvement in the emission characteristics of Bi₂S₃ thin films with irradiation in the range of 250–1000 Gy. Impedance spectroscopy investigation exhibited that the resistance due to grain boundaries meaningfully contributed to the electrical characteristics of the Bi₂S₃ thin films. The achieved results suggested that Bi₂S₃ thin films are a good tool for further study of dosimetry and radiation sensing application.

     

Towards a better understanding of annotation tools for medical imaging: a survey

Multimedia Tools and Applications - Medical imaging refers to several different technologies that are used to view the human body to diagnose, monitor, or treat medical conditions. It requires...     

Collective control over sensitive video data using secret sharing

Today digital video is used extensively in many applications. Sometimes a video could be treated as a top secret for an organization, for example military secrets, surveillance footage and corporate product designs, and may need to be shared among a group of people in a secure manner. Traditional data security methods such as encryption techniques are prone to single-point attack, i.e. the secret can be revealed by obtaining the decryption key from any single person. Alternatively, the secret sharing scheme provides collective control over the secrecy of information and is considered information theoretically secure. In this paper, we propose to adopt a secret sharing based approach to provide collective control over a given sensitive video. We present three methods that utilize the spatial and temporal redundancy in videos in different ways. We analyze the security of these methods and compare them for efficiency in terms of computation time and space using extensive experimentation.     

A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering

Mobile Networks and Applications - As per recent developments in medical science, the skin cancer is considered as one of the common type disease in human body. Although the presence of melanoma is...     

Emergency Prioritized and Congestion Handling Protocol for Medical Internet of Things

Medical Internet of Things (MIoTs) is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body. The healthcare networks transmit continuous data monitoring for the patients to survive them independently. There are many improvements in MIoTs, but still, there are critical issues that might affect the Quality of Service (QoS) of a network. Congestion handling is one of the critical factors that directly affect the QoS of the network. The congestion in MIoT can cause more energy consumption, delay, and important data loss. If a patient has an emergency, then the life-critical signals must transmit with minimum latency. During emergencies, the MIoTs have to monitor the patients continuously and transmit data (e.g., ECG, BP, heart rate, etc.) with minimum delay. Therefore, there is an efficient technique required that can transmit emergency data of high-risk patients to the medical staff on time with maximum reliability. The main objective of this research is to monitor and transmit the patient’s real-time data efficiently and to prioritize the emergency data. In this paper, Emergency Prioritized and Congestion Handling Protocol for Medical IoTs (EPCP_MIoT) is proposed that efficiently monitors the patients and overcome the congestion by enabling different monitoring modes. Whereas the emergency data transmissions are prioritized and transmit at SIFS time. The proposed technique is implemented and compared with the previous technique, the comparison results show that the proposed technique outperforms the previous techniques in terms of network throughput, end to end delay, energy consumption, and packet loss ratio.     

Deep learning model with ensemble techniques to compute the secondary structure of proteins

The Journal of Supercomputing - Protein secondary structure is the local conformation assigned to protein sequences with the help of its three-dimensional structure. Assigning the local...     

The analysis of two-way linearly tapered columns

Based on the analytical method and the algorithm which were presented in a previous paper [Comput. Struct. 27, 565–569 (1987)], this paper introduces the concept and the formulae to facilitate the design of built-up I-shaped steel columns where the web depth has a two-way linear taper. The formulae are expressed in terms of taper index ξ and are used in a computer program to study the effect of ξ on the axial load capacity and to indicate the optimal range of the taper index. Finally, an example is presented to demonstrate the method and to compare its results with an example from the literature.     

Solid–liquid mass transfer in a gas sparging contactor equipped with a tube of circular fins

The solid–liquid mass transfer rate at a stack of circular fin surfaces in a gas sparging contactor was investigated. A diffusion-controlled dissolution technique of copper in an acidified chromate solution was employed. Variables studied included the number of actively exposed fins ranging from 5 to 20, pertinent physical properties of the solution, and air superficial velocity. Experimental data showed that the rate of the diffusion-controlled mass transfer increases with increasing superficial air velocity and decreases with increasing chromate solution acid concentration. Moreover, at relatively low superficial air velocity, increasing the number of actively exposed fins results into a continuous increase in the mass transfer coefficient. At relatively higher superficial air velocity, however, the mass transfer coefficient decreases in the 5–10 range of actively exposed fins and then reverts to increase in the 15–20 range. An empirical correlation relating the mass transfer j factor to Re_g, Fr, and a dimensionless height defined as the ratio of the height of actively exposed fins to the column equivalent diameter was developed based on the data generated in this study, with ±6.45% average deviation.