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Glaucoma is a severe condition of the optic nerve resulting in the loss of eyesight. The proposed methodology has introduced the extraction of HOG (histogram of oriented gradients) features from the retinal fundus image. After the removal of HOG features, the authors compare the performance of five different machine learning techniques like k-nearest neighbour (KNN), support vector machine (SVM), linear discriminant analysis (LDA), naïve bayes, and artificial neural network. The process of image classification is based on analyzing the numerical properties of the obtained image features and classifying the data into different categories. In the paper, the authors intend to classify whether the image belongs to the glaucomatous category or the healthy category. After the application of the different classification algorithms to the test data and further analysis of the results, they could conclude that the SVM classifier provided an accuracy of 90%, KNN 86%, Naïve Bayes 96%, LDA 86%, and ANN 96.90% on the dataset in hand.

Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously. This experimental study aims to perform glaucoma prediction using eight different ImageNet models from Optical Coherence Tomography of Glaucoma. A thorough investigation is performed to evaluate these models' performances on various efficiency metrics, which will help discover the best performing model. Every net is tested on three different optimizers, namely Adam, Root Mean Squared Propagation, and Stochastic Gradient Descent, to find the best relevant results. An attempt has been made to improvise the performance of models using transfer learning and fine-tuning. The work presented in this study was initially trained and tested on a private database that consists of 4220 images (2110 normal optical coherence tomography and 2110 glaucoma optical coherence tomography). Based on the results, the four best-performing models are shortlisted. Later, these models are tested on the well-recognized standard public Mendeley dataset. Experimental results illustrate that VGG16 using the Root Mean Squared Propagation Optimizer attains auspicious performance with 95.68% accuracy. The proposed work concludes that different ImageNet models are a good alternative as a computer-based automatic glaucoma screening system. This fully automated system has a lot of potential to tell the difference between normal Optical Coherence Tomography and glaucomatous Optical Coherence Tomography automatically. The proposed system helps in efficiently detecting this retinal infection in suspected patients for better diagnosis to avoid vision loss and also decreases senior ophthalmologists' (experts) precious time and involvement.

Extrahepatic duplication of the common bile duct (CBD) is an extremely rare anatomic variation seen in the biliary tract. It represents failure of regression of the primitive duplicated biliary ductal system, resulting in five different subtypes of the duplicated CBD as described by Choi et al. To date, only few such cases have been reported in the literature. Associated variation in branching of intrahepatic bile ducts presenting as combined dual ductal anomaly is even rarer phenomena to be seen. We report a case of a 67-year-old man with chronic kidney disease and obstructive jaundice resulting from choledocholithiasis. Evaluation revealed type IIIa branching of intrahepatic bile ducts with type Va duplication of the CBD.

Glaucoma is a progressive and constant eye disease that leads to a deficiency of peripheral vision and, at last, leads to irrevocable loss of vision. Detection and identification of glaucoma are essential for earlier treatment and to reduce vision loss. This motivates us to present a study on intelligent diagnosis system based on machine learning algorithm(s) for glaucoma identification using three-dimensional optical coherence tomography (OCT) data. This experimental work is attempted on 70 glaucomatous and 70 healthy eyes from combination of public (Mendeley) dataset and private dataset. Forty-five vital features were extracted using two approaches from the OCT images. K-nearest neighbor (KNN), linear discriminant analysis (LDA), decision tree, random forest, support vector machine (SVM) were applied for the categorization of OCT images among the glaucomatous and non-glaucomatous class. The largest AUC is achieved by KNN (0.97). The accuracy is obtained on fivefold cross-validation techniques. This study will facilitate to reach high standards in glaucoma diagnosis.

Medical records are transmitted between medical institutions using cloud-based Electronic Health Record (EHR) systems, which are intended to improve various medical services. Due to the potential of data breaches and the resultant loss of patient data, medical organizations find it challenging to employ cloud-based electronic medical record systems. EHR systems frequently necessitate high transmission costs, energy use, and time loss for physicians and patients. Furthermore, EHR security is a critical concern that jeopardizes patient privacy. Compared to a single system, cloud-based EHR solutions may bring extra security concerns as the system architecture gets more intricate. Access control strategies and the development of efficient security mechanisms for cloud-based EHR data are critical. For privacy reasons, the Dynamic Constrained Message Authentication (DCMA) technique is used in the proposed system to encrypt the outsource medical data by using symmetric key cryptography which uses the Seagull Optimization Algorithm (SOA) for choosing the best random keys for encryption and then resultant data is hashed using the SHA-256 technique. The system is developed in Python language, and the results are assessed using performance metrics including delay time, security rate, false error rate (FER), storage time, retrieval time, throughput ratio, encryption and decryption time, accuracy rate, key generation time, and security. The implemented system is superior in terms of security because it adopts the advance random secret keys generation which adds more security to the system of about 94% with less delay and loss ratio.

Glaucoma is a progressive and constant eye disease that leads to a deficiency of peripheral vision and, at last, leads to irrevocable loss of vision. Detection and identification of glaucoma are essential for earlier treatment and to reduce vision loss. This motivates us to present a study on intelligent diagnosis system based on machine learning algorithm(s) for glaucoma identification using three-dimensional optical coherence tomography (OCT) data. This experimental work is attempted on 70 glaucomatous and 70 healthy eyes from combination of public (Mendeley) dataset and private dataset. Forty-five vital features were extracted using two approaches from the OCT images. K-nearest neighbor (KNN), linear discriminant analysis (LDA), decision tree, random forest, support vector machine (SVM) were applied for the categorization of OCT images among the glaucomatous and non-glaucomatous class. The largest AUC is achieved by KNN (0.97). The accuracy is obtained on fivefold cross-validation techniques. This study will facilitate to reach high standards in glaucoma diagnosis.

Glaucoma disease is affecting a large community worldwide. It gradually affects the optic nerve and may cause partial or complete vision loss. Glaucoma happens due to an increase in the fluid pressure inside the optic nerve, which is also known as intraocular pressure (IOP). Therefore, it is essential to detect it in the early stage to prevent blindness. Recently, deep neural networks have been applied to analyse medical imagery. This paper proposes a framework for glaucoma detection using the deep convolution neural network. In this framework, a preprocessing step uses the CLAHE to enhance the local contrast. Further, we have utilized two segmentation models (EfficientNet + U-Net) for segmenting the optic cup and disc mask from retinal fundus images. Moreover, the CDR ratio is computed from the segmented optic cup and disc masks. The framework detects whether the inputted image is glaucoma infected or not based on the CDR ratio. The accuracy of the proposed framework is compared to various baseline models. A qualitative and quantitative assessment has been done on various benchmark datasets (DRISHTI-GS1 and RIM-ONE). The experimental outcomes illustrate that the proposed framework outperformed the other state-of-the-art methods for glaucoma detection in the retinal fundus image.

Healthcare today is one of the most promising, prevailing, and sensitive sectors where patient information like prescriptions, health records, etc., are kept on the cloud to provide high quality on-demand services for enhancing e-health services by reducing the burden of data storage and maintenance to providing information independent of location and time. The major issue with healthcare organization is to provide protected sharing of healthcare data from the cloud to the decision makers, medical practitioners, data analysts, and insurance firms by maintaining confidentiality and integrity. This article proposes a novel and secure threshold based encryption scheme combined with homomorphic properties (TBHM) for accessing cloud based health information. Homomorphic encryption completely eliminates the possibility of any kind of attack as data cannot be accessed using any type of key. The experimental results report superiority of TBHM scheme over state of art in terms throughput, file encryption/decryption time, key generation time, error rate, latency time, and security overheads.

The authors have requested that this preprint be removed from Research Square.

Glaucoma is one of the major causes of vision loss in today’s world. Glaucoma is a disease in the eye where fluid pressure in the eye increases; if it is not timely cured, the patient may lose their vision. Glaucoma can be detected by examining boundary of optics cup and optics disc acquired from fundus images. The proposed method suggest automatic detect the boundary of optics cup and optics disc with processing of fundus images. This paper explores the new approach fast fuzzy C-mean technique for segmenting the optic disc and optic cup in fundus images. Results evaluated by fast fuzzy C mean a technique is faster than fuzzy C-mean method. The proposed method reported results to 91.91%, 90.49% and 90.17% when tested on DRIONS, DRIVE and STARE on publicly available databases of fundus images.

Healthcare today is one of the most promising, prevailing, and sensitive sectors where patient information like prescriptions, health records, etc., are kept on the cloud to provide high quality on-demand services for enhancing e-health services by reducing the burden of data storage and maintenance to providing information independent of location and time. The major issue with healthcare organization is to provide protected sharing of healthcare data from the cloud to the decision makers, medical practitioners, data analysts, and insurance firms by maintaining confidentiality and integrity. This article proposes a novel and secure threshold based encryption scheme combined with homomorphic properties (TBHM) for accessing cloud based health information. Homomorphic encryption completely eliminates the possibility of any kind of attack as data cannot be accessed using any type of key. The experimental results report superiority of TBHM scheme over state of art in terms throughput, file encryption/decryption time, key generation time, error rate, latency time, and security overheads.

Glaucoma is one of the major causes of blindness. Glaucoma is a condition due to increased pressure within the eyeball, causing progressive, irreversible and gradual loss of sight. It can be prevented only detection in disc ratio (CDR), ratio of blood vessel area in interior and superior side of the blood vessel in the nasal temporal side extracted from retinal fundus images. Reducing the number of features and reducing the error rate are two conflicting objectives. The proposed methodology aims to explore the use of Differential Evolution based multi objective feature selection technique to select a subset of features which minimize both our conflicting objectives. The performance of selected subset of features has been evaluated using KNN classification technique. It has been observed that said extracted subset of five features, KNN classification achieved better accuracy than any other subset of features.

Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.

The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.

Computer aided design (CAD) generates 2D and 3D design of the objects usually used for machine design. These data items are publically available over Internet. Wide accessibility of these data items security becomes an important issue. These data items can be protected use either by using cryptography, digital signature or watermarking from unauthorized access. In the proposed work, watermarking scheme for computer aided design (CAD)-generated 3D object is proposed that keeps geometrical shape and quality of watermarked objects intact. In the proposed method, geometrical shape of the 3D objects is analyzed to embed the watermark in least perceivable area of 3D objects. The experimental results report robustness against similarity transformation (distortion-less) and distortion attacks.

The last few months have produced a remarkable expansion in research and deep study in the field of machine learning. Machine learning is a technique in which the set of the methods are used by the computers to make prediction, improve prediction and behavior prediction based on dataset. The learning techniques can be classified as supervised and unsupervised learning. The focus is on supervised machine learning that covers all the predictions problem for which we had the dataset in which the outcome is already known. Some of the algorithm like naive bayes, linear regression, SVM, k-nearest neighbor, especially neural network have gain growth in this area. The classifiers of machine learning are completely unconstrained with the assumptions of statistical and for that they are adapted by complex data. The authors have demonstrated the application of machine learning techniques and its ethical issues.

Cloud computing becomes an essential tool for internet users which provide various computing resources over the cloud. Providing on-demand storage is being one of the major services which is effective to meet out the expectations of an organization when utilizes the computational resources while accessing large amount of data. Storing the data on cloud is being a big challenge for the researchers in terms of maintaining the protection and security of data. Sharing of the data among a group can lead to insecurity of data from external and internal threats. This paper introduces a protected data sharing framework in the cloud storage that maintains the privacy and confidentiality of data. The proposed method uses the MD5 hash function that is relatively faster than the SHA256 hash function for checking the data integrity. The framework also provides a check on accessing and sharing of data. Exhaustive re-encryption computations are avoided and a single encryption key is used to encrypt the entire file. There are two distinct key shares for each of the users and user is allowed to share one at a time get access data. In this way having access to a single portion of a key permit the framework to safeguard the data against internal threats. Cryptographic server is used to store the other key share and performs all the expensive computations which are considered as a trusted third party. Proposed work also measure the efficiency of the model based on the time taken to execute the various operations.

Nowadays, machine learning plays a very important role in every field. For recommendation systems, user feedback is relevant because they contain different forms of emotional details that may affect the reliability or consistency of the recommendation. Online reviews, comments are very helpful in selecting the said items and services as it gives real feedback about the quality of these items and services. The categorizations of these items based on feedback provided by actual users are known as sentimental analysis. In this study, we described various machine learning techniques and parameters used for the sentimental analysis of reviews, comments, and feedback available on health care, Facebook, Twitter, and other social media networking sites. The study reveals that the most commonly used approaches are machine learning and deep learning.

This paper proposes a deep image analysis-based model for glaucoma diagnosis that uses several features to detect the formation of glaucoma in retinal fundus. These features are combined with most extracted parameters like inferior, superior, nasal, and temporal region area, and cup-to-disc ratio that overall forms a deep image analysis. This proposed model is exercised to investigate the various aspects related to the prediction of glaucoma in retinal fundus images that help the ophthalmologist in making better decisions for the human eye. The proposed model is presented with the combination of four machine learning algorithms that provide the classification accuracy of 98.60% while other existing models like support vector machine (SVM), K-nearest neighbors (KNN), and Naive Bayes provide individually with accuracies of 97.61%, 90.47%, and 95.23% respectively. These results clearly demonstrate that this proposed model offers the best methodology to an early diagnosis of glaucoma in retinal fundus.

The developments in technology over the years bring the support to drivers using smart vehicle systems. In past few years, there has been substantial increase in road accidents in India and worldwide as well. The most significant reasons for the same are drowsiness and fatigue. Therefore, driver drowsiness and fatigue detection is major possible area to prevent a large number of sleep induced road accidents. Considering this problem, this article proposes a Real-Time Drowsiness Detection System (RT-DDS) applicable in motor vehicles with the help of conventional Computer Vision applications. The system employed various Computer Vision applications using blink rate, eye closure, yawning to effectively and quickly identify the drowsiness of a driver during driving the vehicle and alter the driver accordingly. The proposed work tried to contribute in reducing the increased number of road accidents while keeping the methodologies simple and intact.

Blockchain is most promising and trustworthy decenterized distributed database technology where all the information is maintained on all the nodes and provide transparent and secure system. Number of application uses the Blockchain technology like cryptocurrency, banking, e-voting, Security of IoT devices etc. The proposed paper lists the various features, structures and prominent application of Blockchain and its applications .

Background and Aims: COVID-19 is a dominant pulmonary disease, with multisystem involvement, depending upon co morbidities. Its profile in patients with pre-existing chronic liver disease (CLD) is largely unknown. We studied the liver injury patterns of SARS-Cov-2 in CLD patients, with or without cirrhosis. Methods: Data was collected from 13 Asian countries on patients with CLD, known or newly diagnosed, with confirmed COVID-19. Result: Altogether, 228 patients [ 185 CLD without cirrhosis and 43 with cirrhosis] were enrolled, with comorbidities in nearly 80%. Metabolism associated fatty liver disease (113, 61%) and viral etiology (26, 60%) were common. In CLD without cirrhosis, diabetes [57.7% vs 39.7%, OR=2.1(1.1-3.7), p=0.01] and in cirrhotics, obesity, [64.3% vs. 17.2%, OR=8.1(1.9-38.8), p=0.002) predisposed more to liver injury than those without these. Forty three percent of CLD without cirrhosis presented as acute liver injury and 20% cirrhotics presented with either acute-on-chronic liver failure [5(11.6%)] or acute decompensation [4(9%)]. Liver related complications increased (p

Fasciola hepatica is parasite in domestic animals. Humans are accidental host following ingestion of plants or water infested with metacercariae or undercooked liver. A rare case of endoscopic extraction of liver fluke presenting as biliary obstruction with cholangiolar liver abscess is reported in this case report.