Hybrid Recommender System for Mental Illness Detection in Social Media Using Deep Learning Techniques.

Recommender systems are chiefly renowned for their applicability in e-commerce sites and social media. For system optimization, this work introduces a method of behaviour pattern mining to analyze the person's mental stability. With the utilization of the sequential pattern mining algorithm, efficient extraction of frequent patterns from the database is achieved. A candidate sub-sequence generation-and-test method is adopted in conventional sequential mining algorithms like the Generalized Sequential Pattern Algorithm (GSP). However, since this approach will yield a huge candidate set, it is not ideal when a large amount of data is involved from the social media analysis. Since the data is composed of numerous features, all of which may not have any relation with one another, the utilization of feature selection helps remove unrelated features from the data with minimal information loss. In this work, Frequent Pattern (FP) mining operations will employ the Systolic tree. The systolic tree-based reconfigurable architecture will offer various benefits such as high throughput as well as cost-effective performance. The database's frequently occurring item sets can be found by using the FP mining algorithms. Numerous research areas related to machine learning and data mining are fascinated by feature selection since it will enable the classifiers to be swift, more accurate, and cost-effective. Over the last ten years or so, there have been significant technological advancements in heuristic techniques. These techniques are beneficial because they improve the search procedure's efficiency, albeit at the potential sacrifice of completeness claims. A new recommender system for mental illness detection was based on features selected using River Formation Dynamics (RFD), Particle Swarm Optimization (PSO), and hybrid RFD-PSO algorithm is proposed in this paper. The experiments use the depressive patient datasets for evaluation, and the results demonstrate the improved performance of the proposed technique.

Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model.

Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model's outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use.

Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method.

Breast cancer can develop when breast cells replicate abnormally. It is now a worldwide issue that concerns people's safety all around the world. Every day, women die from breast cancer, which is especially common in the United States. Mammography, CT, MRI, ultrasound, and biopsies may all be used to detect breast cancer. Histopathology (biopsy) is often carried out to examine the image and discover breast cancer. Breast cancer detection at an early stage saves lives. Deep and machine learning models aid in the detection of breast cancer. The aim of the research work is to encourage medical research and the development of technology by employing deep learning models to recognize cancer cells that are small in size. For histological annotation and diagnosis, the proposed technique makes use of the BreCaHAD dataset. Color divergence is caused by differences in slide scanners, staining procedures, and biopsy materials. To avoid overfitting, we used data augmentation with 19 factors, such as scale, rotation, and gamma. The proposed hybrid dilation deep learning model is of two sorts. It illustrates edges, curves, and colors, and it improves the key traits. It utilizes dilation convolution and max pooling for multi-scale information. The proposed dilated unit processes the image and sends the processed features to the Alexnet, and it can recognize minute objects and thin borders by using the dilated residual expanding kernel model. An AUC of 96.15 shows that the new strategy is better than the old one.

Blockchain-Based Decentralized Cloud Solutions for Data Transfer.

Cloud computing has increased its service area and user experience above traditional platforms through virtualization and resource integration, resulting in substantial economic and societal advantages. Cloud computing is experiencing a significant security and trust dilemma, requiring a trust-enabled transaction environment. The typical cloud trust model is centralized, resulting in high maintenance costs, network congestion, and even single-point failure. Also, due to a lack of openness and traceability, trust rating findings are not universally acknowledged. "Blockchain is a novel, decentralised computing system. Its unique operational principles and record traceability assure the transaction data's integrity, undeniability, and security. So, blockchain is ideal for building a distributed and decentralised trust infrastructure. This study addresses the difficulty of transferring data and related permission policies from the cloud to the distributed file systems (DFS). Our aims include moving the data files from the cloud to the distributed file system and developing a cloud policy. This study addresses the difficulty of transferring data and related permission policies from the cloud to the DFS. In DFS, no node is given the privilege, and storage of all the data is dependent on content-addressing. The data files are moved from Amazon S3 buckets to the interplanetary file system (IPFS). In DFS, no node is given the privilege, and storage of all the data is dependent on content-addressing.

Retrospective Review of the Use of High-Dose Cyclophosphamide, Bortezomib, Doxorubicin, and Dexamethasone for the Treatment of Multiple Myeloma and Plasma Cell Leukemia.

BACKGROUND: Multiple myeloma (MM) usually follows a clinical course leading to refractoriness and limited treatment options in advanced stages, which might need bridge therapies to either autologous stem cell transplantation or novel therapies. We report our experience with the high-dose chemotherapy mCBAD (modified cyclophosphamide, bortezomib, doxorubicin, and dexamethasone) regimen in newly diagnosed MM (NDMM), relapsed/refractory MM (RRMM), and plasma cell leukemia (PCL) patients. PATIENTS AND METHODS: We searched our electronic records database for MM patients who received mCBAD from 2010 to 2016 for 28-day cycles of cyclophosphamide 350 mg/m2 intravenously (I.V.) twice daily with mesna 400 mg/m2 I.V. daily (days 1-4), bortezomib 1.3 mg/m2 subcutaneously/I.V. (days 1, 4, 8, 11), doxorubicin 9 mg/m2 daily continuous infusion (days 1-4), dexamethasone 40 mg orally daily (on days 1-4, 9-12, 17-20). International Myeloma Working Group (IMWG) criteria were used for response assessment and diagnosis. Descriptive statistics, Fisher exact test, χ2, Wilcoxon rank sum, and Kaplan-Meier were used for statistical purposes. RESULTS: One hundred forty patients met the inclusion criteria. A median of 2 cycles of therapy was administered. The overall response rate was 85% in patients with RRMM (n = 116) and 100% in NDMM (n = 13) and PCL (n = 11) patients. Respective median progression-free survival (mPFS) for NDMM, PCL, and RRMM were 19.61 months (95% confidence interval [CI], 5.26 to not applicable [NA]), 7.56 months (95% CI, 4.7 to NA), and 4.64 months (95% CI, 3.75-6.73). Patients with RRMM who used mCBAD as a bridge to autologous transplant (36.2%) had mPFS (11.48 months; 95% CI, 7.52-15.9 months) compared with those who did not (mPFS: 3.19 months; 95% CI, 2.4-3.75 months). Cytopenias occurred in more than 90% of patients, and febrile neutropenia was noted in 26%. All cases of treatment-related mortality (8%) occurred in patients with RRMM, except for 1 patient with PCL. CONCLUSION: mCBAD results in high response rates in myeloma and PCL, however, with high treatment-related mortality. Its use in RRMM should be limited to patients who have immediate need for therapy without other treatment options and who have good performance status (score of 0-1) or NDMM if novel agents are not available depending on practice setting. mCBAD can be a treatment option for patients with PCL.