Efficient Deep Neural Networks for Classification of Alzheimer’s Disease and Mild Cognitive Impairment from Scalp EEG Recordings

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

Intelligent networking is a concept that enables 5G, the Internet of Things (IoT) and artificial intelligence (AI) to combine as a way to accelerate technological innovation and develop new revolutionary digital services. In the intelligent connectivity vision, the digital information gathered by the machines, devices and sensors which make up the IoT is analysed and contextualized. It is anticipated that the high availability of 5G and its inclusion of a large number of connections would help promote the production of wearable devices used to monitor the different biometric parameters of the wearer. Since these are AI-based health systems, the data obtained from these devices will be analysed in order to assess a patient’s current health status. This chapter presents a detailed design for the development of intelligent data analytics and mobile computer-assisted healthcare systems. The proposed advanced PoS consensus algorithm provides better performance than other existing algorithms.

Big Data Analytics for 5G-Enabled IoT Healthcare

In breast cancer screening, binary classification of mammograms is a common task aiming to determine whether a case is malignant or benign. A Computer-Aided Diagnosis (CADx) system based on a trainable classifier requires clean data and labels coming from a confirmed diagnosis. Unfortunately, such labels are not easy to obtain in clinical practice, since the histopathological reports of biopsy may not be available alongside mammograms, while normal cases may not have an explicit follow-up confirmation. Such ambiguities result either in reducing the number of samples eligible for training or in a label uncertainty that may decrease the performances. In this work, we maximize the number of samples for training relying on multi-task learning. We design a deep-neural-network-based classifier yielding multiple outputs in one forward pass. The predicted classes include binary malignancy, cancer probability estimation, breast density, and image laterality. Since few samples have all classes available and confirmed, we propose to introduce the uncertainty related to the classes as a per-sample weight during training. Such weighting prevents updating the network's parameters when training on uncertain or missing labels. We evaluate our approach on the public INBreast and private datasets, showing statistically significant improvements compared to baseline and independent state-of-the-art approaches. Moreover, we use mammograms from Susan G. Komen Tissue Bank for fine-tuning, further demonstrating the ability to improve the performances in our multi-task learning setup from raw clinical data. We achieved the binary classification performance of AUC = 80.46 on our private dataset and AUC = 85.23 on the INBreast dataset.

https://www.frontiersin.org/articles/10.3389/fradi.2021.796078/pdf

Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms

The most commonly diagnosed and second leading cause of cancer fatalities is breast cancer in women. AI and IoT integrated system that can help diagnose earlier breast cancer. The key tool for detecting breast cancer is mammograms. Yet cancer cells in breast tissue are difficult to identify. Since it has less fat and more muscle. To examine the irregular areas of density, mass and calcification that signify the presence of cancer, digitized mammography images are used. Several imaging techniques have been developed to detect and treat breast cancer early and to decrease the number of deaths, and many methods of diagnosis of breast cancer have been used to increase diagnostic accuracy. The current technique does not detect breast cancer reliably in the early stages, and most women have suffered from this.

Breast Cancer Detection Using Machine Learning

An individual Human computer interference system is being introduced. In olden times, as an input device the mouse and keyboard were used by human computer interference system. Those people who are suffering from certain disease or illness cannot be able to operate computers. The idea of controlling the computers with the eyes will serve a great use for handicapped and disabled person. Also this type of control will eliminate the help required by other person to handle the computer. This measure will be the most useful for the person who is without hands through which they can operate with the help of their eye movements. The movement of the cursor is directly associated with the center of the pupil. Hence our first step would be detecting the center of point pupil. This process of pupil detection is implemented using the Raspberry Pi and OpenCV. The Raspberry Pi has a SD/MMC card slot which is used for placing the SD card. The SD card is boosted with the operating system that is required starting up of Raspberry Pi. The Raspberry PI will get executed once the application program is loaded into it.

Eyeball based Cursor Movement Control

/pdf/visualizing-road-damage-by-monitoring-system-in-cloud-2mwn48a7y3.pdf

Visualizing road damage by monitoring system in cloud

In the present conditions, students regularly have difficulty finding a fitting institution to pursue higher studies based on their profile. There are some advisory administrations and online apps that recommend universities but they ask huge consultancy fees and online apps are not accurate. So, the aim of this research is to develop a model that predict the percentage of chances into the university accurately. This model provides also the analysis of scores versus chance of prediction based on historical data so that students can understand whether their profile is suitable or not. The proposed model uses linear regression and random forest algorithms but cat boost algorithm is giving highest accuracy.

Prediction Probability of Getting an Admission into a University using Machine Learning

Plant disease is a major problem for food security, but in many parts of the world their rapid prediction remains difficult because of lack of the infrastructure required. New advances in machine vision achieved via deep learning have paved the way for diagnosis of AI-assisted diseases.\n To help determine the extent of plant disease, provide agricultural specialists with a digital archive with photos of diseased leaves. Estimate depends on Standard Area Diagrams (SADs), a collection of diseased leaf images, each of which includes an incrementally more diseased leaf compared\n to the previous one. Every SAD shows seriousness of the disease in terms of the percentage of the diseased leaf. Users then turn to the field for a leaf. For eg, equate it to SADs and use it to measure the severity of the disease. “This app is useful for crop consultants and research\n scientists looking to cut costs and improve the time and accuracy for assessing disease severity in plants.”

D. Deepa

Papers

Eyeball based Cursor Movement Control

Visualizing road damage by monitoring system in cloud

Big Data Analytics for 5G-Enabled IoT Healthcare

Prediction Probability of Getting an Admission into a University using Machine Learning

Plant Disease Detection and Classification Using Image Processing and Neural Networks