Articles published in IJEER

Special Issue on Complexity and Uncertainty in Big Data Analytics, and Machine Learning in Real-World Applications


An Improved Method for Skin Cancer Prediction Using Machine Learning Techniques

Among skin diseases the type that causes cancer are the fatal ones and pose the biggest issues. These issues arise since cancers are just much larger quantities of the same cells that are present around the body, which makes diagnosis very difficult until later stages. Now the onset of artificial intelligence and machine learning techniques, in the field of images, has allowed computers to identify sequences and patterns in images that can never be observed by the naked eye. Hence in order to battle skin cancer in its early stages a system has been proposed to identify and predict skin cancer in its earlier stages.

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Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection

The primary contributor to lung cancer is an abnormal proliferation of lung cells. Tobacco usage and smoking cigarettes are the primary contributors to the development of lung cancer. The most common forms of lung cancer fall into two distinct types. Non-small-cell lung cancers and small-cell lung cancers are the two primary subtypes of lung cancer. A computed tomography, or CT, scan is an essential diagnostic technique that may determine the kind of cancer a patient has, its stage, the location of any metastases, and the degree to which it has spread to other organs.

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Detecting Vehicles at Hair Pin Curves using Internet of Things (IOT)

With increase in the number of vehicles especially personal vehicles there is an increase in accidents due to which, every year almost 1.30 million people die due to accidents involving vehicles. There is no effective way to prevent accidents and know the location where the accidents happen to get help easily, especially in hilly areas. In Hilly areas, there are no straight roads for vehicles and sometimes we encounter so many curves, some of which are dangerous that we have no idea if there are any other vehicles coming or not if not maneuvered properly can cause an accident.

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Deep Learning Techniques for Early Detection of Alzheimer’s Disease: A Review

Alzheimer's disease (AD) is the most prevalent kind of dementia illness that can significantly impair a person's capability to carry out everyday tasks. According to findings, AD may be the third provoking reason of mortality among older adults, behind cancer and heart disease. Individuals at risk of acquiring AD must be identified before treatment strategies may be tested. The study's goal is to give a thorough examination of tissue structures using segmented MRI, which will lead to a more accurately labeling of certain brain illnesses. Several complicated segmentation approaches for identify AD have been developed.

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Linear Vector Quantization for the Diagnosis of Ground Bud Necrosis Virus in Tomato

In this varying environment, a correct and appropriate disease diagnosis including early preclusion has never been more significant. Our study on disease identification of groundnut originated by Groundnut Bud Necrosis Virus will cover the way to the effective use of image processing approach in agriculture. The difficulty of capable plant disease protection is very much linked to the problems of sustainable agriculture and climate change. Due to the fast advancement of Artificial Intelligence, the work in this paper is primarily focused on applying Pattern Recognition based techniques.

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Feed Forward Neural Network based Brain Tumor Diagnosis in Magnetic Resonance Images

In the realm of medicine, value, resource use and final care are determined by good technological advancement. However, there are crucial components that must be present for a disease to be diagnosed. The monitoring of illness progression traditionally relies primarily on a subjective human judgment and is neither precise nor timely. One important aspect that utilizes data at various disease progression phases is to maintain routine disease surveillance. The Feed Forward Neural Network based Brain Tumor Diagnosis in Magnetic Resonance Images is provided in this paper as an automatic brain cancer diagnosis and grade classification method. It is highly helpful to have accurate information about the disease in order to classify it and make decisions.

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