A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process

A Hypertuned Pipeline Vector Using Meta Classifier Technique for Feature Selection in Multi Disease Prediction

Automation of health sector plays a very important role especially during this pandemic due to the side effects of either vaccination or attack of the COVID. Most of the researchers designed a system to predict whether a person suffers from a particular disease or not. Few researchers worked on prediction variants of a single disease based on symptoms but due to this COVID-19, different people are getting attacked with different diseases as a side effect. This proposed system aims to identify the multiple diseases that a person may suffer from based on the symptoms. In this paper, the dataset obtained from the open access repository “Kaggle” contains 17 symptoms combinations to identify the one of the 41 types of diseases as class label. All the symptoms may not be important for identification, so in this model, the important features are identified using the pipeline vector of different Machine Learning approaches are passed as base line classifier and decision tree classifier as meta line to the elimination function. The model has got “99.48%” accuracy for selecting the essential features using bagging and boosting algorithms

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

A Comprehensive Review on Intelligent Techniques in Crop Pests and Diseases

Artificial intelligence (AI) has transformative potential in the agricultural sector, particularly in managing and preventing crop diseases and pest infestations. This review discusses the significance of early detection and precise diagnosis of various AI tools and techniques for disease identification, such as image processing, machine learning, and deep learning. It also addresses the challenges of AI implementation in agriculture, including data quality, costs, and ethical concerns. The analysis classifies the hurdles and AI offers benefits such as improved resource management, timely interventions, and enhanced productivity. Collaborative efforts are essential to harness AI's potential for sustainable and resilient agriculture

FLY-CAPS- A Hybrid Firefly Feature Optimized Capsule Networks for Plant Disease Classification in Resource Constriant Internet of Things (IoT)

Recent advancements in artificial intelligence, automation, and the Internet of Things (IoT) enable farmers to better monitor and diagnose all agricultural procedures with super-intellectual accuracy. These technologies also contribute to boosting the productivity of agriculture, which increases the country’s economy. Though these technologies help farmers increase productivity, the detection of plant diseases still needs heightened scrutiny for prevention and cultivation. Plant disease categorization has expanded with the introduction of deep learning algorithms, but it still needs more innovation in terms of accuracy and computing burden. Thus, a novel deep learning model based on capsule networks with firefly optimization and potent multi-layered feedforward prediction networks is proposed in this research. The handcrafted features in this proposed system are optimized before being extracted using a capsule network, which reduces the complexity overhead and is suitable for IoT devices with limited resources. Finally fed to the feed forward layers for better classification. The extensive experimentation has been tested with the Plant Village databases, which contain more than 50,000 images of healthy and infected plants. Performance criteria including recall, specificity, recall, accuracy, and f1-score are used to assess the proposed algorithm's performance. Additionally, its efficiency and computational cost are contrasted with those of other recent models. The suggested model has greater performance (95%) with reduced computing overhead, according to experimental data, which is advantageous for the new prediction approach and the welfare of the farmer

Explainer: An interactive Agent for Explaining the Diagnosis of Cardiac Arrhythmia Generated by IK-DCBRC

Interactions between medical applications and users involve a high level of trust, since many complex, automated applications are integrated and involve critical domains in which public health is paramount. Although uncertainty decreases the accuracy and trust of such medical applications under these circumstances, explanation-aware computing becomes crucial in improving the efficiency of these applications. This paper describes an intelligent agent that interacts with users to provide meaningful explanations of previous diagnoses supported by IK-DCBRC. The agent ensures intelligent interactions with users via a rule-based system that generates appropriate explanations according to the selected level of abstraction and the detected cardiac arrhythmia. The paper also describes a particular medical application, that is, cardiac arrhythmia with automatic diagnoses supported by the case-based reasoning classifier, IK-DCBRC