Improving prediction of plant disease using k-efficient clustering and classification algorithms

Because plant disease is main cause of most plants’ damage, improving prediction plans for early detection of plant where it has disease or not is an essential interest of decision makers in the agricultural sector for providing proper plant care at appropriate time. Clustering and classification algorithms have proven effective in early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of care quality provided to similar plants. Thus, in this article, we present an artificial intelligence (AI) model based on k-nearest neighbors (k-NN) classifier and k-efficient clustering that integrates k-means with k-medoids to take advantage of both k-means and k-medoids to improve plant disease prediction strategies. Objectives of this article are to determine performance of k-mean, k-medoids and k-efficient also we compare k-NN before clustering and with clustering in prediction of soybean disease for selecting best one for plant disease forecasting. These objectives enable us to analysis data of plant that help to understand nature of plant. Results indicate that k-NN with k-efficient is more efficient than other in terms of inter-class, intra-class, normal mutual information (NMI), accuracy, precision, recall, F-measure, and running time

Hybrid of K-means and partitioning around medoids for predicting COVID-19 cases: Iraq case study

COVID-19 was discovered near the end of 2019 in Wuhan, China. In a short period, the virus had spread throughout the entire world. One of the primary concerns of managers and decision-makers in all types of hospitals nowadays is to implement detection plans for status of patient (Negative, Positive) in order to provide enough care at the proper moment. To reduce a pandemic of COVID-19, improving health care quality could be advantageous. Making clusters of patients with similar features and symptoms supplies an overview of health quality given to similar patients. In the scope of medical machine learning, the K-means and Partitioning Around Medoids (PAM) clustering algorithms are usually used to produce clusters depend on similarity and to detect helpful patterns from sizes of data. In this paper, we proposed a hybrid algorithm of K-Means and Partitioning Around Medoids (PAM) called K-MP to take benefits of both PAM and K-Means to construct an efficient model for predicting patient status. The suggested model for the real dataset was collected from 400 patients in the many Iraqi clinics using a questionnaire. We evaluated the proposed K-MP by using true negative rate, balance accuracy, precision, accuracy, recall, mean absolute error, F1 score, and root mean square error. From these performance measures, we found that K-MP is more efficient in discovering patient status comparing to K-Means and PAM. &nbsp

Hybrid of K-Means and partitioning around medoids for predicting COVID-19 cases: Iraq case study

COVID-19 was discovered near the end of 2019 in Wuhan, China. In a short period, the virus had spread throughout the entire world. One of the primary concerns of managers and decision-makers in all types of hospitals nowadays is to implement detection plans for status of patient (Negative, Positive) in order to provide enough care at the proper moment. To reduce a pandemic of COVID-19, improving health care quality could be advantageous. Making clusters of patients with similar features and symptoms supplies an overview of health quality given to similar patients. In the scope of medical machine learning, the K-means and Partitioning Around Medoids (PAM) clustering algorithms are usually used to produce clusters depend on similarity and to detect helpful patterns from sizes of data. In this paper, we proposed a hybrid algorithm of K-Means and Partitioning Around Medoids (PAM) called K-MP to take benefits of both PAM and K-Means to construct an efficient model for predicting patient status. The suggested model for the real dataset was collected from 400 patients in the many Iraqi clinics using a questionnaire. We evaluated the proposed K-MP by using true negative rate, balance accuracy, precision, accuracy, recall, mean absolute error, F1 score, and root mean square error. From these performance measures, we found that K-MP is more efficient in discovering patient status comparing to K-Means and PAM

Secure wireless sensor network using cryptographic technique based hybrid genetic firefly algorithm

Wireless sensor networks (WSNs) are formed of self-contained nodes of sensors that are connected to one base station or more. WSNs have several primary aims one of them is to transport network node\u27s trustworthy information to another one. As WSNs expand, they become more vulnerable to attacks, necessitating the implementation of strong security systems. The identification of effective cryptography for WSNs is a significant problem because of the limited energy of the sensor nodes, compute capability, and storage resources. Advanced Encryption Standard (AES) is an encryption technique implemented in this paper with three meta-heuristic algorithms which are called Hybrid Genetic Firefly algorithm, Firefly algorithm, and Genetic algorithm to ensure that the data in the WSNs is kept confidential by providing enough degrees of security. We have used hybrid Genetic firefly as a searching operator whose goal is to improve the searchability of the baseline genetic algorithm. The suggested framework\u27s performance that based on the algorithm of hybrid genetic firefly is rated by using Convergence Graphs of the Benchmark Functions. From the graphs we have conclude that hybrid genetic firefly with AES is best from other algorithms.&nbsp

A novel secure artificial bee colony with advanced encryption standard technique for biomedical signal processing

Over the years, the privacy of a biomedical signal processing is protected using the encryption techniques design and meta-heuristic algorithms which are significant domain and it will be more significant shortly. Present biomedical signal processing research contained security because of their critical role in any developing technology that contains applications of cryptography and health deployment. Furthermore, implementing public-key cryptography in biomedical signal processing sequence testing equipment needs a high level of skill. Whatever key is being broken with enough computing capabilities using brute-force attack. As a result, developing a biomedical signal processing cryptography model is critical for improving the connection between existing and emerging technology. Furthermore, public-key cryptography implementation for meta-heuristic-based bio medical signal processing sequence test equipment necessitates a high level of skill. The suggested novel technique can be used to develop a secure algorithm of artificial bee colony, which depend on the advanced encryption standard (AES). AES can be used to reduce the encryption time and to increase the protection capacity for health systems. The novel secure can protect the biomedical signal processing against plain text attacks

Advancement of artificial intelligence techniques based lexicon emotion analysis for vaccine of COVID-19

Emotions are a vital and fundamental part of life. Everything we do, say, or do not say, somehow reflects some of our feelings, perhaps not immediately. To analyze a human\u27s most fundamental behavior, we must examine these feelings using emotional data, also known as affect data. Text, voice, and other types of data can be used. Affective Computing, which uses this emotional data to analyze emotions, is a scientific fields. Emotion computation is a difficult task; significant progress has been made, but there is still scope for improvement. With the introduction of social networking sites, it is now possible to connect with people from all over the world. Many people are attracted to examining the text available on these various social websites. Analyzing this data through the Internet means we\u27re exploring the entire continent, taking in all of the communities and cultures along the way. This paper analyze text emotion of Iraqi people about COVID-19 using data collected from twitter, People\u27s opinions can be classified based on lexicon into different separate classifications of feelings (anticipation, anger, trust, fear, sadness, surprise, disgust, and joy) as well as two distinct emotions (positive and negative), which can then be visualized using charts to find the most prevalent emotion using lexicon-based analysis

Postharvest losses of fruit and vegetables during retail and in consumers’ homes: Quantifications, causes, and means of prevention

The issue of food loss and waste (FLW) reduction has recently achieved much public attention as part of worldwide efforts to combat global hunger and improve food security. Studies conducted by various international and national organizations led by the FAO indicated that about one third of all food produced on the planet and about a half of all fruit and vegetables (F&V) are lost and not consumed. FLW occurs during five key stages of the food supply chain: agricultural production, postharvest handling and storage, processing, distribution, and consumption. Large portions of FLW in developed countries occur during retail and consumption, and are largely related to logistic management operations and consumer behaviors. In light of the great importance of FLW reduction, the United Nations set up in September 2015 an ambitious goal to halve per capita global food waste by 2030, and this decision was adapted by the US Federal Government, the EU Parliament, and many other countries. This first Adel Kader review article is dedicated to the subject of F&V losses during retail and consumption, and contains the following chapters: 1) Introduction of the problem of global food losses; 2) Quantifications of F&V losses during retail and consumption in the UK, US and other countries; 3) Causes and consumer decisions related to F&V wastage; 4) Emerging new technologies for prevention of F&V losses, including advances in logistics and cold chain management, retail packaging and technological innovations; 5) Other means to reduce F&V losses, including consumer awareness campaigns, advertisement of home storage instructions and policy and legislative measures. Due to the great importance of reducing F&V losses, we encourage postharvest researchers to become more engaged with logistics and food supply-chain operations, and to conduct multidisciplinary research incorporating consumer behavior studies into postharvest research