F. Sammy

Recently, extensive research has focused on the design of clustering algorithms for grouping vehicles in a set of clusters in Vehicular Ad Hoc Networks (VANETs). However, due to the dynamic nature of VANETS, frequently connected or detached nodes threaten the stability of the network. When these nodes are clustered heads (CHs), the impact of these disruptions on network performance is even worse. Therefore, the stability of clusters is an important issue that should be maintained to enhance the performance of the network with minimum data loss. In this article, a novel clustering approach is proposed, which is based on the average weight concept of three main attributes, namely, the geostationary coordinates (x, y) and drop density. The average of the above-mentioned attributes is calculated and compared with the values of other vehicles attributes. Those vehicles having minimum average values are dropped and not to be considered in the cluster formation. The cluster members having minimum Euclidean distance are considered as Cluster Head (CH) until a certain checkpoint is not attained. The checkpoints are created in order to generate the social score of the vehicles. Once the social score is attained, a multiobjective framework is developed, which considers minimizing the Euclidian distance and maximizing the social score. The social score is generated based on the Quality of Service (QoS) attained attributes, and the data transmission is performed using Ad Hoc On-Demand Distance Vector (ADOV) routing protocol. A comparison of the proposed work and existing work has also been undertaken to demonstrate the improvement in the proposed work. As a result, throughput, PDR, network longevity, and energy consumption have been improved by 28.27, 20.9, 23.12, and 27.48 percent, respectively. Extensive simulations show that the proposed scheme, unlike other popular clustering algorithms, can significantly increase the stability of the network by extending the life of the cluster head.

The liver is in charge of a plethora of tasks that are critical to healthy health. One of these roles is the conversion of food into protein and bile, which are both needed for digestion. Inhaled and possibly harmful chemicals are flushed from the body. It destroys numerous nutrients acquired through the gastrointestinal system and limits the release of cholesterol by utilizing vitamins, carbohydrates, and minerals stored in the liver. The body’s tissues are made up of tiny structures known as cells. Cells proliferate and divide in order to create new ones in the normal sequence of events. When an old or damaged cell has to be replaced, a new cell must be synthesized. In other circumstances, the procedure is a total and utter failure. If the tissues of dead or damaged cells that have been cleared from the body are not removed, they may give birth to nodules and tumors. The liver can produce two types of tumors: benign and malignant. Malignant tumors are more dangerous to one’s health than benign tumors. This article presents a technique for the classification and identification of liver cancers that is based on image processing and machine learning. The approach may be found here. During the preprocessing stage of picture creation, the fuzzy histogram equalization method is applied in order to bring about a reduction in image noise. After that, the photographs are divided into many parts in order to zero down on the area of interest. For this particular classification task, the RBF‐SVM approach, the ANN method, and the random forest method are all applied.

Increased quantities of the same sort of item are not nearly as critical to client happiness as a high-quality product. The requirements and expectations of the consumer have an impact on the overall quality of a product or service. The term “quality” may also be defined as the sum total of all the features that contribute to the production of goods and services that are satisfactory to the consumer. Certain imported commodities have lately seen an improvement in quality thanks to efforts by importing nations. Additionally, it safeguards food imported from other nations by confirming that it is safe for human consumption before it is released. This article describes a technique for monitoring perishable goods that is based on the Internet of Things and machine learning. Pictures are recorded using high-resolution cameras in this suggested architecture, and then these images are sent to a cloud server using Internet of Things devices. When uploaded to a cloud server, these photos are segmented using the K-means clustering method. Then, using the principal component analysis technique, features are extracted from the photos, and the images are categorized using machine learning models that have been trained. This proposed model makes use of the Internet of Things, image processing, and machine learning to monitor perishable food.

Due to a lack of efficient measures for dealing with food waste at many levels, including food supply chains, homes, and restaurants, the world’s food supply is shrinking at an alarming pace. In both homes and restaurants, overcooking and other factors are to be blamed for the majority of food that is wasted. Families are the primary source of food waste, and we sought to reduce this by identifying fresh and damaged food. In agriculture, the detection of rotting fruits becomes crucial. Despite the fact that people routinely classify healthy and rotten fruits, fruit growers find it ineffective. In contrast to humans, robots do not grow tired from doing the same thing again and again. Because of this, finding faults in fruits is a declared objective of the agricultural business in order to save labour, waste, manufacturing costs, and time spent on the process. An infected apple may infect a healthy one if the defects are not discovered. Food waste is more likely to occur as a consequence of this, which causes several problems. Input images are used to identify healthy and deteriorated fruits. Various fruits were employed in this study, including apples, bananas, and oranges. For classifying photographs into fresh and decaying fruits, softmax is used, while CNN obtains fruit image properties. A dataset from Kaggle was used to evaluate the suggested model’s performance, and it achieved a 97.14 percent accuracy rate. The suggested CNN model outperforms the current methods in terms of performance.

VANETs (vehicular ad hoc networks) have evolved as a platform for enabling intelligent inter-vehicle communication while also improving traffic safety and performance. VANETs are a difficult research topic because of the road dynamics, high mobility of cars, their unlimited power supply, and the growth of roadside wireless infrastructures. In wireless networks, game theory approaches have been widely used to investigate the interactions between competitive and cooperative behavior. In this research, we propose a technique for vehicular ad hoc networks that uses a game theory approach to automate vehicle grouping and cluster head nomination. This will eliminate the need for cluster reformation on a regular basis. Furthermore, each vehicle’s social behavior will be exploited to establish clusters in the vehicular environment. For the development of clusters on the social behavior of the cars, a machine learning approach (K-means algorithm) is applied. The proposed system is tested against a variety of characteristics, including CH life time, average cluster member life time, average number of reaffiliation times, throughput, and packet loss rate, and the results indicate that the VANET performed very well with high accuracy in validation and testing, and overall in the range of 0.97 to 0.99.

Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework’s development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.

Major depressive disorder (MDD) is a mood state that is not usually associated with vision problems. Recent research has found that the inhibitory neurotransmitter GABA levels in the occipital brain have dropped. Aim. The aim of the research is to evaluate mental workload by single channel electroencephalogram (EEG) approach through visual-motor activity and comparison of parameter among depressive disorder patient and in control group. Method. Two tests of a visual-motor task similar to reflect drawings were performed in this study to compare the visual information processing of patients with depression to that of a placebo group. The current study looks into the accuracy of monitoring cognitive burden with single-channel portable EEG equipment. Results. The alteration of frontal brain movement in reaction to fluctuations in cognitive burden stages generated through various vasomotor function was examined. By applying a computerised oculomotor activity analogous to reflector image diagram, we found that the complexity of the path to be drawn was more important than the real time required accomplishing the job in determining perceived difficulty in depressive disorder patients. The overall perceived difficulty of the exercise is positively linked with EEG activity measured from the motor cortex region at the start of every experiment test. The average rating for task completion for depression patients and in control group observed and no statistical significance association reported between rating scale and time spent on each trial ( ) for control group while the normalised perceived difficulty rating had 0.512, 0.623, and 0.821 correlations with the length of the pathway, the integer of inclination in the pathway, and the time spent to complete every experiment test, respectively ( ) among depression patients. The findings imply that alterations in comparative cognitive burden levels during an oculomotor activity considerably modify frontal EEG spectrum. Conclusion. Patients with depression perceived the optical illusion in the arrays as weaker, resulting in a little bigger disparity than individuals who were not diagnosed with depression. This discovery provided light on the prospect of adopting a user-friendly mobile EEG technology to assess mental workload in everyday life.

To enhance the control technology of coal gangue dry separation method which is replaced by the machine in coal washing plant and to explore the control effects of traditional PID and dynamic domain fuzzy self-tuning PID, which will aid in determining the ideal position and orientation for grasping an object as well as understanding physical and logistic data patterns, an optimal design of PID controller for sorting robot based on deep learning is initiated. The mathematical model of ball screw system driven by a single joint motor of the robot is introduced, the control effects of classical PID and variable domain fuzzy self-tuning PID are studied and imitated, respectively. The simulation outcome appears that the selection time is 0.001s and simulation time is 8s. The tracking error of variable domain fuzzy PID is minor than that of PID tracking at the starting point, and the convergence rate of error is quick than that of PID manage, the steady-state error is minor than PID, the control accuracy is higher, and the tracking performance is better. The advantages of variable domain fuzzy PID control method in position tracking control are verified, the variable domain fuzzy PID can modify the control framework online as per the different position mistake and mistake change rate, the design of the variable domain of input and output makes the fuzzy inference rules locally finer, the speed of adjustment is faster and the tracking accuracy is further improved, so it has finer tracking presentation than the traditional PID tracking management.

Alzheimer’s disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer’s disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.

Secure patient health record (PHR) information exchange via cloud computing is a considerable security risk to user privacy. The fundamental reason of this issue is cloud computing’s reliance on trustworthy third parties to share data across it. To exchange data securely, many conventional cryptographic algorithms employ various keying approaches. However, relying on a trusted third party compromises the privacy of consumers’ data. To offer secure communication without the involvement of a third party, a distributed blockchain based (DBC) ciphertext–policy attribute-based encryption (CP-ABE) approach is employed in this study. Because of bilinear paring and simple scalar multiplication factors, the proposed CP-ABE system is entirely dependent on elliptic curve cryptography to reduce complexity. Furthermore, the data requester provides dynamic attributes, and a user-centric access policy is created, allowing multiple authorities to manage the attributes and provide data access. Data confidentiality, data authentication, user authentication, and tamper-proof data are all guaranteed by the suggested method. The DBC-CP-ABE method is used to provide user-centric access policies and effective key management.