A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

Alzheimer’s disease(AD)is a chronic and common form of dementia that mainly affects elderly individuals.The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear,and there is nomedicinal or surgical treatment available yet forAD.ADcauses loss of memory and functionality control in multiple degrees according to AD’s progression level.However,early diagnosis of AD can hinder its progression.Brain imaging tools such as magnetic resonance imaging(MRI),computed tomography(CT)scans,positron emission tomography(PET),etc.can help in medical diagnosis of AD.Recently,computer-aided diagnosis(CAD)such as deep learning applied to brain images obtained with these tools,has been an established strategic methodology that is widely used for clinical assistance in prognosis of AD.In this study,we proposed an intelligent methodology for building a convolutional neural network(CNN)from scratch to detect AD stages from the brain MRI images dataset and to improve patient care.It is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting problems.Therefore,for better understanding of classifiers and to overcome the model overfitting problem,we applied data augmentation to the minority classes in order to increase the number of MRI images in the dataset.All experiments were conducted using Alzheimer’s MRI dataset consisting of brain MRI scanned images.The performance of the proposed model determines detection of the four stages of AD.Experimental results show high performance of the proposed model in that the model achieved a 99.38%accuracy rate,which is the highest so far.Moreover,the proposed model performance in terms of accuracy,precision,sensitivity,specificity,and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.

Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease and treating it is pretty challenging in this period.Image processing is employed to detect plant disease since it requires much effort and an extended processing period.The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf curl.Therefore,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and classification.The gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant leaf.According to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.

Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in Mobile Cloud

In today’s world,smart phones offer various applications namely face detection,augmented-reality,image and video processing,video gaming and speech recognition.With the increasing demand for computing resources,these applications become more complicated.Cloud Computing(CC)environment provides access to unlimited resource pool with several features,including on demand self-service,elasticity,wide network access,resource pooling,low cost,and ease of use.Mobile Cloud Computing(MCC)aimed at overcoming drawbacks of smart phone devices.The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance.For remote execution,recent studies suggested downloading all or part of mobile application from mobile device.On the other hand,in offloading process,mobile device energy consumption,Central Processing Unit(CPU)utilization,execution time,remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks designed.To address the issues,a Heuristic and Bent Key Exchange(H-BKE)method can be considered by both ways to optimize energy consumption as well as to improve security during offloading.First,an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where,the secondary users are allocated with the unused primary users’spectrum.Next,a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’unused spectrum by the secondary user.Simulations are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption,energy consumption along with the security aspects covered during offloading in MCC.

Securing Copyright Using 3D Objects Blind Watermarking Scheme

Recently,securing Copyright has become a hot research topic due to rapidly advancing information technology.As a host cover,watermarking methods are used to conceal or embed sensitive information messages in such a manner that it was undetectable to a human observer in contemporary times.Digital media covers may often take any form,including audio,video,photos,even DNA data sequences.In this work,we present a new methodology for watermarking to hide secret data into 3-D objects.The technique of blind extraction based on reversing the steps of the data embedding process is used.The implemented technique uses the features of the 3-D object vertex’discrete cosine transform to embed a grayscale image with high capacity.The coefficient of vertex and the encrypted picture pixels are used in the watermarking procedure.Additionally,the extraction approach is fully blind and is dependent on the backward steps of the encoding procedure to get the hidden data.Correlation distance,Euclidean distance,Manhattan distance,and the Cosine distance are used to evaluate and test the performance of the proposed approach.The visibility and imperceptibility of the proposed method are assessed to show the efficiency of our work compared to previous corresponding methods.