Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters ($1\times 1,3\times 3$, and $5\times 5$). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.

Alzheimer’s Disease (AD) is a psychological disorder in elderly people which causes severe intellectual disabilities. Proper processing of neuro-images can provide differences in brain tissues which may help in diagnosing the disease more effectively. But, due to the complex structures, this is a challenge in differentiating the brain tissues and classifying AD using traditional classification mechanisms. Deep Neural Network (DNN) is a machine learning technique that has the ability to absorb the most important information for classifying an object accurately. LeNet is a popular DNN based model with a simple and effective architecture that also consumes very less implementation time. As like most of the DNN models, LeNet also uses MaxPooling layer for dimensionality reduction by eliminating the information of minimum valued elements. In brain images low intensity valued pixels also may contain very important features. To keep the minimum valued elements too in the network, we have created a separate layer that performs Min-Pooling operation. MinPooling and MaxPooling layers are then concatenated together. Finally, we have replaced all MaxPooling Layers in LeNet by the concatenated layers. We have analysed and compared the performances of modified LeNet model with 20 other most commonly used DNN models, and some of the related works. It is observed that, the modified LeNet model achieved the highest performances. It is also observed that, original LeNet model can classify AD with a performance rate of 80%, whereas, the proposed modified LeNet model achieved an average performance rate of 96.64%.

Alzheimer’s disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results.

Deep learning has obtained wide attention in various fields enabling systems to derive essential information from digital inputs. Lately, the use of deep learning in remote sensing applications has also been motivated and applied, wherein considerable improvements in the results are witnessed. Synthetic aperture radar images have been used in various earth observation systems because of their all-day imaging capacity and self-illuminating nature. Various works concentrating on extracting meaningful information from SAR data for various other applications have been proposed in the literature. Classification of SAR images has been one of the utmost steps in numerous SAR applications. Therefore, this work focuses on studying several existing techniques that use deep learning for SAR image classification by examining the architectures involved. Based on the study, crucial observations are made, highlighting the merits and demerits of several approaches, allowing researchers to better understand how the methods can impact the performance of the deep learning models for SAR image classification in the future. Potential hybrid models for the classification of SAR images are also presented in this paper.

Future generation communication systems are aiming to provide a tremendous high data rate with low-latency high reliable and three-dimensional coverage. To achieve such a challenging goal, it is required to have very precise location information related to the mobile terminal. The advancement of signal processing techniques and communication technologies enables the path for improving localization performance. Recently, intelligent reflecting surface (IRS) has been widely considered as the key element for the future generation of wireless communication. Over the past few years, the performance of IRS-assisted networks is extensively investigated from the point of view of communication purposes and its improvement. However, by virtue of its potential, IRS finds its application for wireless localization. In this paper, we discuss and summarize the works that have already been carried out targeting localization performance improvement. In addition, we figured out the associated challenges and the opportunities to scale up the localization accuracy. Particularly in this paper, the authors have discussed the challenging issues such as channel modeling, channel estimation, system architecture, hardware impairment, IRS deployment strategies, phase optimization, mobility management, and near-field environments. Although these challenges are associated with opportunities to make the IRS-assisted system more effective and efficient.