Kapil Aggarwal

Alzheimer’s Disease (AD) is a disease of the brain that lowers quality of life due to cognitive impairment. A correct diagnosis is therefore essential for timely interventions and follow-up care for executives. However, existing deep learning methodologies for neuroimage-based diagnosis seriously lack reliability due to decreased accuracy and increased false positive rates, both of which can lead to misdiagnosis and suboptimal care planning. This study proposed an innovative Steerable Quantum Probabilistic Hamiltonian Generative Modeling with Adaptive Chaotic Satin Bowerbird Optimization (SQPHGM-ACSBO) framework for diagnosing AD using neuroimages within the ADNI dataset. A total of 5,154 neuroimages from the ADNI dataset were utilized in this study, comprising 2,590 MCI, 1,124 AD, and 1,440 cognitively normal (CN) samples. The process begins with image enhancement via an Adaptive Self-Guided Loop Filter, followed by precise brain region segmentation with GoogLeNet Inception-v3, and advanced feature extraction using a new Discrete Cosine-Krawtchouk-Tchebichef Transform (DCKTT). Classification is performed using the SQPHGM model, which combines the strengths of Steerable Transformers and Quantum-Probabilistic Hamiltonian Learning to model complex neuroimaging patterns, while the Adaptive Chaotic Satin Bowerbird Optimization (ACSBO) algorithm optimizes classification performance. With 99.9% accuracy and 99.8% precision, the suggested method outperforms current methods and provides a dependable, high-performance solution for AD diagnosis from neuroimaging data, according to experimental results. The main innovation of this work is the all-in-one optimization of steerable transformer-based directional feature learning, quantum-probabilistic Hamilton generator modeling, and adaptive-chaotic satin bowerbird optimization integrated within a single diagnostic pipeline, which allows for representing the complicated neuroimaging patterns better, and the convergence stability is also increased, compared to the previous deep learning-based Alzheimer diagnosis models.

The journal and authors retract the article entitled “An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices”

In this research, a neural network (NN) model for metal price forecasting based on an evolutionary approach is proposed. Both the neural network model’s network parameters and network architecture are selected automatically. The time series metal price data set is used to construct a novel fitness function that takes into account both error minimizations and the reproduction of the auto-correlation function. Calculating the average entropy values allowed the selection of the input parameter count for the neural network model. Gold price forecasting was performed using the proposed methodology. The optimal hidden node number, learning rate, and momentum are 9, 0.026, and 0.76, respectively, according to the evolutionary-based NN model. The proposed strategy is shown to reduce estimation error while also reproducing the auto-correlation function of the time series data set by the validation results with gold price data. The performance of the proposed method is better than other current methods, according to a comparison study.

Cloud Computing and Distributed Computing is one of the prominent domains of research with the high-performance applications for assorted domains including web services, e-governance, telemedicine, citizen portals and many others. All these applications are performance aware and needs huge attention towards the security and integrity so that the citizen, social and corporate services can be delivered with huge level of performance with minimum error rate. In this research manuscript, the integration of high-performance research implementation is integrated using advanced simulation tools for Internet of Things (IoT) and cloud-based integrations and thereby the outcomes are attained with elevated effectiveness on assorted parameters.

With the adoption and wide usage of smart gadgets and devices for multiple applications, the association of advanced technologies is quite prominence and needs higher degree of accuracy and performance. These are legitimately connected with the advanced innovations including Internet of Things, Internet of Everything, Internet of Vehicles, Internet of Cloud Apps and numerous others. This original copy is therefore displaying the enormous issues and research focuses in this area with the associated streams so the different elements of IoT, IoE, IoX, IoV and others can be broke down with the examination openings and the portions for various streams. In the present situation, the IoT based coordination of digital forms of money is very unmistakable whereby the distributed system is pursued and it goes under the innovation of blockchain. The blockchain innovation is likewise connected with the shrewd advances. In the present period, Blockchain Technology is one of the key territories of research just as execution explicitly in the space of cryptocurrency. Presently days, various computerized cryptographic forms of money are very conspicuous and shared all through the world in spite of tremendous analysis and discussions. Other than these purposes of research, the vehicular mix of IoT is very conspicuous that is tended to utilizing IoV that is Internet of Vehicles in the keen traffic observing and brilliant transportation. Presently days, the IoT is moving towards IoE or IoX which alludes to Internet of Everything and this is additionally tended to in this composition. The manuscript is presenting the scenarios for the implementation patterns associated with the security and dynamic key based environment.