World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers’ sensitive information of a reputed TI company securing its one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization’s information security monitoring team.

The growth of internet era leads to a major transformation in a storage of data and accessing the applications. One such new trend that promises the endurance is the Cloud computing. Computing resources offered by the Cloud includes the servers, networks, storage, and applications, all as services. With the advent of Cloud, a single application is delivered as a metered service to numerous users, via an Application Programming Interface (API) accessible over the network. The services offered via the Cloud are such as the infrastructure, software, platform, database and web services. The main motivation of this application model is to provide computationally secure key generation to protect the data via encryption. This key generation in the cryptography process falls into three categories in this research work. In the first part, SVM based encryption service model is constructed for which the key generation is from the conventional encryption operation mode with some improvements. To make the process more complex, the optimization techniques are taken into account for the key generation in descendant two methods application model that acts computationally more secure specifically for Cloud environment. The results of security analysis confirm the effectiveness of the proposed application model withstands potentially against various attacks such as Chosen Cipher Attack, Chosen Plain text Attack indistinguishable attacks for files. In case of images, it resists well against statistical and differential attacks. Comparative Analysis shows evidence of the efficiency of the developed pioneering application model quality and strength compared with that of the existing services.

Aim: Further to our reports on chip-integrable uncooled terahertz microbolometer arrays, compatible with medium-scale semiconductor device fabrication processes, the possibility of the development ...

In this study, uncooled antenna-coupled microbolometer arrays were fabricated to detect terahertz waves by using nanoscale meander-shaped Ti thermistors with design widths of DW = 0.1 and 0.2 μm, respectively, on SiO₂ and SiN_x substrates. Each unit device with a thermistor with DW = 0.1 μm yielded double the electrical responsivity (787 V/W) of unit devices with thermistors with DW = 0.2 μm (386 V/W) at the maximum allowable bias current (I_b = 50 for DW = 0.1 μm and 100 μA for DW = 0.2 μm, respectively). However, the calculated noise-equivalent power (NEP) of unit devices with thermistors with DW = 0.1 μm was $1.85\times {10}^{-10}\mathrm{W}/\sqrt{\mathrm{Hz}}$ at I_b = 50 μA and $1.58\times {10}^{-10}\mathrm{W}/\sqrt{\mathrm{Hz}}$ at I_b = 100 μA for unit devices with thermistors with DW = 0.2 μm. Hence, the reduction in DW did not lead to an improvement in NEP. This study validates our previous investigation into the effect of width on such device parameters such as the temperature coefficient of resistance (TCR) and resistivity in the context of device miniaturization. The smaller grain size in thinner metal interconnects (thermistors) can be linked to the lower TCR and increased resistivity of the devices. Thus, the enhancement in responsivity in the design was largely due to the nanoscale meander design that, however, was detrimental to the noise response of the devices. These devices with nanoscale Ti meander thermistors deliver high responsivity in unit devices with scope for further miniaturization and have significant potential for application as on-chip integrable detector arrays.

Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e‐Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real‐time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI‐ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F‐measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT‐fog‐cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS‐key is describing text of that key PACS‐key describing text of that key.

This work proposes a unique design of charge plasma based junctionless SiGE source TFET with dual cavity and ferroelectric gate dielectric. The biosensor works on the principle of dielectric modulation for label-free detection, where the cavity lies under the source and gate metal just around the tunneling junction of the dual gate TFET. Nanocavity above the source and gate regions act as reservoir of biomolecules (to be detected) around the tunneling junction, letting the drain current modulate. Cavity dimensions and its placement have been optimized to achieve better current sensitivity. Dual gate architecture with laterally split dielectric is used to overcome the short channel impact and to enhance the current ratio. The biosensor can recognize neutral, positive, and negative charges with the highest drain current sensitivity and ION/IOFF ratio for biomolecule gelatin. It engages the principle of negative capacitance for better subthreshold swing and ON current for identification of biomolecules such as gelatin (k = 12), keratin (k = 8), streptavidin (k = 2.1), bacteriophage T7 (k = 6.3), and APTES (k = 3.57) at low voltage biasing. Finally, RF analyzes are carried out to explore the benefits of using negative capacitance where the biosensor acts as an intrinsic voltage amplifier exhibiting superior gain-bandwidth product (GBP) and cut-off frequency (fT), indicating its potential for high-speed operation and real-time sensing applications. The research yields repeatable outcomes for several calculated analyses.