In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation networks with improved characteristics for connectivity. This paper highlights significant wireless and wired IoT technologies and their applications, offering a new categorization for conventional IoT network protocols. It provides an in-depth analysis of IoT communication protocols with detailed technical information about their stacks, limitations, and applications. The study further compares industrial IoT-compliant devices and software simulation tools. Finally, the study provides a summary of the current challenges, along with a broad overview of the future directions to tackle the challenges, in the next IoT generation. This study aims to provide a comprehensive primer on IoT concepts, protocols, and future insights that academics and professionals can use in various contexts.

Despite the numerous and noticeable inherited gains of Mobile Cloud Computing (MCC) in healthcare, its growth is being hindered by privacy and security challenges. Such issues require the utmost urgent attention to realize its full scale and efficient usage. There is a need to secure Health Information worldwide, regionally, and locally. To fully avail of the health services, it is crucial to put in place the demanded security practices for the prevention of security breaches and vulnerabilities. Hence, this research is deliberated on to provide requirement-oriented health information security using the Modular Encryption Standard (MES) based on the layered modeling of the security measures. The performance analysis shows that the proposed work excels, compared to other commonly used algorithms against the health information security at the MCC environment in terms of better performance and auxiliary qualitative security ensuring measures.

In this paper, a new active element called voltage differencing inverting buffered amplifier (VDIBA) is presented. Using single VDIBA and a capacitor, a new resistorless voltage-mode (VM) first-order all-pass filter (APF) is proposed, which provides both inverting and non-inverting outputs at the same configuration simultaneously. The pole frequency of the filter can be electronically controlled by means of bias current of the internal transconductance. No component-matching conditions are required and it has low sensitivity. In addition, the parasitic and loading effects are also investigated. By connecting two newly introduced APFs in open loop a novel second-order APF is proposed. As another application, the proposed VM APF is connected in cascade to a lossy integrator in a closed loop to design a four-phase quadrature oscillator. The theoretical results are verified by SPICE simulations using TSMC 0.18 μm level-7 CMOS process parameters with ±0.9 V supply voltages. Moreover, the behavior of the proposed VM APF was also experimentally measured using commercially available integrated circuit OPA860 by Texas Instruments.

Traditional multimedia experiences are undergoing a transformation as generative AI integration fosters enhanced creative workflows, streamlines content creation processes, and unlocks the potential for entirely new forms of multimedia storytelling. It has potential to generate captivating visuals to accompany a documentary based solely on historical text descriptions, or creating personalized and interactive multimedia experiences tailored to individual user preferences. From the high-resolution cameras in our smartphones to the immersive experiences offered by the latest technologies, the impact of generative imaging undeniable. This study delves into the burgeoning field of generative AI, with a focus on its revolutionary impact on image generation. It explores the background of traditional imaging in consumer electronics and the motivations for integrating AI, leading to enhanced capabilities in various applications. The research critically examines current advancements in state-of-the-art technologies like DALL-E 2, Craiyon, Stable Diffusion, Imagen, Jasper, NightCafe, and Deep AI, assessing their performance on parameters such as image quality, diversity, and efficiency. It also addresses the limitations and ethical challenges posed by this integration, balancing creative autonomy with AI automation. The novelty of this work lies in its comprehensive analysis and comparison of these AI systems, providing insightful results that highlight both their strengths and areas for improvement. The conclusion underscores the transformative potential of generative AI in image generation, paving the way for future research and development to further enhance and refine these technologies. This article serves as a critical guide for understanding the current landscape and future prospects of AI-driven image creation, offering a glimpse into the evolving synergy between human creativity and artificial intelligence.

Due to the absence of commercially available fractional-order capacitors and inductors, their implementation can be performed using fractional-order differentiators and integrators, respectively, combined with a voltage-to-current conversion stage. The transfer function of fractional-order differentiators and integrators can be approximated through the utilization of appropriate integer-order transfer functions. In order to achieve that, the Continued Fraction Expansion as well as the Oustaloup’s approximations can be utilized. The accuracy, in terms of magnitude and phase response, of transfer functions of differentiators/integrators derived through the employment of the aforementioned approximations, is very important factor for achieving high performance approximation of the fractional-order elements. A comparative study of the accuracy offered by the Continued Fraction Expansion and the Oustaloup’s approximation is performed in this paper. As a next step, the corresponding implementations of the emulators of the fractional-order elements, derived using fundamental active cells such as operational amplifiers, operational transconductance amplifiers, current conveyors, and current feedback operational amplifiers realized in commercially available discrete-component IC form, are compared in terms of the most important performance characteristics. The most suitable of them are further compared using the OrCAD PSpice software.