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In today’s simulated path, global and customized music recommendation systems are crucial for reducing the deluge of alternatives, increasing person involvement, and growing a stronger bond between track libraries and their respective listeners. With the purpose of suggestion, a better user enjoys and lives aggressively in the virtual tune marketplace, it is vital to fix this predicament. The biggest obstacle is coming up with a solution that can accurately record all the unique aspects of musical preferences, such as style, environment, speed, and cultural attitude. For the cause of addressing those problems, in this paper, the Fuzzy Convolutional Generative Adversarial Network (FC-GAN) is a modern method that generates customized song recommendations with the aid of combining the strength of deep gaining knowledge of with fuzzy logic. The FC-GAN version makes use of convolutional neural networks (CNNs) to get a whole lot of traits out of audio samples, and fuzzy logic makes it better at taking pictures the subjective nature of people’s adjust preferences. With the use of an adverse education system, FC-GAN is capable of closely ensembling user possibilities with its music embeddings. In the track enterprise and beyond, the counseled FC-GAN structure has first-rate promise for diverse packages. It integrates without problems with existing track streaming systems to provide customized hints, which reinforces user engagement, retention, and happiness. In addition, FC-GAN can make it easier to compose, remix, and generate tune, giving manufacturers and artists extra ingenious tools to specific themselves creatively. By making use of melody datasets and user interaction logs from the actual international, each folks show that FC-GAN can offer extra accurate, diverse, and surprising personalized track recommendations. In addition, the builders validate FC-GAN’s practicality by checking out its scalability, computational efficiency, and resilience throughout distinctive person demographics and musical genres.

Duplex turning becomes an important metal cutting process due to unique features like higher productivity with better surface finish at lower specific energy and vibration. Such process requires two-cutting tools which are mounted parallelly and fed inward to cut the material from rotating surfaces. Such complex process needs modeling and optimization to analyze the effect of factors and identify the optimal cutting condition. This paper focuses to develop two models related to statistical and intelligent techniques especially responses surface methodology (RSM) and artificial neural network (ANN) for prediction analysis of duplex turning. Based on prediction potential, the ANN model is utilized to analyze the effect of various parameters (cutting speed, feed rate, primary depth-of-cut (DOC) and secondary-DOC on the responses as surface roughness and cutting forces (primary and secondary). Further, the parameters are optimized using Taguchi Methodology (TM) and experimentally validated. The results show that ANN model predicts the data with more precision than RSM model. Further, the optimal data are experimentally validated and significantly agreed with predicted data of ANN model with percentage error as 2.24%, 1.40% and 0.75% for surface roughness, cutting forces (primary and secondary), respectively.

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its functionality as functional elements of the architecture and shows the interaction between other components present in the architecture. The Internet of things (IoT) reveals as a frequent technology that allows accessing the physical objects present in the world. IoT systems utilize wireless sensor network to transmit and receive data by establishing communication. Wireless Sensor Networks transmits digital signals to the cyber-world for analyzing and processing the information into useful data by either formulating or communicating with the intelligent and innovative system. While talking about IoT and WSN, agents introduced in such environments assist in making decisions quickly by perceiving the input from the environment. The number of agents needed for an application depends upon the complexity of the problem. Multi-Agent architectures discussed in the article describe their association, roles, functionality and interaction. This paper gives a detailed survey of various agent/multi-agent learning architectures introduced over IoT and WSN. Moreover, this survey with the performance and the SWOT analysis on the Agent-based learning architecture helps the reader and paves a way to pursue research on Agent-based architectural deployment over IoT and WSN paradigms.

An intelligent integrated environment that provides decision support for printed circuit board (PCB) assembly is presented. This simulation-based decision support system enables manufacturing engineers, with little or no simulation experience, to exploit this technology fully for design and management of PCB assembly lines. PCB lines are selected for their simplicity in representation but stochasticity in nature of product demand and change. Within the environment is the capability to design graphically simulation models mapped to the real system, to define desired production targets, to execute a base language model and to generate results that can be intelligently analysed and presented for remedial action. A case example is also presented.

A caster-walker with ER fluid brakes installed in the rear wheels has been developed and tested. The brake actuation is "intelligent", in response to signals from a speed sensor and microprocessor, permitting it to prevent stumbling by the patient in mounting or dismounting the walker and on downslopes due to walker acceleration. The walker also serves effectively as a walk trainer. The ER brake is advantageous for small-wheeled vehicles because it is compact and simple, and consumes very little power. In this report, we describe the structure and characteristics of the brake, and key points in the brake design and development.

Large diameter aspherical reflector machining technology is a kind of technology which has big difficulty, long process cycle and high production cost. Therefore, the machining efficiency and accuracy are reduced. But now the intelligent large diameter aspherical reflector machining technology greatly increases the machining efficiency and accuracy. This paper introduces intelligent large diameter aspherical reflector machining technologies. Intelligent large diameter aspherical reflector machining technology is a technology to make the skill of the advanced optical machining person digitization and quantization. Compared with traditional optical machining technology, intelligent optical machining technology makes the judgement of the optical shape more accurate, control of the process more reliable, machining efficiency and machining accuracy greatly improved. Take 1.3m diameter aspherical reflector as an example. Before manufacture, PV of the mirror is 8722nm and RMS is 1230nm. After simulation and manufacture, PV of the mirror is 4948nm and RMS is 358nm and PV of the mirror is 5390nm and RMS is 356nm respectively. The PV error between simulation and manufacture is +8.93%. The RMS error between simulation and manufacture is -0.56%. These results prove that intellectualized large diameter aspherical reflector manufacture technology is highly efficient and accurate.

The purpose of this review is to discuss and summarize some of the interesting findings and applications of modified chitosan (MCS) and their derivatives in different areas of drug delivery. This review highlights the important applications of MCS in the design of various novel delivery systems like liposomes, microspheres, microcapsules, and nanoparticles. In addition to their well-known effects on drug solubility and dissolution, bioavailability, safety, and stability, their uses as recipients in drug formulation are also discussed. This review also focuses on various factors influencing inclusion complex formation because an understanding of the same is necessary for proper handling of these versatile materials. Some important considerations in selecting MCS in drug formulation such as their commercial availability, regulatory status, and patent status are also summarized.

By virtualization and service mode on demand of cloud computing, software engineering based on cloud computing should adapt new mode and method. How to combine traditional software development and deployment with cloud computing platform is still an indefinite problem. To resolve the problem in software development based on cloud computing, software engineering concept and properties are illustrated, and the software engineering methods and strategies are discussed and analyzed, and the implement application development and testing are performed. Through the software engineering based on cloud computing research and practice, some valuable strategies and methods are proposed, and the result shows that software engineering based on cloud computing has much benefits in software development flow and methods.

Although the asphalt pavement performed well in continuity and comfort, the properties of easy aging, temperature sensitive and easy cracking have confused the highway maintenance people for a long time. The intelligent asphalt pavement comes into being to complete the asphalt pavement and overcomes the shortcomings above. This paper introduces several existing intelligent asphalt pavements, summarizes the research status, puts forward the advantages and application foreground, and provides a reference for the future development of asphalt pavement. Current research showing that the intelligent asphalt pavement is a kind of new asphalt pavement that is time saving, labor saving, and energy saving.