Deep learning algorithms and their relevance: A review

Nowadays, the most revolutionary area in computer science is deep learning algorithms and models. This paper discusses deep learning and various supervised, unsupervised, and reinforcement learning models. An overview of Artificial neural network(ANN), Convolutional neural network(CNN), Recurrent neural network (RNN), Long short-term memory(LSTM), Self-organizing maps(SOM), Restricted Boltzmann machine(RBM), Deep Belief Network (DBN), Generative adversarial network(GAN), autoencoders, long short-term memory(LSTM), Gated Recurrent Unit(GRU) and Bidirectional-LSTM is provided. Various deep-learning application areas are also discussed. The most trending Chat GPT, which can understand natural language and respond to needs in various ways, uses supervised and reinforcement learning techniques. Additionally, the limitations of deep learning are discussed. This paper provides a snapshot of deep learning

Performance of re-ranking techniques used for recommendation method to the user CF- Model

The recent research work for addressed to the aims at a spectrum of item ranking techniques that would generate recommendations with far more aggregate variability across all users while retaining comparable levels of recommendation accuracy. Individual users and companies are increasingly relying on recommender systems to provide information on individual suggestions. The recommended technologies are becoming increasingly efficient because they are focusing on scalable sorting-based heuristics that make decisions based solely on "local" data (i.e., only on the candidate items of each user) rather than having to keep track of "national" data, such as items have been all user recommended at the time. The real-world rating datasets and various assessments to be the prediction techniques and comprehensive empirical research consistently demonstrate the proposed techniques' diversity gains. Although the suggested approaches have primarily concentrated on improving recommendation accuracy, other critical aspects of recommendation quality, such as recommendation delivery, have often been ignored

Investigation on Effect of Material Hardness in High Speed CNC End Milling Process

This research paper analyzes the effects of material properties on surface roughness, material removal rate, and tool wear on high speed CNC end milling process with various ferrous and nonferrous materials. The challenge of material specific decision on the process parameters of spindle speed, feed rate, depth of cut, coolant flow rate, cutting tool material, and type of coating for the cutting tool for required quality and quantity of production is addressed. Generally, decision made by the operator on floor is based on suggested values of the tool manufacturer or by trial and error method. This paper describes effect of various parameters on the surface roughness characteristics of the precision machining part. The prediction method suggested is based on various experimental analysis of parameters in different compositions of input conditions which would benefit the industry on standardization of high speed CNC end milling processes. The results show a basis for selection of parameters to get better results of surface roughness values as predicted by the case study results

Development of empirical relationships for prediction of mechanical and wear properties of AA6082 aluminum matrix composites produced using friction stir processing

AbstractFriction Stir Processing (FSP) has been established as a potential solid state production method to prepare aluminum matrix composites (AMCs). FSP was effectively applied to produce AA6082 AMCs reinforced with various ceramic particles such as SiC, Al2O3, TiC, B4C and WC in this work. Empirical relationships were estimated to predict the influence of FSP process parameters on the properties such as area of stir zone, microhardness and wear rate of AMCs. FSP experiments were executed using a central composite rotatable design consisting of four factors and five levels. The FSP parameters analyzed were tool rotational speed, traverse speed, groove width and type of ceramic particle. The effect of those parameters on the properties of AMCs was deduced using the developed empirical relationships. The predicted trends were explained with the aid of observed macro and microstructures

Impact of Cu doping on the structural, morphological and optical activity of V2O5 nanorods for photodiode fabrication and their characteristics

In this paper, we report a wet chemical precipitation method used to synthesize pure and Cu-doped V2O5 nanorods with different doping concentrations (CuxV2O5 where x = 3, 5 or 7 at%), followed by annealing at 600 °C and characterizations using several techniques. Indeed, a growth mechanism explaining the morphological evolution under the experimental conditions is also proposed. The XRD patterns revealed that all of the studied samples consist of a single V2O5 phase and are well crystallized with a preferential orientation towards the (200) direction. The presence of intrinsic defects and internal stresses in the lattice structure of the CuxV2O5 samples has been substantiated by detailed analysis of the XRD. Apart from the doping level, there was an assessment of identical tiny peaks attributed to the formation of a secondary phase of CuO. SEM images confirmed the presence of agglomerated particles on the surface; the coverage increased with Cu doping level. XPS spectral analysis showed that Cu in the V5+ matrix exists mainly in the Cu2+ state on the surface. The appearance of satellite peaks in the Cu 2p spectra, however, provided definitive evidence for the presence of Cu2+ ions in these studied samples as well. Doping-induced PL quenching was observed due to the absorption of energy from defect emission in the V5+ lattice by Cu2+ ions. We have proposed a cost-effective, less complicated but effective way of synthesizing pure and doped samples in colloidal form, deposited by the nebulizer spray technique on p-Si to establish junction diodes with enhanced optoelectronic properties

Subtle C–H···Hal (Hal = Cl, Br) Bonding as Predominant Synthon in the Assembly of Supramolecular Architectures Based on Luminescent Tin(IV) Complexes. Crystallography, Hirshfeld Surfaces, DFT Calculations, and Fluorescence

This report illustrates a successful C–H···Hal synthon-directed strategy to promote supramolecular aggregation of molecular luminescent complexes <b>1</b>–<b>6</b> into two- or three-dimensional supramolecular architectures. The tin complexes were prepared from the reaction of R_<i>n</i>SnHal_4–<i>n</i> compounds (R = <i>n</i>-Bu, Ph; Hal = Cl, Br; <i>n</i> = 0, 1) with (C₅H₄N)­HCN­(C₆H₄)­EH Schiff bases by either step-by-step synthesis [E = O (PyNO)] or multicomponent reaction [E = S (PyNS)]. Compounds <b>1</b>–<b>6</b> were characterized by IR, Raman, and ¹H, ¹³C, and ¹¹⁹Sn NMR spectroscopic studies as well as by X-ray diffraction studies. In addition, the fluorescent properties of all compounds were also investigated in the solid state and in THF solutions; the emission wavelengths ranged from orange to red (λ_max = 591–626 nm). Detailed structural characterization of the supramolecular organization of ordered solids revealed overall 2D and 3D interlinked networks driven by extensive C–H···Hal–Sn (Hal = Cl, Br) weak hydrogen bonds as primordial synthon and further stabilized by π···π stacking interactions as well as C–H···π, C–H···O, or C–H···N contacts. Hirshfeld surface analysis and DFT calculations were used to asses additional insights into crystal structural features