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In the past few decades recommender system has reshaped the way of information filtering between websites and the users. It helps in identifying user interest and generates product suggestions for the active users. This paper presents an enlightening analysis of various recommender system such as content-based, collaborative-based and hybrid recommendation techniques along with few optimization models that has been applied to improvise the parameters being considered by the aforementioned techniques. We explored 125 articles published from 1992 to 2019 in order to discuss the problems associated with the existing models. Various advantages and disadvantages of each recommendation model including the input methods has been elaborated. Critical review on research problems based on the explored techniques and future directions has also been covered.

Feature selection technique is a knowledge discovery tool which provides an understanding of the problem through the analysis of the most relevant features. Feature selection aims at building better classifier by listing significant features which also helps in reducing computational overload. Due to existing high throughput technologies and their recent advancements are resulting in high dimensional data due to which feature selection is being treated as handy and mandatory in such datasets. This actually questions the interpretability and stability of traditional feature selection algorithms. The high correlation in features frequently produces multiple equally optimal signatures, which makes traditional feature selection method unstable and thus leading to instability which reduces the confidence of selected features. Stability is the robustness of the feature preferences it produces to perturbation of training samples. Stability indicates the reproducibility power of the feature selection method. High stability of the feature selection algorithm is equally important as the high classification accuracy when evaluating feature selection performance. In this paper, we provide an overview of feature selection techniques and instability of the feature selection algorithm. We also present some of the solutions which can handle the different source of instability.

Purpose Dysarthria is a neuromotor speech disorder caused by neuromuscular disturbances that affect one or more articulators resulting in unintelligible speech. Though inter-phoneme articulatory variations are well captured by formant frequency-based acoustic features, these variations are expected to be much higher for dysarthric speakers than normal. These substantial variations can be well captured by placing sensors in appropriate articulatory position. This study focuses to determine a set of articulatory sensors and parameters in order to assess articulatory dysfunctions in dysarthric speech. Design/methodology/approach The current work aims to determine significant sensors and parameters associated using motion path and correlation analyzes on the TORGO database of dysarthric speech. Among eight informative sensor channels and six parameters per channel in positional data, the sensors such as tongue middle, back and tip, lower and upper lips and parameters (y, z, φ) are found to contribute significantly toward capturing the articulatory information. Acoustic and positional data analyzes are performed to validate these identified significant sensors. Furthermore, a convolutional neural network-based classifier is developed for both phone-and word-level classification of dysarthric speech using acoustic and positional data. Findings The average phone error rate is observed to be lower, up to 15.54% for positional data when compared with acoustic-only data. Further, word-level classification using a combination of both acoustic and positional information is performed to study that the positional data acquired using significant sensors will boost the performance of classification even for severe dysarthric speakers. Originality/value The proposed work shows that the significant sensors and parameters can be used to assess dysfunctions in dysarthric speech effectively. The articulatory sensor data helps in better assessment than the acoustic data even for severe dysarthric speakers.

This paper demonstrates the Refractive Index (RI) sensing characteristics of the proposed evanescent wave absorbance (EWA) sensor in reflective mode. The end reflective-EWA (ER-EWA) sensor is fabricated using Tollens’ reagent for depositing silver (Ag) layer throughout the length of sensing region and to fabricate in-line reflective mirror at the end of sensing probe. The fabricated ER-EWA sensor measures changes in RI of seawater sample with a sensitivity of 48.036 arbitrary unit per RI unit (a.u./RIU) in the range of 1.332 RIU to 1.344 RIU. The proposed sensor is further analyzed for its sensitivity towards changes in salinity at constant turbidity and variation in turbidity at constant salinity values. By validating the functionality of the proposed ER-EWA sensor against commercial sensors, accuracies for salinity and turbidity measurements are obtained as 98.1791% and 93.5737%, respectively. This proves the potential of the proposed ER-EWA sensor in real time monitoring of ocean parameters such as salinity and turbidity.

This paper primarily focuses on a systematic top-down approach in the structural and feasibility analysis of the novel modular system which integrates a 5 kW wind turbine with compressed air storage built within the tower structure, thus replacing the underground cavern storing process. The design aspects of the proposed modular compressed air storage system (CAES) were executed to eliminate the multistage air compression process and overcome the adverse safety issues of underground caverns prevailing in the conventional system. Moreover, the waste heat treatment can be eliminated owing to its low-pressure operation (8 bar max) which reduces the complexity of the system. A detailed structural analysis was conducted for the foundation, tower structure, storage chamber and wind blades using the Structural Analysis and Designing Program (STAADPRO) and Analysis System (ANSYS) software for a basic wind speed of 180 km/hour (50 m/sec) as per the IS 875 (Part 3): 2015. Certainly, the results proved that the system remained stable at its natural and sub-harmonic frequencies. However, to ensure the feasible operation of the concept, a prototype model was tested for wind speeds starting from 3 m/s and was found to compress up to a maximum of 8 bar pressure.

Non-invasive extraction of fetal electrocardiogram (FECG) by processing the abdominal signals is emerging as a promising approach in the areas of obstetrics and gynecology. This paper presents a two-stage improved non-linear adaptive filter for FECG extraction. The reference input to the adaptive noise canceler (ANC) is first processed using an adaptive neuro-fuzzy inference system (ANFIS) to estimate the non-linear maternal component in abdominal signals. A parallel sub-filter (PSF) ANC is proposed to assess the fetal ECG from the abdominal signal. The PSF-ANC decomposes a single adaptive filter into multiple sub-filters to improve the convergence performance. The filter coefficients of PSF-ANC adaptively obtained using normalised least mean square algorithm by minimizing the mean square error. Different error and common error algorithms are proposed based on the computation of the error signal. A synthetic data from the FECG synthetic database is used to evaluate the convergence performance. Two real-time data from the Daisy database and the Non-invasive FECG database from Physionet are used to evaluate the proposed ANFIS-PSF’s performance qualitative and quantitatively. The results justify the performance improvement of proposed ANFIS-PSF ANC compared to the state of art techniques. The proposed scheme achieves a sensitivity of 97.92%, 94.52% accuracy, a positive predictive value of 94.66%, and an F1 score of 96.12%.

This paper brings forth the fault tracking(estimation) problem for Fractional-Order Takagi-Sugeno Fuzzy(FOTSF) uncertain model subject to time-varying actuator fault and actuator saturation. In order to maintain the stability of considered system, fuzzy fault-tolerant sliding-mode controller is constructed based on fast adaptive fault estimation algorithm. Precisely, stability analysis is performed for the FOTSF model based on state and fault estimations by using the Lyapunov’s stability theorem. More precisely, the sufficient constraints for stability of formulated model are built in proposed theorems. Eventually, two numerical exemplars including one chaotic model of Rossler are issued to support the proposed results and to demonstrate the efficacy of prescribed controller.

In recent research, very large-scale integration (VLSI) design and its implementation plays a major role. It has been considered as compact and contains additional techniques. However, it undergoes...

Hemostasis remains the foremost vital role of cautery, without hemostasis we have the danger of bleeding which results in the worst-case of a retro neural structure intumescence and visual impairment. Hemostasia permits for a safer and a lot of favorable outcomes, besides hemostasia cautery is used for different functions. We use it ordinarily in medicine to induce timely structure for patients with dry eyes, to shut conjunctivas incisions also as a surgical marker. Heat is generated through associate electrical current and flows to a metal tip to coagulate blood vessels to stop bleeding. Electrocautery comes within the monopolar or bipolar style with monopolar electrocautery and is applied through a hand-held active conductor which travels back to the generator through an inactive conductor connected to the patient (the grounding pad). Therefore, the patient is an element of the circuit. The inactive conductor is usually placed on the outsized space of the body to avoid physical heat phenomenon. With bipolar electrocautery, the existing is passed between one extractor tip to the opposite and a restricted quantity of tissue is cauterized in between. The existing is unfolded with monopolar electrocautery in which there's a lot of tissue harm and the tissue recovery takes a longer time.

Telehealth is an important tool utilized to provide remote clinical care and has increased in prevalence during the coronavirus disease of 2019 (COVID-19) pandemic. It allows providers to conduct safe, timely, and high-quality ambulatory care for patients without increasing risk of disease exposure for both parties. Major organizations including the Centers for Disease Control and Prevention and American College of Obstetrics and Gynecology have released recommendations encouraging the use of telehealth systems for patient care. In obstetrics and gynecology, practice of telehealth has not been commonplace and no practical procedural guidelines have been published. The authors have created such guidelines for use of telehealth in a moderate-risk academic generalist practice in response to the COVID-19 pandemic. This document highlights the process to determine which obstetrics and gynecology patients are candidates for telehealth, the frequency of follow-up, and the technical aspects of designing and delivering a de novo telehealth system. The guidelines were vital in providing structure amid a sudden transition in an academic setting while ensuring patient and provider safety.

Purpose B-mode ultrasound images are used in identifying the presence of fat deposit if any in carotid artery. The intima media, lumen, bifurcation boundary is detected by the echogenic characteristics embedded in the carotid artery. Methods A fully automatic self-learning based segmentation is proposed by extracting the edges by a modified affinity propagation, which are given as inputs to the Density Based Spatial Clustering of Applications with Noise (DBSCAN) for super pixel segmentation. The segmented results are analyzed with Gradient Vector Flow (GVF) snake model and Particle Swarm Optimization (PSO) clustering based segmentation using various performance measures. Results The proposed parameter free, fully automatic segmentation method combining Affinity propagation and DBSCAN are evaluated for a database of 361 images and gives reinforced results in the longitudinal B-mode ultrasound images. The proposed approach gives an improved accuracy of 12% increase when compared with the manual segmentation and 15% compared with segmentation by affinity propagation and DBSCAN when performed individually. The average Root Mean Square Error (RMSE) is 110 ± 44 µm. Conclusion Extracted edge points are used for clustering in a fully automated carotid artery segmentation approach.

This paper proposes a real-time simulation model to simplify the research and outcome of a portable thermoelectric generator (TEG) system in real conditions. Consequently, the model is divided into three parts: conventional, segmented, and hybrid TEG systems. The conventional TEG system consisted of Bi2Te3 as the p-type and the n-type materials, whereas the segmented and the hybrid TEG systems consisted of a different combination of materials, including PbTe and Sb2Te3. The optimization of the TEG system length was carried out to achieve the highest power output, which was found to be 2 mm. In addition, thermomechanical stress distribution analysis of the module was conducted to determine the maximum load the TEG system could withstand before undergoing fracture, depending upon the yield strength of the material. The stress was analyzed in all three TEG systems, and the results were evaluated. Results were observed from the optimized length at 2 mm. The conventional, segmented, and hybrid TEG systems showed maximum power output of 147.122 mW, 171.934 mW, and 550 mW, respectively, with a temperature difference at 50 K.

The fiber fraction in a composite will have a significant influence on its behavioural aspects and is reported in the current study. With different fiber fractions (0, 12, 24, 36 and 48 wt%) of the areca sheath fiber reinforcing agent, composite specimens were prepared and its physical and mechanical properties were examined. The measured density of the neat epoxy to the higher fiber fraction composite specimen (48 wt%) varies from 1.147 to 0.9524 g/cm3, from which it is evident that the density decreases with increase in the fiber concentration. On the other hand, void fraction for a neat epoxy is 0.26% which increases with the increase in the fiber concentration and is 6.14% for the 48 wt% fiber loaded specimen. An optimum values of investigated mechanical properties such as the hardness (72.56 HRB), Young’s modulus (1.98 GPa), and the Impact strength (4.724 J) have been observed with the increase in fibre fraction. The flexural strength for the neat epoxy is 46.28 MPa starts decreasing with the inclusion of fibers and reaches 12.05 MPa at a higher fraction of 48 wt%. The similar declining behaviour may be observed for the flexural modulus of 0.26 GPa and tensile strength of 14.02 MPa at high fiber fraction.

Aluminium – silicon alloys are most frequently adopted for numerous engineering applications such as automobile, marine and aircraft manufacturing industries due to its high strength to weight ratio. Turning is considered as one of the most important machining processes for producing components of desired shape with dimensional accuracy. Manufacturing processes influences the functional performance of the components. This present article discuss about the optimization of machining parameters in turning LM 25 aluminium alloy in wet and dry environments. The parameters considered for machining are cutting speed, feed and depth of cut with the performance measures as surface roughness, material removal rate and cylindricity error. Taguchi’s L9 orthogonal array was employed for conducting the experiments. The parameters are optimized simultaneously by Grey Relational Analysis (GRA) method. The impact of the machining parameters on performance measures were compared on both wet and dry machining environments.

The article presents the study of the deformation of an SMA bimorph actuator during static heating. The thermomechanical behavior of thin film CuAlNiMn shape memory alloy (SMA) on polyimide substrate is analyzed using finite element method. The analysis is carried out for three different geometries to obtain the displacement when temperature is varied. The actuation characteristics of the SMA bimorph are studied through direct heating and joule heating techniques. The surface morphology and composition of the bimorph sample are obtained using the Scanning Electron Microscope (SEM). The numerical analysis of the thermomechanical behavior of a thin film CuAlNiMn SMA bimorph is carried out using the finite element method. The procedure is applied for a double-legged cantilever configuration of the bimorph to obtain the actuation in terms of displacement; by varying the temperature. The same is repeated for semi-circular and pentagon shapes of the bimorph to obtain the influence of area on the displacement. It is observed that SMA bimorph displaces with the rise in temperature and the magnitude of displacement increases with the increase in surface area of different geometries. The physical actuation characteristic of CuAlNiMn-polyamide bimorph is studied by thermal heating and electrical heating of the sample, which confirm the shape memory effect of the SMA and provide the displacement characteristics of the bimorph when applying an electrical current.

Cognitive computing is the mirroring of human brain and this is made possible by using natural language processing, pattern recognition and data mining. By mirroring the human brain (Cognitive computing system), helps to solve some of the complicated problems without much of human supervision. In the fast-changing world, the major challenge every organization facing is difficulty in retaining its employees. Employees may leave an organization due to low salary, overwork, lack of opportunities and recognition, work culture, work-life imbalance etc. Better ways to retain employees is to understand their requirements and fulfill them. The proposed employee feedback sentiment analysis system collects the employee feedback reviews from open forums and perform sentiment analysis using Recurrent Neural Network – Long Short-term Memory (RNN-LSTM) algorithm. On performing Sentiment analysis, employee review comments are classified as Positive or Negative. A report is generated and sent to the HR of the organization as webapp or mobile app. The report has total number of positive and negative comments and positive and negative counts with respect to salary, work pressure etc. With the report, the organization can arrive at identifying social sentiments of their brand and may take corrective actions to retain employees which benefits both organization and employees. This paper also captures the performance of various models in training and predicting the employee feedback dataset and the models evaluated are Logistic Regression, Support Vector Machine, Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, Decision Tree Classifier and Gaussian Naïve Bayes. The classification report and accuracy of each model is captured. The dataset size was gradually increased from 200 to 1000 and accuracy was predicted for each model. It was identified that the accuracy of machine learning algorithms was ranging between 66% to 85%. On training RNN-LSTM algorithm with dataset of size 30 k, the accuracy was 88%. It was identified that Deep learning algorithm RNN-LSTM performs better with huge dataset. Increasing dataset size still increase the performance of RNN-LSTM algorithm in training and prediction. Thus, the objective function of the proposed model to perform sentiment analysis on employee feedback review comments is achieved successfully.

Detection of densely interconnected nodes also called modules or communities in static or dynamic networks has become a key approach to comprehend the topology, functions and organizations of the networks. Over the years, numerous methods have been proposed to detect the accurate community structure in the networks. State-of-the-art approaches only focus on finding non-overlapping and overlapping communities in a network. However, many networks are known to possess a hidden or embedded structure, where communities are recursively grouped into a hierarchical structure. Here, we reinvent such sub-communities within a community, which can be redefined based on nodes similarity. We term those derived communities as hidden or hierarchical communities. In this work, we present a method called Hidden Community based on Neighborhood Similarity Computation (HCNC) to uncover undetected groups of communities that embedded within a community. HCNC can detect hidden communities irrespective of density variation within the community. We define a new similarity measure based on the degree of a node and it’s adjacent nodes degree. We evaluate the efficiency of HCNC by comparing it with several well-known community detectors through various real-world and synthetic networks. Results show that HCNC has better performance in comparison to the candidate community detectors concerning various statistical measures. The most intriguing findings of HCNC is that it became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks.

Deep learning plays a major role in the present video processing tools and algorithms. To​ alleviate the limitations of present in-loop filters in the scalable extension of HEVC (SHVC), a combined residual network (CResNet) in-loop filtering is proposed in this paper. The proposed CResNet in-loop filter exploits layer information available in the spatial temporal domain to restrain the visual artifacts like blocking and ringing. Particularly, the block information related to current and co-located blocks of the spatial and temporal base layer reference frames are considered to optimize the in-loop filtering. The proposed architecture has four convolution layers at the base layer and two convolution layers at the enhancement layer that significantly reduces the coding complexity and memory. Additionally to completely train the input content and also to enhance the in-loop filter performance, the on/off level control flag for coding tree Unit (CTU) is sensed using rate distortion optimization (RDO) approach. The experimental results demonstrate that the proposed architecture provides up to 6.2% to 7.2% reduction in bit rate and 1.01 dB improvement in PSNR compared to the standard SHVC codec.

The removal of irrelevant and insignificant features from the high-dimensional dataset is a necessary prerequisite for the exploration of information. Meta-heuristic optimization techniques have be...

Reliability of each state of process in many chemical process industries largely relies upon water and vitality supplies. In this way, there is great necessity to have an improved and controlled smart energy distribution network (SEDN) in industries. In SEDNs, sensor information related to flow control and optimization serves as a basis for modelling of energy management systems. Therefore, it is important to ensure that sensor data are accurate and precise. However, they are affected by random noise and measurement biases, which compromise the quality of measurements. Data Reconciliation (DR) is one such approach popularly used in industries to reduce the adverse impact of random errors present in pipe flow measurements. In this study, Python-based simulations of weighted least squares (WLS) and principal component analysis (PCA) based DR techniques are implemented on the selected flow streams of SEDN, and reconciled estimates are obtained. The results show that Root Mean Square Error (RMSE) is the best performance metric since it is more sensitive to small changes in the measurement values and the reconciled estimates. Further, it is observed that PCA-DR performs better than WLS-DR in reducing the random error (and thereby achieving greater precision of measured values). HIGHLIGHTS Application of data reconciliation (DR) techniques to treat random errors present in flow sensor data used by water distribution networks.; Selection of best performing metric to evaluate data reconciliation (DR) techniques.; Analyze the performance of selected DR techniques for small and large scale networks using Python-based simulation.

Recently 1:12 magnets of Sm-(Fe,V) have shown promising coercivities and the potential to be alternative rare-earth-lean permanent magnets. In this work, we investigated the effects of partial substitution of Cu, Mo and Ti for V in the magnets prepared by hot compaction and hot deformation of mechanically milled powders. The microstructure of the Sm-Fe-(V,Cu) and Sm-Fe-(V,Ti) hot-deformed magnets consisted in fine grains with sizes between 50 and 150 nm. The Sm-Fe-(V,Cu) magnet showed the best performance with μ0H c = 0.96 T, μ0M r = 0.49 T, (BH) max = 42 kJ m − 3 and T C = 362 ∘ C. Atom probe tomography of this magnet revealed the presence of a thin Sm17.5Fe71.5V8Cu3intergranular phase of 3-6 nm surrounding the 1:12 nanograins. The addition of a small amount of Cu, not only improved the magnetic properties but also hindered the grain growth during hot deformation. Micromagnetic simulations of the magnetization reversal agreed with the experimental values of coercivity. The presence of the intergranular phase reduces the number of grains that switch simultaneously.

The ball on beam system is a highly nonlinear laboratory model which helps to understand the modern and classical control theory. This paper emphasizes the position control of a ball on beam system which is actuated by shape memory alloy wires in an antagonistic configuration. The shape memory alloy actuator provides the necessary force to actuate the beam in the left and right directions of angular position. To achieve effective control of any system it is necessary to design the controller optimally but it is very difficult due to the requirement of proper tuning which is not feasible by the conventionally designed controller. The objective of this paper is the optimal design of the well-received PID controller by using intelligent tuning techniques to drive the system with the best performance. Since the genetic algorithm and particle swarm optimization are the numerical algorithms that have been extensively used to solve all types of complex engineering problems to improve their performance, these algorithms are used to tune the PID controller. The results of genetic algorithm and particle swarm optimization are benchmarked with a conventionally designed PID controller.

In the current era of big data, the recommender system aspires to provide users with a tailored set of personalized items from a pool of a large population. The most popular collaborative filtering system performs this information filtering process by computing similarity among users or items. This paper proposes a similarity metric that comprises of weights and values. Values are calculated by considering the matching set of users for which similarity is to be computed. The optimal values of weights are decided using an upgraded form of the Crow Search Algorithm (CSA). The exploration and exploitation stability of CSA is improvised by making use of Levy flight diffusion, adaptive operator adjustment, and event factor. The performance of the implemented metaheuristic approach is validated on Jester, MovieLens 100K, and MovieLens 1M dataset. Comparative analysis of proposed model against several other traditional metaheuristic based personalization systems reveal that our model is less delicate to the dimension of datasets and it also presents exceptional refinement in terms of prediction complexity and accuracy.

In today’s digital world, data transmission and storage is becoming a massive problem. This is because the data produced by various sensors worldwide is outstripping the ability to store them. Pre-processing the entire data before transmission is the best solution for reducing the storage issue. ‘Compressed sensing’(CS) is a pre-processing technique that exploits the sparsity of the signal for sampling the data. Since most of the natural signals are sparse, CS allows sampling at a rate lesser than that required in Nyquist sampling theorem. However, in conventional CS, sampling is done for the entire image at once which increases processing time and reduces visual quality. In block compressed sensing (BCS), blocks of the images are processed simultaneously which increases processing speed and decreases the processing time. To improve the quality of the reconstructed signal, a variant of BCS, Adaptive block compressed sensing (ABCS) is used. This review paper studies the advantages, challenges and applications of applying ABCS for image compression.

In the current period of energy development, it is very complex to produce energy from agricultural wastes due to the involvement of multiple criteria such as social, economical and environmental factors. In this study, a hybrid multi-criteria decision-making (MCDM) model based on the weight obtained from analytical hierarchy process (FAHP) has been utilized for ranking. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation based on Distance from Average Solution (EDAS) are proposed to evaluate the possibilities of utilizing locally available biomass. For this purpose, a number of criteria are defined from the viewpoint of yielding maximum bio-oil during pyrolysis. The proposed methods are having excellent agreement with each other, and they are exactly matched with the experimental results. This study consists of seven biomass alternatives with seven evaluation criteria. Out of seven selected biomass materials, sugarcane bagasse is ranked top. The experimental results confirmed the prediction with maximum bio-oil yield of 48.5 wt% obtained from sugarcane bagasse. At the end of the study, the obtained bio-oil from top ranked biomass material was analysed for physical, elemental and chemical compositions using Fourier-transform infrared (FTIR) spectroscopy and gas chromatography (GC) for its utility assessment. This study gives new insights into decision-making, specifically thermochemical conversion process.

The intention of this study was to prepare polyvinyl alcohol (PVA) and carboxymethyl cellulose (CMC) films and to improve their physicochemical properties by cross-linked with amino acids. Thus, th...

The cloud is a collection of resources like hardwares and softwares etc., and these resources are shared between public and private cloud for better resource utilization. The entire idea behind our model is to concentrate on how to magnify the resource usage on Multi sharing system with reservation policy plans in hybrid cloud environment and how to provide access control to Carry Forward of Unused Resources. In existing system, the cloud offers various types of reservation plans and on-demand plans with restrictions. Scope for either under or over provisioning of reserved resources was not taken care of. It was very much restricted to sharing of resources with multiple users. This was leading to an over and under provisioning of resources and no option was provided to carry forward the unused resources and there was no access control for their contribution of resources. So, to overcome with this problem “Multi Sharing Resources in Hybrid Cloud (MSRHC)” model is proposed. The proposed model mainly aims to enable sharing of multiple resources by multiple users and providing access control to unused resources. The experimental results show that by using Carry Forward of Unused Resources (CFUR) and Access Control for Contributed User (ACCU) approach, the results have produced 100 percent of resource utilization and full fledge access only to contributor user which reduces the cost.

This paper presents the design, physical modelling and testing of electromechanical logic elements using shape memory alloy (SMA) wire actuated hinged beam switches. Microelectromechanical system logic devices actuated by electrostatic, pneumatic and electrothermal actuation mechanisms are used as alternatives to the conventional logic systems for harsh operating conditions such as ionizing radiations and high temperature. Microoptoelectromechanical system logic device actuated by electrothermal cantilevers can be used in the same applications. Mechanical logic units using linkages and joints in automobiles require an appropriate actuation mechanism. The actuation mechanism used in each case is inherent to typical limitations and design complexity. This work contributes to the evolution of the mechanical logic design architecture to present simplicity to the logic system, involving SMA actuated hinged beam switches. The performance of the SMA hinged beam switch is evaluated by assessing the minimum current required for the actuation of the SMA to reduce the switching delay; also the switching transients during ON and OFF are observed for a static input. The functionality of basic electromechanical logic elements composed using SMA hinged beam switch is verified.

Car segments have an expanding rivalry in the market and it will in general create development in the current items by either succeed with another or altered propelled material items. A suspension framework is one of the most intriguing push territories on vehicle structure. This task effort is worried about the plan and investigation of mechanical portrayal of leaf springs that are in effect despite everything utilized generally in cars as suspension segments. Car makers have the due significance on enhancement for the mileage of the vehicle which thusly on a structure perspective the weight decrease is the most concerned one. There by the strength-weight proportion idea has been created and composite material is executed. The utilization of composite leaf spring rather than ordinary steel leaf spring is favored for the examination due to its high solidarity to weight proportion. It is made convincing to decrease the weight of the leaf spring without relinquishing the solidness and burden conveying limit of the spring by the usage of composite materials. The target of this task work is the plan and examination of the exhibition qualities of Glass Fiber Reinforced plastic (GFRP) with iron powder as a composite leaf spring and correlation with traditional leaf spring. The composite example is created utilizing hand layup technique. Exploratory work have been completed an all Universal Testing Machine for the composite and ordinary steel leaf spring and approved with the ANSYS investigation. The investigation results are connected so near test.

The association rule based classification is imperative in the disease prediction owing to its high predictability. To deal with the sensitive data, we propose an algorithm using fuzzy inference set. The association rule mining is improved further by generating an associative rules for each item of the data set. The ranking of the item in the data set is based on the information mass value estimated. The mass value represents the depth of the item in the data set and its class. Selection of the certain item set is done based on the mass value of different associated items. According to the associative items selected, the association rule mining is performed. For each association rule generated, this method calculates the impact of each object from the rules based on how fuzzy rules are generated. Fuzzy impact rules indicate symptoms and diagnostic labels. A class of disease posses disease influence measure that predicts each class of disease has changed. The proposed algorithm improves the classification efficiency and reduces the error rates.

Internet of Underwater Things (IoUT) consists of a large number of interconnected resource-constrained underwater devices that are capable of monitoring vast unexplored water bodies. Specifically, these devices are equipped with cameras to capture the underwater scenes and communicate them with each other and also with the cloud. However the data generated is very high which limits the performance of the IoUT devices in terms of computational capabilities and battery lifetime. Block Compressed Sensing technique which performs block by block fixed sampling can be utilized to achieve data compression however it ends up in image distortions after reconstruction. To unravel this issue, Adaptive Block Compressive Sensing technique is used. In this paper, Energy based Adaptive Block Compressive Sensing (EABCS) with Orthogonal Matching Pursuit reconstruction algorithm is proposed to improve the sampling performance and visual quality of the reconstructed image. Sparse binary random matrix is used as measurement matrix as it is highly sparse. With this energy based adaptive strategy, higher measurements are assigned to blocks with higher energy and vice versa. The proposed EABCS technique has achieved better compression with approximately 25–30% of measurements/samples with an increase in Peak signal to noise ratio of about 3–5 dB and structural similarity Index of around 0.1–0.3 with respect to other adaptive strategies. Percentage of space saving is also about 60–70%.

Recommender system is one of the most popular technique used for information filtering. It helps in discovering hidden knowledge patterns from a large set of ubiquitous products and services. The most popular approaches such as collaborative filtering suffers from the complication of data sparsity, overspecification and high computation complexity when dataset drifts from scarcity to abundance. In this regard, we developed a hybrid model that contemplates between accuracy and computation time in order to generate a real-time most relevant items for the users. We made use of imputation technique, fuzzy logic using novel similarity technique and McCulloch-Pitts(MP) Neuron to cope up with aforementioned complications. The experimental evaluation on MovieLens dataset shows that the proposed model yields high efficiency and effectiveness. We tested the resultant classification accuracy of our proposed model using precision, recall and f1-score.

This article is devoted to resolving the issue of time-varying fault in the fractional-order financial system subject to constant delay. A fault-tolerant control is addressed to solve the faults which occur by unpredictable affairs in the financial system. Further, a set of adequate constraints in the form of Linear Matrix Inequalities are derived to ensure asymptotic stability of the considered system under the theorem of Lyapunov. More precisely, Lyapunov–Krasovskii candidates are employed independently with various state-space trajectories such as interest rate, investment demand and price index. Lastly, a numerical example is provided to support the constructed theoretical results.

Copper-based sintered friction materials are most suitable for heavy-duty off-road vehicles, trains, aircraft, and military applications. This present study aims to investigate the tribological performance, dominating wear mechanism of an existing copper-tin sintered friction material that is being used in armoured fighting vehicle. The brake pad was tested as per IS 2742 using the chase test machine. Especially fade cycle was carried out till 441℃ to analyze the frictional response of the material. Physical, mechanical, and tribological properties were evaluated as per industrial standards. Morphological analysis was carried out using field emission scanning electron microscopy, and wear debris analysis was carried out using scanning electron microscopy–energy dispersive X-ray analysis and X-ray diffraction analysis. The dominating wear mechanisms were found to be delamination and abrasive wear. The investigated results showed a less wear rate of 0.05 cm3/MJ. However, results seem to be better for high-energy applications by exhibiting excellent mechanical properties.

The deblocking filter in the standard H.265 SHVC reduces blocking artifacts at the edges and block boundaries of coded frame with exhaustive computation. But the maximum removal of these blocking artifacts is not achieved; also the computational complexity is still a burden. This made many research works discussed related to maximum removal of blocking artifacts without considering computational complexity. In this paper, a convolutional neural network (CNN)-based deblocking filter is proposed for SHVC in H.265 that removes blocking artifacts with less computation. The proposed CNN framework learns the reconstructed samples of the input frame in a video sequence. The blocking artifacts are efficiently detected by the preprocessing unit which is considered as the first convolution layer. Next, the features are extracted for the frames in a video based on patch by employing kernel and stride for scanning the complete frames in a video. The normalization is applied from base layer to enhancement layer in the CNN framework to preserve sharpness in the video by removing artifacts generated due to inter-layer prediction and quantization. The network model is trained by rectified linear unit activation function to perform nonlinear mappings. In addition, the CNN-based deblocking filter achieves less computation due to max-pooling and soft-max layers in CNN. The simulation results are conducted that produce an average of 0.76 dB increase in PSNR and 57% time saving compared with the standard SHM reference encoder.

Strictly Chordality- k graphs ( S C k graphs) are graphs which are either cycle free or every induced cycle is exactly k , for some fixed k , k ≥ 3 . Note that k = 3 and k = 4 are precisely the Chordal graphs and Chordal Bipartite graphs, respectively. In this paper, we initiate a structural and an algorithmic study of S C k , k ≥ 5 graphs.

Boron is a promising additive to hydrocarbon aviation fuels to enhance the volumetric energy density, which in turn leads to better specific impulse in hypersonic ramjet propelled missiles. This perspective article provides a summary of the different preparation methods of nanoboron, with an emphasis on its properties, quality and ease of scale-up. Self-propagating high temperature synthesis is projected as a promising method to produce amorphous nanoboron of high purity via metallothermic reduction of boron oxide. The effects of operating conditions such as time, temperature and concentration of diluent (NaCl) on particle size distribution, purity and yield are evaluated, and compared with the literature. In order to prepare slurries of boron in hydrocarbon fuels that are stable and long-standing, the addition of surfactants is necessary. The importance of Bancroft’s rule and hydrophilic-lipophilic balance are evaluated for the preparation of stable boron-fuel suspensions. Examples of stabilization of micron boron and nanoboron in jet fuels using different non-ionic surfactants like Spans and Tweens are discussed. Without any surfactant, complete settling of 10 wt% micon/nanoboron in Jet-A1 and JP-10 fuels occurred within 10 min. An optimized formulation of Span-80 and Tween-80 delayed the complete sedimentation of micron boron by a few days, and led to a much better stability of nanoboron in the fuels for over 7 days. Finally, the challenges and future prospects in scaling up of the nanoboron production process, and the preparation of slurry fuels with good stabilization of boron are discussed.

This paper proposes a novel behavior-inspired recommendation algorithm named TimeFly algorithm, which works on the idea of altering behavior of the user with respect to time. The proposed model considers solving two recommendation problems (fluctuating user interest over time and high computation time when dataset shifts from scarcity to abundance) and presents a real application of the proposed method in the field of recommendation engine. It describes a system which enrolls the changing behavior of user to furnish personalization suggestions. The results obtained by TimeFly are compared with the results of other well-known algorithms. Simulation results on 100K, 1M, 10M, and 20M MovieLens dataset reveal that using TimeFly leads to high accurate predictions in less computation time.

An electrochemical sensor using lanthanide element Gadolinium doped Zinc oxide and functionalized Multiwalled Carbon Nanotube (Gd-ZnO:f-MWCNT) fabricated electrode was presented for the simultaneous determination of vitamins (Riboflavin (VB₂) and Pyridoxine (VB6)). The nanocomposite was characterized by FESEM, EDS, XRD and FTIR techniques. The CV, EIS and DPV techniques were used to evaluate the electrochemical properties of the nanocomposite. Under optimum conditions, the peak current of VB₂ and VB6 are linearly proportional to its concentration in the range from 0.05 to 10 μM and the estimated detection limits were 0.009 and 0.001 μM for VB₂ and VB6 respectively. The sensitivities calculated were 0.94 μA nM-1 cm-2 (VB₂) and 1.43 μA nM-1 cm-2 (VB6). In addition, the proposed electrode was exhibited high selectivity, reproducibility, as well as long-term stability for the determination of vitamins. Finally, the vitamins contained in pharmaceutical formulation and commercial orange juice has been determined by the proposed sensor and the obtained results were validated with HPLC.

6-Thioguanine encapsulated chitosan nanoparticles (6-TG-CNPs) has formulated by the ionic-gelation method. Morphologically, the 6-TG-CNPs were spherical and showed mean size, PDI, zeta potential, and entrapment efficiency of 261.63 ± 6.01 nm, 0.34 ± 0.10, +15.97 ± 0.46 mV and 44.27%, respectively. The IR spectra confirmed the 6-TG complex with chitosan. The in vitro drug release profile of 6-TG-CNPs revealed an increase in sustained-release (91.40 ± 1.08% at 48 h) at pH 4.8 compared to less sustained-release (73.96 ± 1.12% at 48 h) at pH 7.4. The MTT assay was conducted on MCF-7 and PA-1 cell lines at 48 h incubation to determine % cell viability. The IC50 values of 6-TG, 6-TG-CNPs, and curcumin for MCF-7 were 23.09, 17.82, and 15.73 μM, respectively. Likewise, IC50 values of 6-TG, 6-TG-CNPs, and curcumin for PA-1 were 5.81, 3.92, and 12.89 μM, respectively. A combination of 6-TG-CNPs (IC25) with curcumin (IC25) on PA-1 and MCF-7 showed % cell viability of 43.67 ± 0.02 and 49.77 ± 0.05, respectively. The in vitro cytotoxicity potential in terms of % cell viability, early apoptosis, G2/M phase arrest, and DNA demethylating activity of 6-TG-CNPs alone and combination with curcumin proved to be more effective than that of 6-TG on PA-1 cells.

A microarray dataset contains thousands of DNA spots covering almost every gene in the genome. Microarray-based gene expression helps with the diagnosis, prognosis and treatment of cancer. The nature of diseases frequently changes, which in turn generates a considerable volume of data. The main drawback of microarray data is the curse of dimensionality. It hinders useful information and leads to computational instability. The main objective of feature selection is to extract and remove insignificant and irrelevant features to determine the informative genes that cause cancer. Random forest is a well-suited classification algorithm for microarray data. To enhance the importance of the variables, we proposed out-of-bag (OOB) cases in every tree of the forest to count the number of votes for the exact class. The incorporation of random permutation in the variables of these OOB cases enables us to select the crucial features from high-dimensional microarray data. In this study, we analyze the effects of various random forest parameters on the selection procedure. ‘Variable drop fraction’ regulates the forest construction. The higher variable drop fraction value efficiently decreases the dimensionality of the microarray data. Forest built with 800 trees chooses fewer important features under any variable drop fraction value that reduces microarray data dimensionality.

Classifying data samples into their respective categories is a challenging task, especially when the dataset has more features and only a few samples. A robust model is essential for the accurate classification of data samples. The logistic sigmoid model is one of the simplest model for binary classification. Among the various optimization techniques of the sigmoid function, Adam optimization technique iteratively updates network weights based on training data. Traditional Adam optimizer fails to converge model within certain epochs when the initial values for parameters are situated at the gentle region of the error surface. The continuous movement of the convergence curve in the direction of history can overshoot the goal and oscillate back and forth incessantly before converging to the global minima. The traditional Adam optimizer with a higher learning rate collapses after several epochs for the high-dimensional dataset. The proposed Improved Adam (iAdam) technique is a combination of the look-ahead mechanism and adaptive learning rate for each parameter. It improves the momentum of traditional Adam by evaluating the gradient after applying the current velocity. iAdam also acts as the correction factor to the momentum of Adam. Further, it works efficiently for the high-dimensional dataset and converges considerably to the smallest error within the specified epochs even at higher learning rates. The proposed technique is compared with several traditional methods which demonstrates that iAdam is suitable for the classification of high-dimensional data and it also prevents the model from overfitting by effectively handling bias-variance trade-offs.

As web-based social network allows anyone to post the content without any restriction, the trustworthiness of the content creator plays an important role before using the content. An effiective way to find the trustworthiness is, by analyzing the web resources related to the content creator. Therefore the trustworthiness is assessed using the provenance based ontological model called W7 model. Since it is a real time data, the computed trust for each reviewer using the ontological model is uncertain and vague. An appropriate way to classify such data is using the fuzzy logic with gradual trust level. As the computed trust data are feature-based and non-symbolic, the classification ambiguity need to be reduced greatly. This is achieved with the fuzzy decision tree approach, which is a fusion of fuzzy sets with decision tree. The truth of the rule is crucial in trustworthy user classification, as highly truthful rules really increase the credibility of the user in their domain. Therefore, in the proposed model, degree of truth is used as a pruning criteria that classifies the users with minimum number of fuzzy evidence or knowledge. This paper proposes a semantic provenance based gradual trust model to classify the trustworthy reviewers in a book-based social networks using fuzzy decision tree approach. Performance analysis of the proposed model in the terms of classifier accuracy, precision, recall, the number of rules generated and its time complexity are discussed. The analysis shows that the proposed learning model outperforms other classification models. This method is also applied to other data sets and the performance of the classifier is assessed.

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

Single crystals of Fe(II) and Fe(III) (1 mol %) doped ammonium dihydrogen phosphate crystals were obtained by slow evaporation solution growth technique (SEST). Single crystal X-ray diffraction studies of doped samples shows slight structural changes. The obtained crystals was further characterized by FT-IR, SEM, UV-DRS. The band gap energies of the crystals were estimated by the application of Kubelka-Munk algorithm. Second harmonic generation (SHG) efficiency of Fe(II) and Fe(III) crystals let out that Fe(II) doping in ADP has better nonlinear optical (NLO) property than Fe(III) due to lattice stress.

Heat exchanger is one the most essential heat transfer equipment in thermal area. On that heat transfer and effectiveness enhancement by using nano particles is recently developing method. In this study enhancement of effectiveness focused by using nano particles mixed with cold fluid used in the heat exchanger. Hot fluid is water with 200 °C flow into Counter flow heat exchanger and the cold fluid is mixed with 5% of nano particles which is combination of Aluminium oxide and Magnesium oxides with equal proportion. The inlet temperature of the cold fluid is maintained at 30 °C throughout the experiments. The temperatures of the each place need to measure are obtained by the thermocouples connected on the particular portion of the setup. There are different mass flow rate of cold fluid passed in each successive experiments for the same mass flow rate and inlet temperature of the hot fluid. The consequent exit temperatures of hot and cold fluids were measured and considered for the mathematical calculations of the outcome of the experiments.

Heating ventilation and Air Conditioning (HVAC) systems consume a substantial volume of energy within corporate buildings, mainly due to lack of severe monitoring which results in compromising either energy efficiency or user comfort. We propose a simple HVAC control system that automates the HVAC operation in real time by considering energy management policies and user preferences. Our system is built on top of IoT (Internet of Things) framework, where appliances in a laboratory are automated with suitable sensors also thermal parameters are obtained from sensors and user feedback information are collected for real-time processing in our distributed cloud environment. We utilized Blynk Application Programming Interface (API) to monitor the real time data from sensors, to obtain user feedback periodically and to dynamically adjust the temperature settings based on energy management policies, user feedback and sensor values. The effectiveness of HVAC is evaluated mathematically. Our experiments indicate that we achieve total saving of 0.9 kWhr and also maintain user thermal comfort consistently.

This paper investigates the problem of finite-time stability for Takagi–Sugeno (T–S) fuzzy system with intermediate estimator. Based on intermediate estimator, a fuzzy fault-tolerant controller is designed for the considered fuzzy system to solve the tracking problem of time-varying fault. In particular, $$H_\infty $$ performance index is employed to deal the external noises. More incisively, the constraints in the form of Linear Matrix Inequalities (LMIs) for finite-time stability with $$H_\infty $$ performance are built by constructing suitable Lyapunov’s candidates. Finally, an illustration of truck–trailer model is given to exhibit the efficacy of proposed controller.

In the present investigation, the authors explored the depolymerization of waste poly(ethylene terephthalate) (PET) beverage bottles using glycolysis (transesterification) catalyzed by Al3+, Fe3+ and Zn2+ impregnated bentonite catalysts. Heterogeneous catalysts such as clay, zeolite, alumina etc. preferred over homogeneous catalysts, thus we focused on the development of solid acid catalysts based on economically viable clay catalysts. The desired Lewis acidic nature was introduced by wet impregnation method at variable metal ion to clay ratio such as 0.5, 1.0, 2.0, 3.0, 4.0 and 5.0 wt. %. The effect of metal-ion loading on the surface area, pore volume and crystalline nature of the bentonite has been evaluated by nitrogen adsorption and desorption studies and XRD. The PET wastes collected from post-consumer beverage bottles were depolymerized using ethylene glycol as a glycolyting agent afforded bis(2-hydroxy ethyl)terephthalate (BHET) in 85-90 % yield under optimized conditions. The yield of BHET was observed to be increasing with increase in Lewis acidic metal ions loading from 0.5 to 4.0 wt. % as well as increase in catalysts to PET ratio. Among the catalysts, Al3+ and Zn2+-bentonite catalysts showed higher yield of BHET up to 90 % at 4 and 5 wt. % loading of metal-ion.

Carbazole derivatives are important compounds from medicinal point of view because of their widespread biological significance. In the present work two compounds 7-(4-chlorophenyl)-5-methyl- 12-(phenylsulfonyl)-12H-naphtho[1,2-b]carbazole (I) and 7-ethyl-5-methyl-12-(phenylsulfonyl)-12Hnaphtho[ 1,2-b]carbazole (II) have been synthesized and characterized by XRD, Hirshfeld surface, energy framework and docking analysis. Single crystal X-ray diffraction analysis shows that the compound I crystallizes in monoclinic system with space group P21/n whereas compound II crystallizes in triclinic with space group P-1. In both compounds there are two intramolecular C-H···O hydrogen bonds, which generates two S (6) ring motifs. The crystal packing is stabilized through weak C-H···O and C-H···Cl interactions. The molecules also features C-H···π interactions. The intermolecular interactions of both compounds were analyzed using Hirshfeld surface analysis and two dimensional fingerprint plots, which was confirmed by the XRD data. Energy frameworks were used to calculate the intermolecular interaction energies and their distribution over the crystal structure. Molecular docking studies show that the compounds exhibits antitumor activity.