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ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transaction with ATM is increasing day by day. ATM is very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's world, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including finger printing, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holder to verify the identity of unauthorized user through some dedicated artificial intelligent agents, for remote certification. However, it obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy. By using this real time dataset, the proposed system achieves the highest accuracy with 97.93%

This paper is discusses about the Knowledge, Present Status and Opinion of Open Access Resources among the higher educational institutions in Cuddalore Distrcit. The relevant data has collected from the faculty members who are working in higher educational institutions which consists of Engineering & Technology and Arts & Science from January 2022 to May 2022. Totally 1272 questionnaires were distributed which includes 432 from Engineering & Technology institutions and 840 from Arts & Science Colleges in Cuddalore District, Which indicates the questionnaires distributed who are in the role during the study period. And out of 1272, 1035 were received from faculty members after duly filled and responded. The dographic details wise analysis of the 1035 faculty members, 284(27.44%) of stated to ways known about the open access resources through ‘Library Professionals’ which includes 75(7.25%) of them from ‘Engineering and Technology institutions and 209(20.19%) of them from ‘Arts & Science’ institutions.

Total dissolved solids (TDS) due to both geogenic and anthropogenic contaminations, is one of the preliminary as well as essential parameters among others to describe water quality. It includes organic and inorganic components, heavy metals, salts, other dissolved substances, and others due to indiscriminate disposal of untreated domestic and industrial effluents, urban and/or agricultural runoff which exists in the form of micro or nano level in nature. Higher TDS levels in water severely impact health and the environment. For instance, it causes gastrointestinal, cardiovascular, genotoxic, respiratory, skin, and hepatic effects in humans. High TDS level (> 500 ppm) results in excessive scaling in household appliances (e.g., pipes, heaters, and boilers) thereby reducing their efficiency. Therefore, the removal of TDS from various water matrices is taking paramount importance to minimize its impact on health and the environment. Various TDS removal technologies based on the membrane, ion, and temperature gradient are employed to treat water. In this review paper, several TDS removal technologies for instance reverse osmosis, nanofiltration, distillation, ultrafiltration, forward osmosis, precipitation, desalination, ion exchange, electrochemical techniques, electrodialysis, electrolysis, electrocoagulation, and adsorption are discussed in detail.

Renal calculi are a typical urological system problem. It involves the urinary tract's crystallization process. An obstruction in the ureter can produce renal or ureteric colic, an acute and debilitating pain. It typically happens in the ureter's more constrained regions. It may also be accompanied by other symptoms like hydronephrosis, haematuria, acute pain, nausea, and vomiting. Case Summary: A case of multiple renal calculi in both kidneys with a 19mm calculus lodged at the PU (Ureteropelvic) Junction of the right kidney. The patient presented with severe cutting pain in the lumbar region in the abdomen with radiating pain from the right loin to left. The patient had pain before and after urination. After case-taking, based on the totality of symptoms and repertorization, Homoeopathic medicine, Lycopodium clavatum 200C was prescribed. That proved that Homoeopathic medicines are effective for the expulsion of renal stones.

In recent times, it has been witnessed that wireless systems based on IoT-based have developed rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has proliferated into several different applications in real-time. The IoT and WSNs now have various critical and non-critical applications impacting nearly every area of our everyday life. WSN nodes are usually small and battery-driven machines. Thus, the energy effective data aggregation techniques that increase the lifespan of the network are highly significant. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. This paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data.

The application of electrode heating is proposed in electrochemical micromachining (EMM). In the EMM process, the temperature of the electrode, voltage, duty cycle, and electrolyte concentration are considered process parameters. Taguchi L18 mixed-level orthogonal array (OA) design was adopted for designing the experiments and this study highlights the effect of temperature on responses such as radial overcut (ROC), material removal rate (MRR), and conicity factor (CF). In addition, multicriterion design making (MCDM) with VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje, i.e., multi-criteria optimization and compromise solution) technique is used for finding the best alternatives based on the distinct weight assessing methods such as equal weights method (EWM), analytic hierarchy process (AHP), and entropy-based weights method (EBWM). Furthermore, a confirmation test is also conducted to find the best optimal parameters and their levels among the EWM-VIKOR, AHP-VIKOR, and EBWM-VIKOR. The results revealed that AHP-VIKOR provides a maximum improvement of 0.945 among the three methods.

In this study, we made an attempt to attenuate the dexamethasone induced hypertension through Biochanin-A (BCA) in experimental rats. Hypertension was induced in male albino Wistar rats by subcutaneous administration of dexamethasone (10μg/kg body weight). The rats were orally treated with BCA (10mg/kg body weight) once daily for 45 days and Nicorandil-treated group (6mg/kg body weight) included for comparison. We evaluated the changes in mean arterial pressure, heart rate, blood pressure, vascular function, oxidative stress markers, and gene expression of histone deacetylases (HDAC)-1, HDAC-2, and HDAC-8. Administration of BCA or Nicorandil showed noteworthy improvement in vascular function in experimental rats. Moreover, aortic eNOS expression was down regulated, and NADPH oxidase subunit p47phox was up regulated in hypertensive rats. The antihypertensive effects of BCA were connected with concomitant downregulation of p47phox expression and upregulation of eNOS expression. Dexamethasone exposure led to increased mRNA expression of HDACs expression in the kidneys and these were restored after BCA administration. In conclusion, our results are, therefore, BCA reduces hypertension in experimental rats and suggests that BCA might be used against the hypertension.

Electrochemical machining is a nonconventional machining process in which the metal removal is achieved by the electricity and chemical solution known as an electrolyte. It is the reverse electrolysis process where the application of electricity facilitates the current travel in between anode and cathode. The mechanism of the ion movement is similar to the electrolysis process. Electrochemical machining (ECM) is a type of advanced machining process which employs electricity to perform the machining process on the workpiece. It is also known as a reverse electroplating process where metal removal is achieved instead of metal deposition on the metal surface. There are various parameters that affect the metal removal process in the ECM process, such as electrolyte, power supply, workpiece material, and tool material. The electrolyte is one of the key factors impacting the machining rate, surface finish, and reliability of the produced parts. In this project, a brief study is carried out regarding the electrochemical process and the electrolytes where the properties, functions, merits, and demerits are evaluated. The impact of the various electrolytes and their suitability for machining of various metals is also discussed. The findings of the effect produced by using the mixture of the electrolyte in the electrochemical machining process are discussed in this project. The effects of the complexing agents on the electrolyte and the electrochemical process as a whole are also reviewed.

Purpose This paper aims to determine the regression rate using wax fuels for three different grain configurations and find a suitable grain port design for hybrid rocket application. Design/methodology/approach The design methodology of this work includes different grain port designs and subsequent selection of solid fuels for a suitable hybrid rocket application. A square, a cylindrical and a five-point star grained were designed and prepared using paraffin and beeswax fuels. They were tested in a laboratory-scale rocket with gaseous oxygen to study the effectiveness of solid fuels on these grain structures. The regression rate by static fire testing of these wax fuels was analyzed. Findings Beeswax performance is better than that of paraffin wax fuel for all three designs, and the five-slotted star fuel port grain attained the best performance. Beeswax fuel attained an average regression rate ≈of 1.35 mm/s as a function of oxidizer mass flux Gox ≈ 111.8 kg/m2 s and for paraffin wax 1.199 mm/s at Gox ≈ 121 kg/m2 s with gaseous oxygen. The local regression rates of fuels increased in the range of 0.93–1.194 mm/s at oxidizer mass flux range of 98–131 kg/m2 s for cylindrical grain, 0.99–1.21 mm/s at oxidizer mass flux range of 96–129 kg/m2s for square grain and 1.12–1.35 mm/s at oxidizer mass flux range of 91–126 kg/m2 s for a star grain. A complete set of the regression rate formulas is obtained for all three-grain designs as a function of oxidizer flux rate. Research limitations/implications The experiment has been performed for a lower chamber pressure up to 10 bar. Originality/value Different grain configurations were designed according to the required dimension of the combustion chamber, injector and exhaust nozzle of the design of a lab-scale hybrid rocket, and input parameters were selected and analyzed.

In this research contribution, we have constructed a subclass of analytic bi-univalent functions using Horadam polynomials. Bounds for certain coefficients and Fekete- Szegö inequalities have been estimated.

In Tamil Nadu, India, the old wind turbines installed in 1988 have to be repowered and replaced by modern/advanced technological wind turbines. A solar power plant can be set up using the vast area between the wind turbines on the farm. The economic evaluation must be carried out to determine whether this hybrid project is economically beneficial. This article aims to examine the financial viability of repowering old wind turbines with solar power plants’ inclusion. According to the initial investment costs specified within fixed areas and the distance between turbines, the issue is articulated as optimizing the net present value (NPV) of the project. In this study, the economic investigation on the wind farm located in Kayathar was explored by replacing 30 numbers of 200 kW old wind turbines with modern wind turbines of 2000–3000 kW and installing the solar power plant between wind turbines, excluding the shadow region of wind turbines. The HOMER Pro software and Excel spreadsheet were used to analyze the optimal allocation, economic, electricity production, and sensitivity analyses of the effective hybrid systems. The value of electricity prices was measured using the levelized cost of energy and NPV methods. Additionally, a sensitivity analysis of the chosen parameters for the internal rate of return and debt service coverage ratio was estimated for wind and solar hybridization. The economic analysis suggests satisfactory profitability for the project, even without depending on subsidies. Furthermore, sensitivity analysis helps one see the consequences of the risk on the life of the wind farm and its impact on the project’s productivity and performance.

Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms.