Alzheimer's disease is a well-known illness characterized by memory loss and cognitive decline. Since current treatments work best in the early stages, early detection is vital for effective management. The Magnetic Resonance Imaging (MRI) plays a key role in diagnosing Alzheimer's by providing detailed images of brain structures. Advances in deep learning have further improved MRI-based diagnosis through automated image analysis.EfficientNetB2 and MobileNetV3 are lightweight convolutional neural networks (CNNs) known for their high performance and speed, making them ideal for resource-limited and real-time applications.InceptionV3 is a widely used CNN, known for its strong performance in image classification tasks. By training these deep learning models on complete MRI datasets, they offer the potential to aid doctors in making accurate and timely diagnoses. This approach couldlead to better outcomes and improved quality of life for patients.The research aims to enhance early detection and treatment methods for Alzheimer's disease, contributing to advancements in deep learning models.

With the growth of the industrial age, the need for energy is continuously increasing. To provide efficient power distribution, instill minimal losses, ensure high quality, and dependable supply security, the smart grid idea has cropped up in conversations that allows for the generation and sale of power on a small, individual scale. However, the strategy enhances existing system issues, such as how are transactions among both producers and users carried out, validated, and documented. For a start, smart grids use smart contracts to conduct transactions, with the network serving as a transaction verifier. The advantages of smart grids over traditional power distribution systems rest in its innovative offerings to customers who until then, for many years, had few options for choosing between generated energy, most of which are based on fossil fuels. Not only was the equipment used to create energy highly costly, making it unsuitable for small-scale applications or personal usage of the users but also customers who generated their own electricity had it distributed by the power grid and only paid for what they used. However, technological advancements have altered how humans create and distribute power. Automation results in mass manufacturing, which results in less expensive power generators. Inventions enabled energy to be generated from solar power, tidal waves, geothermal power, and other non-conventional sources. This created the opportunity for an area, household, or a single person to produce their own electricity. Whenever a person created more energy than they had use for, they could easily sell the extra electricity to others, therefore acting as alternative energy sources. In terms of power supply, the smart grid provides greater assurance. However, the idea of automating an electric grid complicates the already complex electrical system. On a transactional level, how can we ensure that generators supply electricity after the user has paid. Because the network has so many producers and consumers, it causes issues about who can truly authenticate the transaction and how the process will operate. Traditional energy trading strategies focus on a centralized approach or third-party companies, but that might be vulnerable from single-point failure. As a result, a secure and reliable trading strategy must be installed in the system to ensure the data security of customers. In this regard, a new technology known as a blockchain, which is a chain of decentralized and distributed transaction ledgers that each user retains and maintains, can address the issue outlined above. Many of the issues that inhibit the Energy Internet’s growth can be solved by applying the blockchain’s technical benefits to it. The use of blockchain technology in the energy sector is only getting started, but it has a lot of room for growth in the future. Enhanced security, better data protection, data transparency and integrity, elimination of third-party authority and trust, pervasive solutions, and higher data usability are just a few of the advantages. Restricted adaptability and speed, off-chain service demands, expensive building and maintenance costs, and the need for more experimental studies are among the still thorny issues.

Arrhythmia can be considered as an issue with the heartbeat rate or pace of heartbeat. In this disease, the pulses might be excessively sluggish, excessively fast, excessively sporadic, or too soon. Fast arrhythmias will have more prominent than 100 beats each moment and it is referred to as tachycardias. Moderate arrhythmias will have more delayed than 60 beats every second and this condition is said to be bradycardias. Fibrillations are irregular cardiac beats. A problematic tightness occurs when a single heartbeat occurs earlier than expected. Thusly it is expected to characterize whether the patient is influenced by arrhythmia and this can be accomplished by utilizing PC helped procedures like Machine learning strategies or calculations. The proposed technique is to research if the patient is influenced by arrhythmias. First the interaction of dataset assortment is done and afterward preprocessing is completed to manage some of steady qualities and missing qualities. To order the ECG signal arrhythmia data set into ordinary or unusual by utilizing AI calculations like SVM, Adaboost and profound learning calculation like ANN. At that point the exactness execution is contrasted and the current calculations like VF15, closest neighbor and naivebayes.

Ever since the emergence of digital and modern healthcare, the globe has rushed to implement numerous technologies in this sector in order to improve health operations and patients' health, extend survival rate, and lower healthcare expenses. New methods, strategies, and devices have emerged as a result of advancements in technology throughout history. Such advancements have resulted in significant advances in a multitude of sectors, including industry, agribusiness, education, and now even in healthcare. The emergence of personalized health tracking devices and wearables connected to smartphone apps or inbuilt sensors may continue to observe individuals' health-related metrics, such as the electrocardiogram (ECG) signals, blood pressure, respiratory rate, and blood insulin level, reducing the hazard of data recording inaccuracies. Such sensors that collect and securely send information to the cloud, where it may be compared to past data to look for indicators of any sickness or alert the right medical experts. Reduced errors imply improved functionality, cost, productivity, and quality in medical services. The combination of technology and healthcare has entered in a smart context-aware IoT medical age.

Diabetes disease is caused because of an increase in the blood sugar level. The aim is to bring out a system that predicts patient's diabetes with good accuracy with the results of various techniques of machine learning. Further, the expansion of this research work gives the most effective algorithm for classification for identifying the chances of diabetes in an exceedingly person. The main idea of doing the project is to give a diabetes prediction model for the purpose of predicting the occurrence of diabetes. Further, the most effective algorithm is identified for classification of the chances of diabetes in an exceedingly men or women. Here an algorithm that can predict the diabetes with good accuracy is obatined. The developing model relies on categorization and classification methods like Naive Bayes, Support Vector Machine algorithm, and ANN. The main result of this work will be spotting most effective algorithm which is good at providing good and more accuracy when the classification of person is allotted. It's found that the neural network algorithm performs better in comparison to other algorithms for disease prediction. The project is meant using R Language.

Social media has become a vital source of communication worldwide from human-to-human, humanto-machine, machine-to-machine and machine-to-human. Different forms of these communications have a variety of quality parameters and they are used in various application scenarios. Earlier started as a text exchange medium now has support for all forms of digital media. Race in computing speed and communication bandwidth leverage the growth of the social media. Particularly in India the revolution started when Orkut entered the market in 2007. Even though there was constraint in latest hardware technology and network bandwidth in India, the huge market place it has attracted developers to tune their applications for the available technological resources here. Today there are quite a good number of social media competitors in India. Features offered to customers by these social media vary in spite of their growth. People have different choices to use with. This article analyzes the penetration performance of WhatsApp and Telegram in India. The focus is to create technology awareness among the users of these two competitors.

Encryption is the way toward encoding given information that can't be perceived by an unapproved individual. Securing a message or a picture which is partaken in a social stage be the difficult undertaking these days. This paper proposes an account procedure to steganography by means of reversible room before encryption surface mix. Reversible room before encryption surface mix measure explore supplementary unobtrusive side picture that arranges different facet picture with a similar and relative appearance with optional dimensions. This paper networks the Reversible room before encryption surface association cycle with steganography for covering secrete data. Rather than using contemporary cover photo for covering information our figure seal the source surface picture and introduces coded message via pattern of Reversible room before encryption surface association. This licenses us to isolate the secret messages and source surface from a stego designed surface. This philosophy proffers main three focal points. Most importantly, the positioning provide data embedding furthest extended that is contrast to dimensions of surface of the stego picture. Second, a steganalytic compute not apparently to conquer steganography technique. Third, changeable capacity gained from our arrangement gives value, which grants recovery surface of the source. Exploratory results are guaranteed to provide various consignment of introducing curtailment, produce ostensibly viable surface pictures, and also recuperate fount surface.

In recent days, the reliability and the accuracy of the system plays an important role in every industry starting from medical, mechanical, electrical, to everything. It is hard to achieve hundred percent accuracy and also to reduce all the possible errors happening. A technique called redundancy is used to increase the reliability in fields which uses combinational logic circuits for its working operation. The triple modular redundancy mechanism is famous for fault reduction and is also referred to as the fault tolerance mechanism. The construction of proper voter algorithm plays a vital role in TMR implementation. In fault-tolerant computing area, the most priority is given to the hardware fault tolerance systems. In this article, the model of triple modular redundancy is discussed and the improvements for voter circuit algorithms are proposed. The various aspects of Triple Modular Redundancy technique such as reliability improvement, usage in critical operations and field improvement are also analyzed in this paper. In addition to this, the comparison of TMR with various redundancy techniques such as Hamming code (SEC - DED), Standby sparing, NMR with spares, etc. is presented for the proper selection of redundancy mechanism for the particular application depending upon the user requirement.

Nowadays, Magnetic levitation systems are getting more importance due to its practical importance. The magnetic levitation system is basically a transportation system for guiding and propelling the vehicles. Its main application is in the field of railways. It eliminates the maintenance problems caused due to the friction as it does not contain any moving or joining parts. The other problems taken into account are endurance, lubrication, stiffness, damping factor, etc. The key component of the maglev system is the controller. The controller positions the steel ball at the required height by using the current through the coil. The dynamic performance of the maglev system is influenced by the controller design. This system is highly nonlinear and it is very difficult to design a high-performance controller. In this work, the controllers are designed for the maglev system using the neuro PID Controller and state variable feedback control using pole placement method. The desired performance indices taken are reduced overshoot and shorter settling time. From the responses obtained, neuro PID controller is found to have better output response than state feedback controller by pole placement method.

Effectiveness of structure teaching programme on effects of tobacco abuse among adult construction workers in Tertiary Hospitals Coimbatore.Objectives: To assess the effectiveness of structured teaching program on effects of tobacco abuse among adult construction workers.Methodology: The research methodology selected for this study is pre experimental design, one group pre-test post-test design. This study includes 100 construction workers and then the setting for the study conducted from PSG Hospital in Coimbatore, After Ethical clearance got from the committee IHEC (Institutional human ethical committee), PSG IMS&R. The level of knowledge of tobacco abuse and nicotine dependence was assessed. Data were analysed by inferential statistics and descriptive statistics.Result: The analysis revealed that the among 100 construction workers, 92% had adequate knowledge, 8% had moderate knowledge and no one had inadequate knowledge in assessment of tobacco abuse. And among them 30% have heavy nicotine dependence, 32% have moderate nicotine dependence, 38% have light nicotine dependence.Conclusion: The structure teaching programme has helped the construction workers to know about the effects, complication of tobacco and they gained knowledge about tobacco and nicotine abuse.

Mullite type Bi2Fe4O9 (BFO) and Nd doped Bi2Fe4O9 (BNFO) systems were developed using conventional solid state route under normal atmospheric pressure. The structural phase compositions were analyzed using x-ray diffraction (XRD). The analysis revealed BFO orthorhombic primary phase with traces of secondary phase and compressive lattice structural distortion, the same being evident from theoretically calculated (LSDA + U and PBE + U)correlated systems, where LSDA is Local spin density approximation, Hubbard U = 4.5eV and PBE is Perdew, Burke and Ernzerh of exchange correlation functional. Raman spectral analysis showed changes in the frequency and line width with respect to Nd doping, which correlates the lattice distortion observed in XRD analysis. Henceforth, the Bi2-xNd2xFe4O9 with x = 0, 0.01, 0.03, will be referred as BFO, BNFO1 and BNFO3. From the dielectric and optical studies, a significant increase in the dielectric constant and narrower band gap was observed in the BNFO samples, compared to BFO. The room temperature vibrating sample magnetometer (VSM) analysis of the BNFO samples showed weak ferromagnetic hysteresis behavior. Also, a significant enhancement of magnetic moment was observed with increased concentration of Nd in BNFO samples. This could be due to suppression of oxygen vacancies by the Nd dopant, that reduces the super-exchange interaction of Fe3+-O-Fe3+ and introduce Dzyaloshinskii-Moriya (D-M) interactions. The BNFO3 with control over its dielectric constant, optical band gap and magnetization by doping, makes it a viable material for magneto-electric and other multifunctional applications.

— In this paper, the reduced model of the Pressurized Water Nuclear Reactor (PWR) is derived based on the point kinetics equations and thermal equilibrium relations. The power level of the nuclear reactor is controlled by adjusting the insertion reactivity of the rod. Several controllers such as Genetic Algorithm based PID controller (GAPID), Fractional Order PID controller (FOPID) and Genetic Algorithm based Fractional Order PID Controller (GAFOPID) are used to control the power level of the PWR type of nuclear reactor. The simulation results depict that the Genetic Algorithm based Fractional Order PID Controller (GAFOPID) shows the satisfactory response than other control techniques.

Bismuth ferrite (Bi 2 Fe 4 O 9 ) thin films were grown on p-type Si (100) substrate by radio-frequency magnetron sputtering at 873 K. X-ray diffraction, field emission scanning electron microscopy and Raman spectroscopy studies revealed that the grown films have single-phase polycrystalline nature and are crystallized in orthorhombic structure. The grain size of the grown thin films was found to increase (56–130 nm) with sputtering power. Atomic force microscopy images clearly illustrated that the grown thin films have smooth surface. Energy-dispersive X-ray analysis revealed the presence of Bi, Fe and O elements with desired ratio and also the absence of impurities in the grown films. Analysis of ferroelectric hysteresis loops revealed that the remanent polarization and coercive field increase with the increase in sputtering power. Vicker’s hardness analysis showed that the hardness of films strongly depends on the grain size and film thickness, which are mainly determined by the sputtering power. The above observations revealed that Bi 2 Fe 4 O 9 thin film deposited at higher sputtering power has good crystallinity and shows better electrical properties.

Effect of RF power on structural and magnetic properties of lanthanum (La3+) doped Bi2Fe4O9 thin films grown on p-Si substrates by radio frequency (RF) magnetron sputtering has studied in this investigation. It is observed that the sputtering power affects the crystalline nature and magnetic properties of grown thin films. X-ray diffraction and Raman spectrum confirms that the Bi2Fe4O9 (BFO) thin films were crystallized well with orthorhombic structure. The BFO thin films which was prepared at sputtering power of 100 W have good crystallinity than those prepared at 40 W. The magnetic properties are investigated by vibrating sample magnetometer. The magnetic hysteresis perceptive loop shows that the anti-ferromagnetic behavior of the sample at room temperature. These results confirms that the crystallinity and magnetic properties of the BFO thin films were enhanced at the higher sputtering power (100 W).

Bi1-xCaxMnO3 (0 <= X <= 0.4) thin films are deposited on n-type Si (100) substrate at 800 degrees C by RF magnetron sputtering. X-ray diffraction pattern shows that the films are crystallized in monoclinic structure with C2 space group. The crystallite size and induced strain in the prepared films are measured by W-H plot. The cell parameters and texture coefficient of the films are calculated. The surface morphology of the films is examined by atomic force microscope. The study confirms the optimum level of calcium doping is 20 at. % in Bi site of BiMnO3 film, these findings pave the way for further research in the Ca modified BiMnO3 films towards device fabrication.

This paper presents the growth of bismuth ferrite ( Bi 2 Fe 4 O 9 ) thin film by radio frequency magnetron reactive sputtering on p- Si (100) substrate and the characterization of the grown thin film. The deposited thin film is characterized by X-ray diffraction (XRD), field emission scanning electron micrograph (FESEM), energy dispersive X-ray analysis (EDAX), dielectric measurements and vibrating sample magnetometer (VSM) analysis. The XRD study reveals the orthorhombic structure of the crystallites and the particle size is calculated as 45 nm. The FESEM result confirms that the film has smooth surface and uniform distribution of nanoclusters. The percentage of chemical compositions of the film is confirmed by EDAX measurement. The dielectric behavior of the film is examined in terms of the dielectric constant and the dielectric loss as a function of frequency. The magnetic behavior of the film is measured using VSM with the applied magnetic field of about 1 Tesla and the result shows the ferromagnetic behavior of the sample at room temperature.

In this paper, the reduced model of the Pressurized Water Nuclear Reactor (PWR) reactor is derived based on the point kinetics equations and thermal equilibrium relations. The power level of the nuclear reactor is controlled by adjusting the insertion reactivity of the rod. Several controllers such as Genetic Algorithm based PID controller (GAPID), Fuzzy Logic Controller (FLC), Genetic Algorithm based Fuzzy Logic Controller (GAFLC), Particle Swarm Optimization (PSO) based Fuzzy Logic Controller (PSOFLC) are used to control the power level of the PWR reactor. The simulation results show that the Genetic Algorithm based Fuzzy Logic Controller (GAFLC) shows the satisfactory response in both servo and regulatory level than any other control techniques.