We plan to create a Battery & Weather Monitoring System using Node MCU (ESP 8266) and Thing speak Cloud. This system will allow us to monitor the battery voltage and percentage and humidity and temperature of the battery and remotely from anywhere in the world. It is particularly useful for keeping track of the battery charging or discharging status.

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These days, there is an increase in electricity use and insufficient generation. Among the most efficient renewable energy sources is solar energy. To precisely track the sun's position, the system is calibrated & a performance study of the dual-axis solar panel is shown.

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A vehicle location tracking system is a technology that allows for the real-time monitoring and tracking of vehicles. It typically involves the use of GPS technology to pinpoint the exact location of a vehicle at any given time. You can optimize driver routes, save petrol, reduce theft and control the vehicle functions.

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Chalcone, an important compound in the process of producing flavonoids, has been proven to have many biological and pharmacological characteristics, such as anti-inflammatory, antioxidant, and anti-cancer activities. In this study, the chalcones were chemically combined with either palmitic or stearic acid to produce a distinct set of chalcones fatty acid esters 5b-e and 6b-e. Additionally, two other classes of compounds have been synthesized through electrophilic and Michael addition reactions. These compounds include 2-amino-6-(substituted-phenyl)-4-substitutedphenyl-nicotinonitrile erivatives 9a, c, and e, as well as 2,3-disubstituted chalcones 7b-d and 8b(b')-d. The reactions were carried out using the corresponding chalcones. The structures of all compounds are confirmed through the utilization of diverse spectroscopic techniques, including mass spectra, 1H and 13C NMR, and infrared spectroscopy. 1,1-biphenyl-2-picrylhydrazyl was employed as a reagent to scavenge free radicals and assess the antioxidant capabilities of all synthesized compounds. The findings indicated that compound 5e exhibited higher antioxidant activity (68.58% at C = 2 μg/ml) compared to ascorbic acid, a commonly employed antioxidant. This discovery was unexpected. Conclusion: Chalcone fatty acid esters exhibited favorable activity, with certain members demonstrating superior antioxidant activity compared to ascorbic acid. In addition, the impact and contribution of different functional groups on the antioxidant activity of the produced chalcone derivatives are investigated and explained based on their electrical and structural influence

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Ten derivatives of quinolin-8-yl[(5-aryl-1,3,4-oxadiazol-2-yl)sulfanyl] acetate were synthesized using a range of methods. The structures of the generated compounds were determined using IR, 1H NMR, and mass spectroscopy examinations. The antibacterial efficacy of the chemicals generated was evaluated using the bacterial strains Staphylococcus aureus, Bacillus subtilis, Pseudomonas aeruginosa, and Escherichia coli. The study's findings suggest that a number of the chemicals exhibit promising antibacterial properties

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One leading cause of traffic accidents is drivers who are either sleepy or too distracted to pay attention. It is possible to identify sleepiness and exhaustion by monitoring physiological signals while driving; this information is crucial for alerting the driver to avoid accidents. One way to think of sleepiness is as a transition between the "alert" and "drift-off" stages, when one's capacity to actively reduce their inspection and examination abilities occurs. Extracting as much sleeprelated data as possible from a database of
electroencephalogram (EEG) recordings is the primary goal of this research. This thesis develops four feature extraction approaches to detect driver drowsiness/fatigue states by extracting the most important information. The relevant brain physiological signals are analyzed to do this. In order to mitigate its consequences, the thesis delves into strategies that have been created to classify the various sleepy stages and notify the user at certain moments. There are two other types of methodology: supervised and unsupervised. Supervised methods include WPTBNN, FRFTNN, and FRFTABCS, while unsupervised methods include STFTIPSF and optimization with optimized particle swarms and fuzzy classifiers. An EEG signal downloaded from physionet.org serves as the data set for the study. The procurement of the database is the starting point for each of the described approaches. Next, the obtained signal is broken down into its component parts using WPT, FRFT, and STFT. From there, the coefficients or frequency band characteristics are created, and optimization is performed to choose the most suitable ones. Next, NN, Sparse, and Fuzzy classifiers are used to further classify these optimized best features. Using a number of performance indicators obtained from ROC graphs specific to each methodology, we further examine how well the approaches we developed performed. Results from the ROC graphs are used to do the performance analysis, which takes into account the following metrics: sensitivity, specificity, accuracy, FPR, PPV, NPV, FDR, and MCC. To achieve high-accuracy classification that would precisely discriminate between alert and departure from alert states, an efficient feature extraction method based on Wavelet Packet Transforms and Bootstrap Optimization and Neural Network Classifier (WPTBNN) is suggested. With the help of a Neural Network (NN) classifier, this suggested approach optimizes a Wavelet Packet Transform (WPT) program, retrieves frequency band information from a provided database, and then sorts the different states of awareness. The features are optimized using Bootstrap approach after frequency band feature creation. Then, they are sent to neural
network classification using the Perceptron Learning algorithm. Additionally, the DWTBKNN (Discrete Wavelet Transform + Bootstrap optimization + k-Nearest Neighbor classification) approach has been used to validate the findings

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Facial expressions are the primary, immediate, and instinctive means by which humans communicate their ideas and emotions. Automatic facial expression identification is a captivating and challenging field that has a significant impact on the applications of human-computer interaction due to its intricate nature and diverse range of expressions. Academics are intrigued by the topic due to its extensive array of applications, including cognitive science, health care, and video conferencing, among others. In this work, we have devised three distinct methodologies for fer and compared them with existing models documented in the literature. The Deep Layered Representation (DLR) is a novel convolutional neural network architecture designed specifically for facial emotion recognition. Our technique, which utilizes a multilayer deep neural network, has been applied to the FER2013 dataset from Kaggle. The data were analyzed using five generalized activation functions, namely Elu, ReLu, Softplus, Sigmoid, and Selu, in the final dense layer. By using the same dataset, we have conducted a comparison between our top two models based on activation functions and models based on GoogLeNet and VGG16 + SVM. The results have shown that our models have achieved greater accuracy. As an alternative method, we propose using a deep learning framework that employs transfer learning to comprehend facial expressions. This approach adds additional layers to the existing VGG16 model, which has been updated during training. The recommended model is verified using the benchmark datasets JAFFE and CK+. Many academics have used the well recognized facial expression datasets JAFFE (Japanese Female Face Expression) and Extended Cohn-Kanade (CK+) in their studies. The proposed model has been shown to outperform existing techniques, achieving a 94.8% accuracy rate on the CK+ dataset and a 93.7% accuracy rate on the JAFFE dataset. We have implemented the recommended approach on Google Colab-GPU. Currently, automated and unmanned restaurants are more prevalent in industrialized countries because to a shortage of human resources to gather feedback from customers on the food's quality and service. In order to streamline this procedure, the author has introduced the notion of a "Deep Learning Facial Expression Recognition Based Scoring System For Restaurants." In this system, customers evaluate their food and submit a photograph, and the application assesses the customer's degree of contentment by analyzing their facial expressions. We are using the CNN (Convolution Neural Networks) machine learning methodology to extract facial expressions from photographs. This system has the capability to recognize three specific facial emotions from a single image, which are neutral, unsatisfied, and delighted

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Extensive research is now underway in the fieldof traffic monitoring systems, particularly focusing on the automated detection of traffic accidents. Surveillance cameras that are connected to traffic control systems are being installed at a growing number of urban intersections. Computer vision approaches have the potential to be very valuable tools for automatically detecting accidents.

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With the exponential increase in data transmission across computer networks in today's world, identifying and preventing dangerous network use has become a paramount issue for network managers and users alike. The high volume of incoming data inundating the target server in the network,originating from several sources and resulting on server crashes or severe slowdowns, poses a significant challenge in distinguishing between malicious traffic from attackers and legal traffic from users.

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This paper proposes four optimization algorithms:BAT algorithm, Firefly algorithm (FFA), Gases Brownianmovement optimization (GBMO), Wind driven optimization (WDO) for tuning image parameters a, b, c and k of image. For algorithms brief description, main equations for solution are given. Three objective functions are formulated and performance of images is tested with three objective functions.The simulation results of algorithms are compared by performance parameters such as mean square error, root mean square error, peak signal to noise ratio and entropy.

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Healthiness is the base for every human being. It is directly or indirectly influencing the mental ability of the person. It gives them the confidence to each action of the human. Sound health is necessary to do all our day to day works with the fullest hope. Nowadays all people are having more health- conscious than in the past years. Because of these reasons, there are different types of health check- ups, monitoring clinics are evolved, and they do a lot of monitoring processes like daily, monthly, and master check-ups. To provide multiple services, options, and facilities to their clients the technologies play a vital role in the current era. The rapid development of information technology influences every person's life and health consciousness. These technologies are helping to monitor the status of a person and providenecessary tips then and there. Different methods of check-ups and monitoring process are available to get the information about a person. There are several IoT enabled sensors available to sense the patient complete details about a particular person's behavior, human anatomy, and physiology. This will lead the Big data. The Data gained over the sensors are uploaded to the internet, and connected to the cloud server. The affected person records could be saved in the web server and physicians can get right of entry to the data anywhere in the world. Anyun expected variation in the data of the patient who is using the healthcare system, inevitably the data of the patient will be uploaded to the concerned doctor with immediate notification. This type of health care system will be most useful in rural and remote areas. In this chapter, discuss the Machine learning techniques which are important to the build analysis models. Then how this model is integrated with IoT Technology and provide accurate data of individual person and also discuss the Cardiovascular problems based on real-time input data.

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The public sector of today desperately needs to be efficient, effective, and able to adapt quickly to social and economic developments. Artificial intelligence (AI), one of the keystones of digital technology that has the potential to profoundly alter all spheres of society, including the public sector, its leaders, and public workers, is supporting this shift with great technical advancement. It may also be useful in assuming a new function and giving the public sector and government activity based on contemporary technology a new legitimacy. Since there isn't a clear vision, to the best of our knowledge, there is a theory that supports the viability of using artificial intelligence in government. With their businesses becoming totally digital, public-sector executives must have a thorough grasp of the extent and implications of AI-based applications. This research attempts to provide some insight into this requirement. Prior study examined the use of artificial intelligence, often in a somewhat specialized manner, in the corporate sector. This created a research void that neglected to meet the unique needs of the public sector and government operations. In order to provide an integrated overview of the applications of artificial intelligence in the government sector—which takes into account the significant role that the government sector plays in the lives of citizens and the other vital sectors—the methodology of this study is based on gathering, analysing, and linking pertinent ideas from published scientific research. This study looked at the unique ways that artificial intelligence is applied in the public sector, the part that governments play in making sure that AI strategies are successfully implemented in their departments, and the vast opportunities and capabilities that AI offers to the public sector. The study also concentrated on the most significant problems and obstacles that artificial intelligence encounters in government work, the critical role that collaboration between the public and private sectors plays in the application's success, and the significant contributions that government officials can make to the growth and advancement of the artificial intelligence community.

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This paper explores various traditional Indian housing typologies—Pol houses of Gujarat, Wadas of Maharashtra, Chettinad houses of Tamil Nadu, and Havelis of Rajasthan. These housing forms have been instrumental in fostering social interaction within communities due to their unique architectural and cultural elements. By examining the historical, cultural, and contemporary developments of these housing types, this research aims to understand their role in promoting social cohesion and how they can inform modern housing design.

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