Volume 12 | Issue 1 |

 Abstract

Review on PIXE: Accelerator Based Analytical Technique

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PIXE, Accelerator, Nuclear Analytical Techniques

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The investigation of peasant's history in India overall and Andhra specifically has not gotten sufficient consideration from the professional scholars. It's kind of research work is a new peculiarity. A large number of the early scholars of history focused to a greater degree toward the ordered picturization of the rulers and privileged in light of accessible inscriptional and historical sources. There are anyway a lot of major obstacles to concentrate peasant history; the greater parts of the accounts are connected with just the main areas of the society, for example, the ruling class.

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Peasants, Inscriptions, Class etc.

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The human face serves as a fundamental and unique identifier, especially in the context of criminal detection and law enforcement. The increasing challenges posed by highly populous urban environments demand automated solutions for efficient and timely identification of individuals. This research introduces an innovative real-time criminal identification system that integrates deep learning, specifically Convolutional Neural Networks (CNN), and Haar Cascade classifier for face detection and recognition. The system utilizes live camera feeds in urban environments, enhancing law enforcement capabilities by combining facial recognition with historical criminal activity data. The proposed approach focuses on extracting detailed facial features through CNN, ensuring robust detection in challenging scenarios. The integration of Haar Cascade enables high-precision real-time face detection. our research contributes to the advancement of criminal identification systems by introducing a real-time approach that harnesses the power of deep learning and live camera feeds. The proposed system holds significant potential for enhancing law enforcement capabilities, enabling proactive identification, and contributing to the overall safety and security of urban environments. As a forward-looking solution, our research not only addresses current challenges but also anticipates future needs in the ongoing evolution of urban security. By incorporating real-time data analytics, our system aids authorities in making informed decisions, reinforcing its role as a proactive and intelligence-driven asset for ensuring public safety.

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Real-time criminal identification, Deep learning, Convolutional Neural Networks (CNN), Haar Cascade classifier, Facial recognition

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ccess to quality health care in rural and underserved areas is often limited, leading to delayed diagnosis and poorer health outcomes. This paper explores the potential of artificial intelligence (AI) to address these healthcare disparities. By analyzing existing literature and research, this article examines how AI can be used to improve the diagnosis of acute diseases in villages and small towns. The article covers data-driven solutions, machine learning and deep learning applications, AI-capable organizations, ethical considerations, and more. The results highlight the transformative potential of AI to bring accurate and accessible diagnosis to underserved areas.

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Challenges, opportunities, AI implementation, rural healthcare, healthcare disparities.

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Emotion recognition from electroencephalography (EEG) signals has become a pivotal aspect of affective computing. This research proposes the concatenation of two novel deep neural network architectures to advance the state-of-the-art in EEG-based emotion classification. The first model termed Hybrid STANN with Graph-Smooth Signals, employs a unique combination of spatiotemporal encoding and recurrent attention network blocks. Graph signal processing tools are applied as a preprocessing step for spatial graph smoothing, enhancing the interpretability of physiological representations. The model outperforms existing methods on the DEAP dataset for emotion classification. Additionally, its robustness is demonstrated through successful transfer learning from DEAP to DREAMER and the Emotional English Word (EEWD) datasets, showcasing its effectiveness across diverse EEG-based emotion classification tasks. The second model, named 3DCANN: Spatio-Temporal Convolution Attention Neural Network, addresses the dynamic nature of EEG signals in emotional states. The 3DCANN model features a spatiotemporal feature extraction module and an EEG channel attention weight learning module. By effectively capturing the dynamic relationships and internal spatial relations among multi-channel EEG signals, the model surpasses state-of-the-art performance on the (SEED) Dataset. The integration of dual attention learning and SoftMax classification enhances the model's ability to discern intricate patterns in EEG signals, resulting in superior emotion recognition accuracy. Both proposed models contribute to EEG-based emotion recognition by introducing innovative architectural elements and demonstrating their efficacy through comprehensive evaluations of diverse datasets. This research opens avenues for further exploration in physiological data-driven affective computing applications.

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Emotion Recognition, Graph Filtering, Spatio-Temporal Encoding, 3D Convolution Attention Neural Network, Dual Attention Learning, Transfer Learning.

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Stress is a natural reaction to challenges or changes in one's life. While it can provide a short-term burst of enthusiasm or energy, chronic stress may lead to severe health issues. Women are more likely to report health problems than men, such as headaches and gastric upset. Stress shows up differently for everyone, with common symptoms including pain, acne, headaches, upset stomach, insomnia, anhedonia (loss of interest), binge eating, anorexia, and reduced sexual drive. Stress is caused by different situations for different people. What is stressful for one person, might be easily managed by another. There are many sources of stress -- financial worries, job security, issues at the workplace, relationship issues, family conflict, and traumatic events like the loss of a loved one, severe illnesses, etc. The aim of this article is to find out the causes of stress of women and its remedies.

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Keywords : Stress, Women, Stress management

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AgriculturePlaysAPivotalRoleInIndiasEconomyFacingChallengesSuchAsIncreasedFoodDemandEscalatingLaborCostsUnfavorableEnvironmentalConditionsAndDiminishingAgriculturalLandToAddressTheseIssuesTheConceptOfIndoorFarmingParticularlyHydroponicFarmingHasGainedProminenceHydroponicsInvolvesUsingAWaterSolventToDissolveMineralNutrientSolutionsEnablingPlantsToAbsorbNutrientsMoreEfficientlyThanTraditionalSoilBasedMethodsInThisContextOurProposedSolutionLeveragesInternetOfThingsIoTTecHniquesToEnhanceTheEfficiencyOfIndoorFarmingThroughAClosedLoopSystemTheSystemAimsToPreciselyRegulateNutrientLevelsAndPhForOptimalPlantGrowthKeyComponentsIncludeAPhSensorATotalDissolvedSolidsTdsSensorAndSensorsForMonitoringTemperatureAndHumidityTheAnalogPhSensorAnalogTdsSensorAndDHT11SensorCollectRealTimeDataOnPhLevelsNutrientConcentrationTemperatureAndHumidityRespectivelyTheCollectedDataIsTransmittedToAMicrocontrollerWhichEmploysSmartDecisionMakingAlgorithmsBasedOnIoTPrinciplesTheMicrocontrollerAssessesTheReceivedDataAndDynamicallyDeterminesWhetherAdjustmentsAreNeededForInstanceItDecidesWhetherToReleaseSpecificNutrientsOrPhBufferSolutionsTheReleaseMechanismIsFacilitatedThroughAPeristalticPumpControlledByARelayByIntegratingIoTTecHniquesIntoVerticalFarmingOurSystemOffersASophisticatedAndAutomatedApproachToOptimizeCropCultivationThisNotOnlyAddressesTheChallengesPosedByTraditionalFarmingMethodsButAlsoEnsuresResourceEfficientAndSustainableAgriculturalPracticesInTheFaceOfAGrowingPopulationAndChangingEnvironmentalConditions

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Keywords for a Vertical Farming using IoT Project: 1. Vertical Farming 2. IoT (Internet of Things) 3. Indoor Farming 4. Hydroponic Farming 5. Closed-Loop System 6. Nutrient Regulation 7. pH Sensor 8. TDS (Total Dissolved Solids) Sensor 9. Temperature Monitoring 10. Humidity Monitoring 11. Smart Decision-Making Algorithms 12. Microcontroller 13. Real-time Data 14. Peristaltic Pump 15. Relay Control 16. Resource Efficiency 17. Sustainable Agriculture 18. Crop Cultivation Optimization 19. Environm

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Stable Diffusion uses a text-to-image machine learning model to generate images matching the prompt specification. This paper presents the experimental study of using different qualifiers with four different types of text prompts as a method of creating variations in the generated images while preserving the semantics. It was found that all qualifiers helped in creating some variation in the images. However, depending on the type of variation created in the images, this paper presents an insight into enhancement or insertion of relevant or irrelevant features in the generated images based on one- or two-word qualifiers used.

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Stable Diffusion, text-to-image, Generative AI, Neural Networks

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Coronary sickness is a risky contamination that is spreading swiftly. Many humans suffer from it, and it's far presently the leading purpose of death worldwide. This ailment influences the coronary heart and other body components, so it calls for an green analysis to assist the scientific community in remedy. Early detection of this sickness can shop many lives via proper remedy. however, traditional diagnostic techniques including blood tests, electrocardiograms, cardiovascular computed tomography scans, magnetic resonance imaging of the coronary heart, and many others. are time-ingesting and invasive. on this review paper, diverse coronary ailment detection methods proposed in the past few years had been studied. The paper describes the techniques used by researchers and the accuracy claimed with the aid of them. A commonplace dataset became used to evaluate the claimed accuracy, and a assessment table of different strategies turned into provided inside the effects dialogue phase. The paper offers the strategies which have an ok stage of accuracy.

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coronary heart disorder, cardiovascular sickness, AdaBoost algorithm, important element evaluation, linear discriminant analysis

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The primary objective of our project is to develop a comprehensive health monitoring system capable of detecting the presence of critical medical conditions such as Heart Disease, Diabetes, and Parkinson's Disease. Users can input relevant health parameters, and the system employs machine learning algorithms to analyze the data, providing real-time feedback on potential health risks. This project utilizes a userfriendly interface that allows individuals to input their health information easily. The system then processes this data, leveraging machine learning and statistical models to assess the likelihood of heart disease, diabetes, and Parkinson's disease. The results are promptly communicated to the user, enabling them to take proactive measures for their well-being. The machine learning algorithms taken into consideration for detecting Heart Disease is Logistic Regression, Diabetes is Random Forest and Parkinson's Disease is Support Vector Machine. Once the data is taken from the users for the selected disease detector, the values will be used by the classifiers to detect if the individual has the particular disease or not. This system is implemented using Python programming and Streamlit library for integrating the models to front end.

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Chronic Diseases, Diabetes, Health Monitoring System, Heart Disease, Logistic Regression, Machine Learning Algorithm, Parkinson's Disease, Python programming, Random Forest, Streamlit, Support Vector Machine.

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