The inability to communicate effectively because of communication barriers severely restricts deaf and mute individuals from getting in touch with those who do not know sign language. A new web-based system has been developed to provide an effortless communication solution between users who are deaf, mute and Sign language users. The system implements Django as its backend framework collected with text- to- Sign language conversion and speech-to-text capabilities towards establish successful communication. Through the application users have the option to provide text or audio content that gets translated into American Sign language (ASL) animations through hand gesture visualization from MediaPipe. The system enables automatic speech recognition through the Speech Recognition library and functions as an ASR tool to convert spoken words into text. The Google Translator API enables native language translation of this recorded text which expands communication possibilities to different user groups. From datasets obtained through Kaggle the training of a Convolutional Neural Network model reaches 99% accuracy for sign recognition leading to accurate communication. The application creates a manageable interface which enables people with speech and hearing difficulties to get real-time Sign language animations as visual outputs. The novel system surpasses typical assistive communication solutions because it cuts out the requirement for human Sign language interpreters to provide inclusive communication channels. This proposed web application will increase accessible interactions thus promoting more social integration opportunities for both deaf and mute users. The system will be improved through language expansion of Sign language interpretation and a mobile app adaptation for maximized accessibility. The system incorporates components related to Communication accessibility, Sign language recognition, speech-to-text, ASL animation and employs CNN models in conjunction with Django framework.

The current paper concentrates on the data of food safety sensors on four key environmental parameters, including the temperature, humidity, microbial load, and transport time. The parameters were observed with histograms of 1000 samples, thus providing the information about the refrigeration controls, risks of microbial contamination and hazards in transit. The results potentially will be used to design reinforcement learning (RL) tools to plan dynamic inspection and routing to enhance food safety management.

Smart education empowered by AI is transforming the conventional learning environment by utilizing advanced technologies to deliver customized, engaging, and interactive educational experiences. With the use of AI’s -driven tools, students can access personalized content tailored to their unique progress, strengths, and areas for improvement. AI-generated video resources, including adaptive tutorials, interactive lessons, and real-time assessments, boost student engagement and retention of knowledge, creating a more effective and accessible learning experience. AI also helps teachers by automating administrative tasks and providing insightful data on student performance, freeing them up to focus more on efficient teaching strategies. The incorporation of AI-created educational videos promotes an inclusive and immersive learning atmosphere, accommodating a variety of learning preferences. This strategy bridges the divide between traditional and digital education, granting students more autonomy over their learning paths. By integrating AI into educational videos, institutions can better allocate resources, implement timely learning interventions, and ultimately foster a more equitable educational experience for everyone. The synergy of AI with smart education systems heralds a future where technology and teaching methodologies coexist seamlessly to unlock each student's full potential.

Chemical processes to sustainable and eco- friendly techniques. Of these, green synthesis, also known as biosynthesis has been found to be a viable approach that exploits biological systems, and their biomolecular constituents, to create nanoparticles. It is not only the most environmentally friendly, but also presents unique operational capabilities such as cost-effectiveness, energy efficiency and just one-pot reactions at ambient temperatures. Green synthesis offers a safer and more eco-friendly solution by not requiring the utilization of dangerous reagents and the special conditions of the conventional processes. This review will give a critical summary of the principles of the green synthesis, and more specifically its mechanistic nature in biological and chemical pathways. They provide critical comparisons with the traditional methods and give an assessment of the sophisticated characterization tools that are needed to authenticate the structural, morphological, and functional characteristics of synthesized nanomaterials. Furthermore, some uses of green-synthesized nanoparticles are examined in the biomedicine, environmental cleanup, farming, and technologies in the energy sector. Existing issues such as scalability, reproducibility and mechanistic ambiguities are critically examined and possible ways of overcoming these challenges are presented. This body of evidence supports the case of green synthesis as not only an environmentally friendly alternative, but also as an extremely versatile and technologically better platform that can produce functionally improved nanomaterials to be used in industrial and biomedical innovations in the future. This paper discusses the synthesis of copper oxide nanoparticles (CuO NPs) with Carica papaya leaf extract as the reducing and stabilizing agents. Using the leaves in the precipitation technique, rapid production of CuO NPs was easily biosynthesized and pollution-free without any environmental harmful effects. X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM) were used to determine the properties of the nanoparticles. The size of the particles measured using the Scherer equation showed that they were between 50- 60 nm in samples prepared through chemical precipitation. The manufactured CuO NPs had the right characteristics to be used in biomedical uses, which points to the possibility of green synthesis offering sustainable nanotechnology.

The rapid rise in pollution levels and green house gas emissions has accelerated an adoption of Electrical Vehicles (EVs), which are expected to play the pivotal role in a future energy landscape. As EVs integrate with electrical grids, their impact on voltage profiles and grid load distribution becomes increasingly significant. This study explores an integration of renewable energy sources, EVs into the microgrid and with the emphasis on energy production and consumption optimization. In this analysis, the diesel generator is a microgrid, Photovoltaic (PV) array coupled with the wind farm and the Vehicle-to-Grid system near a load. A paper introduces the model for a PV array that employs Maximum Power Point Tracking (MPPT) through Particle Swarm Optimization (PSO) to maximize an efficiency of a solar energy conversion. Furthermore, it investigates battery control using Adaptive Neuro-Fuzzy Inference System (ANFIS) to manage a storage and distribution of energy from both renewable sources and EVs. A microgrid is designed to cater to an energy demands of various establishments, including hospitals, universities, and EV charging stations. The comprehensive analysis of a micro grid’s performance, with the particular focus on effects of EV integration, is presented using MATLAB/Simulink, revealing an influence of EVs on an overall network stability and energy management.

Today in the world, the crop recommendations and predictions are highly required by the farmers to enhance the production of the crop. The paper will set out to review the different studies conducted on crop recommendations to enhance the crop production in India. The thorough research of multiple literature works can provide an insight into the fact that machine learning (ML) techniques play a crucial role in crop forecasting and advisory. The different ML algorithms that have been very effective in predicting the results are addressed with both pros and cons. This paper discusses the parameters employed in datasets, as well as, standard procedure employed to forecast the crop recommendations. Lastly different ML and crop prediction performance indicators are given. This whole paper provides the discussion of crop recommendation methodology.

Cybersecurity threats are rising in frequency and sophistication, which calls for the enhancement of better, real-time threat detection systems. This paper introduces a cyber threat intelligence (CTI) platform that combines a deep learning-based detection model with real-time log analysis with the utilization of security information and event management (SIEM) systems. A deep neural network trained with stochastic gradient descent (SGD) is at the heart of the proposed gadget, which uses log data to detect malicious activity. Logs are collected and consumed from the Wazuh platform, permitting real-time correlation and possibility tracking. A tailored dashboard presents a friendly interface for visualizing alerts and designs. The suggested structure mixes detection precision, machine scalability, and functioning responsiveness in changing network environments. Experimental outcomes demonstrate that the model is highly accurate and responsive in determining dangers, structuring its feasibility for real-time business environments.

Multilevel inverters (MLIs) have gained an increasingly high profile in the area of power electronics because they are compact in their design and high efficiency, particularly in electric vehicles (EVs) and photovoltaic (PV) systems. This study introduces an innovative MLI architecture leveraging the switched capacitor (SC) methodology to generate higher voltage levels with fewer components. By cascading SC units, an inverter design remains streamlined, effectively reducing a need for multiple active switches and gate driver circuits. This results in the more cost-effective, compact, and reliable system. To ensure stable DC voltage from a solar input, the fuzzy logic controller (FLC) is implemented in conjunction with the Single Input Multi Output (SIMO) converter, which boosts an input voltage above a DC link level. A proposed inverter topology is evaluated against other reduced-switch MLI configurations acquired from metrics like total harmonic distortion (THD), efficiency, and component count. Simulations, conducted in MATLAB/Simulink, along with experimental testing under variable load conditions, validate a practicality and performance of a design for use in EVs and renewable energy technologies.

The growing rates of air pollution are both highly hazardous to the human environment and health, and require effective and real-time monitoring solutions. This project is proposing a Smart IoT-based Air Quality Monitoring and Analysis System with ESP32 and Firebase. The suggested device combines several air quality sensors and ESP32 microcontroller to measure the parameters considered important in the environment (temperature, humidity, concentrations of harmful gases) continuously. The data obtained is sent wirelessly to Firebase cloud platform where real-time data can be stored, visualized and accessed remotely using web or mobile apps. The system has been built to offer quality, low-price and scalable monitoring to fit both urban and rural settings. With Firebase, users can monitor air quality patterns and get notifications when the pollution is above predetermined levels and securely store the data and synchronize it in real time. Moreover, the system aims to facilitate data analysis to identify patterns and possible sources of pollution, which can be used in informed decision-making and environmental consciousness. The proposed solution will improve accessibility and efficiency of environmental monitoring by integrating IoT technology with cloud computing. ESP32 is used, which means that the system has low power consumption and high connectivity reliability, which makes it feasible to be used in the continuous deployment. Altogether, the given project proves to be a good example of the efficient use of smart environmental monitoring, as it contributes to the improvement of the healthier lifestyle and helps to develop sustainable upcoming research works.