Gongcheng Kexue Yu Jishu/Advanced Engineering Science

This article explores the transformative potential of Internet of Things (IoT) and Big Data in driving sustainable urban development in Chhattisgarh, India. It discusses how these technologies are enabling real-time monitoring, predictive analytics, and citizen engagement, thereby transforming cities into smart cities. The article also highlights the challenges and opportunities in leveraging these technologies for efficient resource management, improved public services, and promotion of sustainable practices. The conclusion underscores the immense potential of IoT and Big Data in creating smarter and more sustainable cities in Chhattisgarh.

The objective of this study is to design a breast cancer detection model using hybrid deep learning. The design model encapsulates pre-processing, feature extraction, and classification. Convolutional neural networks (CNN) and recurrent neural networks (RNN) are the names of the two deep learning architectures. Furthermore, the tumour-segmented binary image is regarded as input to CNN, and both GLCM and GLRM are regarded as input to RNN. The study's conclusion demonstrates that, in general, the AND operation of two classifier outputs will produce diagnostic accuracy that is superior to that of conventional models. We compare the proposed model with contemporary neural network systems. The proposed model outperforms the contemporary neural network model with a substantial prediction accuracy of 99.11%. The major contribution of this work is the development and application of a deep forest model for breast cancer classification. The proposed model was simulated in MATLAB 2018R software. For the validation of algorithms, test two reputed datasets, such as DDMS and MIAS. The analysis of the results suggests that the proposed algorithm is very efficient in terms of existing algorithms for breast cancer detection.

Automatic number plate recognition is a paramount component of development of smart cities. Accuracy and precision is major factors in automatic number plate recognition. For the improvements of accuracy recently several algorithms is proposed. This paper proposed feature optimization based cascaded convolutional neural network. the process of feature optimization employed moth flame optimization (MFO). The proposed cascaded CNN algorithm improves the performance of automatic number plate detection. The lining is based on information obtained from a CNN source. To train the neural network, a question expansion strategy is used to enlarge the training set through synthetic transformations, thereby increasing the recall rate. This method maintains high accuracy under varying light conditions and noise, although it is not adaptable to different environments. Experimental results indicate that while the data sets demonstrate the algorithm's strong generalization capabilities, they do not achieve high classification accuracy. However, the class’s average accuracy surpasses that of other methods, demonstrating that CNN is superior in recognizing vehicle identification, as shown by comparative test results.

The Internet of Things (IoT) is a technology that connects billions of devices, or "things," to each other (machine-to-machine) and to people through existing infrastructure. Real-world IoT applications include smart cities, smart homes, connected appliances, shipping and monitoring systems, smart supply chain management, and smart grids. As the number of devices worldwide increases across all aspects of daily life, vast amounts of data are generated. This surge in data brings about new challenges related to the development and use of current technologies, particularly concerning new applications, regulation, cloud computing, security, and privacy. Blockchain technology has shown promise in securing and preserving user data privacy in a decentralized manner. Specifically, Hyperledger Fabric v2.x, an open-source blockchain platform, offers versatility, modularity, and high performance. In this paper, we present a blockchain as a service (BaaS) application based on Hyperledger Fabric to address the security and privacy challenges associated with IoT. We introduce a new architecture to facilitate this integration, which has been developed, deployed, and analysed in real-world scenarios. Additionally, we propose a new data structure with encryption based on public and private keys to enhance security and privacy.

To improve the clarity and visual quality of the low light video images and also to identify hidden details in the images and also to solve the problem of information retrieval from the image caused by color distortion and low saturation are performing image enhancement with classic Retinex algorithm. In the Current scenarios Digital image are been limited in clarity and this is due to poor visibility conditions which may including bad weather like fog, smoke, rain and at dawn. These unique conditions limit the range and effects of imaging it can’t be enhanced further. we useRetinex algorithm here which is most popular and that can be implemented to image enhancement. The basic idea of Retinex algorithm is to exactly split or extract illumination from the reflectance for a given digital image. I have made a study and analyzed how to implemented Retinex algorithm based on color exactly decollation and color constancy with some more additional technique, These method associates in removing noise in the Image.Retinex algorithm has been utilized in cases of severe aerial photograph as well as image blurring with a noticeable results.

In every region of the globe, agriculture is the most important activity, and it is highly dependent on rainfall. It was possible to forecast the rate of agricultural output by taking into account of some factors such as precipitation, wind, temperature, soil, fertilizer and solar radiation. Alterations in the weather play a significant part in crop yield. The accurate projection of crop yields at an early stage is beneficial for market pricing, arranging labour, transportation, and harvest organization. The primary objective of this research is to make an accurate prediction of crop yield. Introduction of deep learning models allows the perfect prediction of production rates. In this paper, an Improved Adaptive Moment Estimation Optimizer Function (IAOF-BiLSTM) is an Enhanced Optimizer Algorithm (EOA) is proposed to get an accurate prediction of yield along with the Bidirectional Long Short-Term Memory (BiLSTM) model. Data was collected from the open source repositories and government websites of Tamilnadu (India) from 1965 to 2022 for training and testing purpose. The proposed model is compared with other existing standard deep learning models and outcome of the proposed IAOF-BILSTM optimizer with Bi-LSTM algorithm predict very well and shows the less error rate in training data and also the algorithm effectively handles issues of underfitting and overfitting in predictions. The performance metrics such as MAE, RMSE and R2 were used to compare the loss value with existing models. The evaluated value is compared between the actual and predicted crop yield and the achieved results are measured using MAE, RMSE and R2.The result shows that the improved optimizer algorithm IAOF-BiLSTM model gives better accuracy and less error rate compared with existing models.

In this study, we investigate the optimization of energy efficiency in Device-to-Device (D2D) communication systems through the implementation of a deep learning-based resource allocation algorithm. Effective energy utilization is crucial for enhancing the performance of D2D networks by reducing interference and improving the quality of service for secondary users. Our proposed algorithm leverages deep reinforcement learning to dynamically manage energy resources, thereby optimizing the allocation process in real-time. The integration of deep learning techniques allows the system to adapt to varying network conditions and user demands, resulting in significant performance improvements. Experimental results demonstrate that our approach not only extends device battery life and reduces operational costs but also enhances the overall reliability and sustainability of D2D communication networks. This research provides a comprehensive framework for achieving energy-efficient resource allocation in next-generation wireless communication systems.

In this paper, we introduce a novel encryption algorithm that leverages binary transposition and a unique custom ASCII mapping to secure plaintext messages. The suggested technique employs a unique ASCII encoding strategy in conjunction with binary XOR operations in a two-step encryption procedure. The algorithm's performance and security are assessed, indicating how well it protects the privacy of data. This technique offers a fresh take on encryption that is resistant to several cryptographic attacks and computationally efficient.

Recent advancement in information technology have improved the design and development of disease prediction systems significantly. The disease prediction system is useful in diagnosing diseases by analyzing medical data. In this digital world, disease prediction systems are extremely important, especially during pandemic situations when physicians are in high demand and people are unable to reach hospitals to monitor and diagnose their health conditions. Many medical expert systems and disease prediction systems have been published in recent years. Still, there is a gap for people to have an effective disease prediction system to predict a patient's disease and severity level at the right time. Predicting the impact level of disease in the human body is considered one of the most difficult issues nowadays due to the increase in voluminous medical data with various new symptoms. This paper presents a survey of machine learning and deep learning techniques for dengue fever prediction. It covers methods like association rule mining, decision trees, clustering, and neural networks, assessing their accuracy and practicality. Challenges like data imbalance and environmental factors are discussed, offering insights for future research in disease prediction.

In present world fossil fuels like petrol, diesel and gas are used in vehicles and it causes pollution. To overcome pollution caused during combustion of fuels EV's are introduced, here they use stored electrical energy from battery as a source to the vehicle. Traveling for a long distance is a challenge for an EV user as there is no charging station available during his travel. The user has to carry their own charger if the brand is different. Only few brands offer fast charging. In present situation there is no single charging station for all types of EV. The charging station available in market charges only their company’s EV's and has only few smart features and are not available for multiple brands. The Universal Electric Vehicle Charging Station (UEVCS) provides charging facilities for all types of EVs including Fast charging facility. It is smart in both hardware as well as in software. The real time monitoring of the battery with multiple brand EV charging facility is the main aim of UEVCS. The efficiency and health of the battery is maintained by providing protection circuit. UEVCS is user friendly providing multiple EV charging facility with real time monitoring of the battery.

This paper presents a comprehensive exploration of automatic identification technologies, focusing on Barcode, Radio-Frequency Identification (RFID), and Chipless RFID. These technologies are analyzed in terms of their application domains, technical specifications, and operating principles. They are essential to contemporary supply chains and asset management systems. The paper explores the unique characteristics and possible applications of every technology, providing information about their advantages and disadvantages. A comprehensive grasp of the practical ramifications of various technologies is provided by the comparative analysis, which takes into account factors like cost, scalability, security, and data storage. The research attempts to assist practitioners, researchers, and decision-makers in choosing the best identification technology based on particular criteria and application situations by synthesizing this data. The study sheds light on the advantages of Chipless RFID for computer science students, researchers, and engineers who are not familiar with this cutting-edge technology. It also shows the changing landscape of identifying technologies and their prospective impact on industries.

In contemporary society, electricity has emerged as an indispensable necessity, spanning from household to industrial operations. With conventional energy reservoirs dwindling steadily, there is an imperative to transition from traditional to non-traditional energy outlets for electricity generation. Renewable sources, devoid of adverse environmental impacts, stand as an innovative avenue for fostering clean energy production. Embracing solar, wind, biomass, hydropower, geothermal, and oceanic resources presents a promising trajectory towards sustainable energy generation. Despite solar and wind energy's output trailing behind fossil fuel production, the adoption of photovoltaic cells and wind turbines has surged in recent years. Solar panels harness solar energy for conversion into electricity, while wind turbines facilitate the conversion of wind energy into electrical power. The concept of a solar-wind hybrid system amalgamates solar and wind energy plants, offering consistent power output. During inclement weather conditions, seamless transition between the two plants is facilitated through a microcontroller, ensuring optimal resource utilisation and system efficiency enhancement compared to standalone generation modes. This hybrid approach finds application in both industrial and domestic settings, striving towards electricity generation devoid of non-renewable resource dependency and ecological imbalance.

The banking sector is eventually undergoing a much profound transformation with several specific tools such as the artificial intelligence which is noticed to be emerging as the significant medium for the required change towards banking institutions. This analysis will signifcantly analyze the signifcance of artificial intelligence in the banking sector. The applications of artificial intelligence which are involved in the banking sector to provide better services will also be known. The authenticated usage of the AI bots in the banking sector and its significance will be eventually noticed.

A greater number of cardiovascular diseases (CVDs) are becoming more prevalent all over the world, and as a result, it has come to light that there is an urgent and pressing need for more modern technology that is capable of diagnosing and treating disorders related to the cardiovascular system. This need has been brought to light as a result of the fact that the prevalence of CVDs is increasing. It has come to light that this need is necessary as a consequence of the fact that the number of cardiovascular diseases is growing. Because cardiovascular illnesses are currently the second biggest cause of mortality on a global scale, after cancer, there is an urgent need for the development of creative solutions within the field of cardiology. This is because of the fact that cancer is the main cause of death. Cancer is the main cause of death all over the globe, which is the reason why this is the case. An application of artificial intelligence (AI) has been used in the field of cardiology with the intention of predicting illnesses, carrying out diagnostic operations, and developing treatment strategies. Not only is the introduction of this specific innovation one of the breakthroughs that has taken place, but it also has the potential to be of significant service to the community as a whole. Recent events have shown that the use of artificial intelligence technology in the area of medicine, and more specifically in the field of cardiology, has resulted in substantial advancements in the world of medicine. In recent times, a great number of discoveries have been produced, and these discoveries have been the driving force behind these advances. The findings of a study that was carried out to analyze the function that artificial intelligence performs in this industry led the researchers to the conclusion that it has the extraordinary potential to bring about significant changes in the treatment methods that are now being used. Currently, there are a great number of applications of artificial intelligence that are being used within the discipline of cardiology. The objective of this study is to investigate the many applications that are currently being utilized. Machine learning (ML) and deep learning (DL) models are being used, which is one of the primary areas of focus that the investigation is focusing on. This investigation is being carried out with the purpose of boosting the precision and efficacy of medicines that are used for the treatment of cardiovascular diseases. In the field of cardiology, artificial intelligence models have reached accuracy rates that are more than 83%, therefore demonstrating their dependability as diagnostic tools. The purpose of this research is to bring attention to the fact that these models have attained these levels of accuracy. With the purpose of drawing attention to the fact that these models have been developed, this study is being carried out that is now being carried out. The production of a synthesis of the research articles that are included within the Medline database is one of the actions that is carried out in order to be successful in accomplishing this objective. This research focuses light on the need of completely capitalizing on the potential that artificial intelligence has to offer in the field of cardiology by putting light on the crucial relevance of predictive modeling, feature selection, and algorithmic breakthroughs. Overall, this study throws light on the importance of using AI to its fullest potential. This is something that really has to be done in order to make the most of the prospects that are presented by artificial intelligence. With the use of artificial intelligence in this specific field, it is anticipated that a revolutionary influence would be brought about as a consequence of the utilization of this technology. This is because technology will make it feasible to perform precision medicine, which will eventually result in better outcomes for patients. This is the reason why this is the case. This milestone has been accomplished in spite of the fact that there are challenges, such as algorithmic biases and security concerns associated with data privacy. Despite the fact that there are impediments, this accomplishment has been accomplished. Over the course of the next few years, it is anticipated that the role that technologies based on artificial intelligence play in the treatment of cardiovascular problems will dramatically grow. This is a prediction that has been made. This is due to the fact that the development of these technologies is still in progress, which is the fundamental reason why this is taking place. As a result of this, the field of cardiology will enter a new age, both in terms of the diagnostic and therapeutic treatments that are now being carried out. This will include both the diagnostic and therapeutic procedures.

The battery is the most crucial and costly component in electric cars, serving as the sole provider of electric power. However, the gradual weakening of the battery's power source leads to reduced performance, posing a significant concern for battery makers. This research proposes utilizing Internet of Things (IoT) methods to monitor and display battery performance. Various measurements such as voltage, current, and temperature are monitored, analyzed, and displayed to alert users about overcharging and other issues through diverse sensors. A microcontroller unit receives data on voltage, current, and temperature and sends battery data to the cloud for display. This paper details a monitoring framework for both battery-powered and fuel-type vehicles, tested under specific conditions to ensure battery state operations are effectively monitored. It also describes the different functions of a Battery Management System (BMS) with a focus on accurate state of charge (SoC) estimation. The advantages and drawbacks of various estimation methods are presented, highlighting how a BMS can utilize these indicators. Electric vehicles (EVs) are at the forefront of sustainable transportation solutions, with their widespread adoption dependent on battery pack performance and longevity. Monitoring and computing systems are essential for ensuring the reliability, efficiency, and safety of EV batteries throughout their operational lifespan. This paper provides an overview of current methodologies, technologies, and advancements in EV battery pack monitoring and computing. It examines the role of sensors, data acquisition techniques, and computational algorithms in assessing key parameters such as SoC, state of health (SOH), and remaining useful life (RUL) of battery packs. Additionally, it explores the integration of real-time data processing, machine learning models, and predictive analytics for proactive maintenance and performance optimization. Challenges such as data security, standardization, and scalability are discussed, alongside potential future directions to enhance the effectiveness of EV battery monitoring and computing systems. This review aims to contribute to the ongoing development and implementation of robust monitoring solutions essential for advancing the EV ecosystem.

This study evaluates the groundwater potential for extraction in the piedmont zone of the Periyar River Basin, located in central Kerala, India. Recognised as the longest river in Kerala, the Periyar River is crucial for sustaining the region’s ecosystems and meeting the water demands of its population. The assessment involves a comprehensive examination of geological, hydrogeological and hydrological factors using Geographic Information system (GIS) tools and the Analytic Hierarchy Process (AHP) within a Multiple Criteria Decision Analysis framework. Groundwater potential zones are identified based on influential parameters such as drainage density, lineament density, rainfall, topography, soil texture, aquifer geology and land cover patterns. Each parameter is meticulously mapped, reclassified and weighted to create a composite groundwater potential map. The study reveals areas with varying groundwater potential, categorized into moderate, high and very high potential zones. Findings indicate that regions with lower drainage density, higher lineament density and favorable soil and geological conditions exhibit greater groundwater potential. The results underscore the importance of sustainable groundwater management practices, especially in light of increasing water demands and environmental challenges. This study provides critical insights for informed decision-making regarding resource allocation and the implementation of preventive measures to mitigate overexploitation and enhance groundwater recharge. Overall, this research contributes to the sustainable management of groundwater resources in the Periyar River basin, offering a valuable tool for policymakers, environmentalists and water resource managers.

A sophisticated system that uses the Internet of Things (IoT) to detect face masks and screen persons fast is the goal of the "IoT-based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control" project. By integrating a camera with a Raspberry Pi (RPI) for image processing and face mask detection, the system provides real-time feedback on whether an individual is wearing a face mask. This project offers an efficient and automated solution to enhance infection control measures, particularly in public spaces and healthcare settings. Make sure these devices can handle a lot of people quickly and accurately. Use the Internet of Things to track who needs to wear a face mask and enforce it in public areas. Use cameras and sensors to see if people are wearing masks and send out instant notifications if they aren't. Create a hub that can handle all the data from the screening devices and the systems that detect when people aren't wearing masks. Implement secure data transmission protocols to protect sensitive health information collected during screening and monitoring. Ensure compliance with data protection regulations to maintain the privacy and confidentiality of individuals. Strategically deploy the IoT-based system in high-traffic and high-risk areas such as airports, schools, and public transportation hubs.

The Raspberry Pi Pico microprocessor is the system's core. It checks the amount of moisture in the soil in real time and carefully manages a 230V water pump so that irrigation can be controlled precisely. The system constantly checks the soil's water level by strategically placing advanced soil moisture sensors. Based on set moisture thresholds, the system starts or stops the water pump automatically. This process makes sure that water resources are used efficiently and increases agricultural productivity by exactly delivering water to plants.The Internet of Things (IoT) monitoring features are built into the system. This means that users can check on soil moisture levels and pump function from afar using a sophisticated web interface or a mobile app. The sensor data is sent to the cloud and carefully analyzed so that deep insights into how to water and check the health of the land can be gained.

In the digital era, safeguarding sensitive information has become more critical than ever. One prominent method for securing data is steganography, which involves concealing a message within an unassuming cover medium, such as an image, to avoid unwanted attention or interception. Unlike encryption, which makes data unreadable, steganography hides the very existence of the message, making the communication covert and imperceptible to the uninitiated eye.This study illustrates a simple and effective technique of text steganography using Python's OpenCV library, a tool adept at image manipulation. The process unfolds in several straightforward steps, beginning with the conversion of the chosen text message into a binary format, a language that is innately understood by computers. Following this, the binary message is intricately woven into the least significant bits (LSBs) of the picture's pixel color channels, a technique which cleverly conceals the message without altering the image's visible properties. To retrieve the concealed message, a reverse process is employed, where the hidden binary bits are extracted from the image and then translated back into its original text format, using ASCII character codes.This method represents a user-friendly approach to steganography, offering a seamless way to secure data transmission by embedding information within an image in a manner that remains both covert and imperceptible, hence promising a robust solution against potential cyber threats.

Water is a critical component in the concrete mix, contributing to its workability and hydration process. Adequate water content is necessary for the proper mixing, placing, and curing of concrete. In areas with lower water availability, managing water in concrete becomes even more crucial, Self-curing concrete is therefore recommended as it reduces the amount of water used during construction process. Concrete is a formulated by mixing cement, fine aggregate, coarse aggregate, and water to produce a cohesive material. The important constituents in concrete is aggregate because it improves the strength and stability of concrete while reducing shrinkage. To conserve aggregate, it is vital to identify substitute aggregate to replace coarse aggregate. Generally, any kind of waste can be used to find alternative materials, which will provide dual advantages. One of the benefits is the use of alternative materials and another is the ability to dispose of trash. The purpose of this research is to analyse the performance of Bethamcherla stone as the partial substitute for coarse aggregate by varying percentages of 10%, 20%, 30%, 40% and 50% incorporating to that poly-vinyl Alcohol by various percentages of 0.03%, 0.06%, 0.12% and 0.24% by weight of cement. The extent of work includes determine Workability of freshly prepared M20 grade concrete by compaction factor and slump cone test and mechanical qualities of concrete including Compressive Strength and Split Tensile Strength for 28 days and 90 days of curing.

Rotating Packed Bed (RPB) provides enhanced flexibility and the opportunity for significant size reductions in distillation processes by intensifying mass transfer. Rotating packed beds have a distinct advantage over conventional columns, mainly due to the intense micro-mixing within the fluid channels. Thinner liquid films will be formed in RPB’s which greatly enhances mass transfer between phases. In current study, distillation of methanol water system at total reflux condition was experimental investigated. The mass transfer performance was studied by varying the rotational speed and gas flow rate in the rotating packed bed. It was found that rotational speed has a significant impact on mass transfer effectiveness. The Height Equivalent to a Theoretical Plate (HETP) in the rotating packed bed used in this study was 3 to 4 times lower than that of conventional packed columns, indicating superior efficiency. Additionally, Response Surface Methodology (RSM) was employed, along with analysis of variance (ANOVA), to optimize the process parameters. The design of experiments was carried out using Minitab, with the mass transfer coefficient selected as the response variable in RSM, dependent on other independent variables. The optimized mass transfer coefficient was achieved at a rotational speed of 800 rpm and a gas flow rate of 0.23 kg/m²s. According to the ANOVA analysis, the gas flow rate was determined to be insignificant, whereas the rotational speed was found to be significant at a 95% confidence interval.

Concrete, known for its versatility and durability, is the material for construction that is most commonly utilised worldwide, comprising key ingredients such as cementitious materials, fine aggregate, coarse aggregate, and water. However, the growing demand for construction has necessitated the exploration of alternative materials that can replicate the performance of conventional components. This study focuses on the development of M-50 grade concrete using innovative substitutions to enhance sustainability and performance. The coarse aggregate is partially replaced with Bethamcherla stone in proportions of 10%, 20%, 30%, 40%, and 50% by weight. This stone, known for its unique characteristics, offers a potential alternative to traditional coarse aggregates, with an aim to improve resource efficiency. In addition to Bethamcherla stone, a self-curing agent, Polyvinyl Alcohol (PVA), is incorporated into the mix at 0.03%, 0.06%, 0.12% and 0.24% by weight of cement. PVA, known for its moisture retention capabilities, enhances the curing process by minimizing external water requirements, which is particularly beneficial in areas facing water scarcity. Furthermore, the study incorporates 10% silica fume, a highly reactive pozzolanic material, as a partial replacement for cement. Silica fume contributes to the development of a denser concrete matrix by improving the microstructure, ultimately increasing the concrete's compressive strength and durability. To evaluate the performance of this modified concrete, both fresh and hardened properties are analyzed. Fresh properties, such as workability and slump, are tested to ensure the concrete mix remains practical and easy to handle during placement. Hardened properties, including compressive strength and split tensile strength, are measured after 28 and 90 days of curing. These tests are crucial for understanding how the incorporation of Bethamcherla stone, PVA, and silica fume affects the long-term strength and performance of the concrete. This research aims to contribute to the development of sustainable, high-performance concrete by addressing key challenges such as resource conservation, water management, and

This research is focused on developing an automated system for forecasting COVID-19 cases by leveraging pre-trained convolutional neural network (CNN) models and chest X-ray images. The primary objective is to create a model with significant clinical potential for early diagnosis of the disease, achieved by integrating advancements in computer vision and medical image analysis. The research aims to evaluate a range of convolutional neural network architectures for automated COVID-19 detection using X-ray images, and to assess the efficacy of these models through expert diagnosis facilitated by deep learning techniques. Given the constraints posed by a limited dataset, the study identifies InceptionNetV3 as the most suitable model due to its superior performance metrics. Specifically, the InceptionNetV3 model demonstrated an impressive accuracy of 98.63% when data augmentation techniques were applied. This high accuracy underscores the model's robustness in handling the challenges associated with limited data. Without data augmentation, however, the models tend to overfit, a common issue when training on small datasets. To address this, the proposed deep learning model has been meticulously trained using X-ray images from patients diagnosed with COVID-19, as well as images from healthy individuals and those with pneumonia. This approach is designed to enhance the model’s ability to distinguish between different conditions and improve diagnostic accuracy. The overarching goal of this research is to assist healthcare professionals by providing a reliable tool for early COVID-19 detection, thereby supporting better clinical decision-making. Additionally, the study outlines the procedures involved in utilizing transfer learning techniques for the automated detection of COVID-19, contributing to the broader field of medical imaging and deep learning.

Object detection is extensively utilized in computer vision and is essential for various Applications, like self-driving cars. Over the past fifty years, object detection techniques have evolved significantly, leading to numerous innovative approaches with notable success. Today, object recognition methods primarily fall into two categories: traditional machine learning techniques and deep learning methods. This article reviews object detection techniques, first summarizing traditional machine learning-based methods. It then examines two prominent deep learning approaches, R-CNN and YOLO. Finally, the article compares and discusses the mentioned methods.

The article presents the results of a study of the working conditions of operators of one of the call centers in Tashkent and employees of the information technology department of the department for the development and design of telecommunication networks of a communications enterprise. The studies found that the levels of noise, magnetic field in the range of 5 Hz - 2 kHz, electric field in the range of 5 Hz - 2 kHz and illumination at the workplaces of call center operators exceed the requirements of hygienic standards. Non-compliance with regulatory requirements is noted in 50.9% of cases of noise levels, magnetic field - 3.6%, electric field - 67.3% and illumination - 10.9%. At the workplaces The communications enterprise revealed that the standard values of the alternating electric field in the range of 5 Hz – 2 kHz were exceeded by 45%, by illumination by 31.8%, and by noise values by 63.6%.

This study investigates the affect (IoT), digital structures on virtual inside emerging economies. Using a quantitative research design, records had been gathered from 397 CEOs and dealing with administrators of SMEs in Pakistan, and analyzed the usage of correlation and structural equation modeling (SEM). The consequences imply virtual platforms considerable drivers’ sustainable digital innovation. Furthermore, digital systems mediate the connection between virtual orientation and sustainable innovation, as well as IoT and sustainable innovation. The observe additionally explores moderating role entrepreneurial orientation, (RBV) and (DCV) theories. Findings from 319 SME employees in India, analyzed via PLS-SEM, demonstrate that adopting digital technologies considerably enhances monetary sustainability and social price. Entrepreneurial orientation similarly moderates the impact of social and financial value introduction on SME overall performance. The observe highlights the want for frugal enterprise models to enable SMEs to navigate fast technological adjustments, reduce aid consumption, and decorate competitive advantage in a rapidly evolving virtual financial system.

The worldwide freshwater disaster, pushed through growing pollution and high energy expenses for conventional water remedy, demands progressive answers. This evaluate specializes in superior water filtration techniques powered via solar energy, highlighting current breakthroughs in membrane generation, along with the usage of plasmonic nanoparticles to enhance membrane performance and decrease fouling. It additionally covers the promising utility of microwave discharge plasma in liquid (MDPL) for efficiently eliminating persistent pollutants like perfluorooctanoic acid (PFOA). Additionally, the assessment discusses the potential of natural fibers, considerable in Asian nations, for water filtration due to their renewability and biodegradability. Finally, it examines reversible-deactivation radical polymerizations (RDRPs), particularly atom switch radical polymerization (ATRP), and their alignment with green chemistry ideas, showcasing their position in developing sustainable filtration materials. This synthesis of present day improvements provides a complete assessment of the way solar electricity and revolutionary substances can cope with worldwide water demanding situations.

The boom of the new 5G applications in the marketplace hence requires even a quicker shape of communique main to expectation of the 6G conversation generation in the subsequent ten years. This essential appraisal of 5G wireless networks and the predicted future troubles of 6G communique networks display the manners in which technology is constrained and the possibility of reaching a hundred% improvement with regards to 5G networks. Today’s statistical investigations estimate that cell net traffic will keep growing drastically and could attain five zettabytes according to month already via 2030. From 5G to 6G, it is hoped that the regulations of the current 5G networks could be rectified especially in phrases of coping with giant Records full-size applications and the developing style of clients. 6G wi-fi communique structures are expected to cast off the critical aspect shortcomings of 5G networks and provide extraordinarily long-variety communique with extremely low latency and approximately 1 Tbps in line with consumer and thousandfold higher wi-fi verbal exchange speed evaluating with 5G. Gathering all the records concerning the results that the 5G era can have on the destiny IoT devices, this paper gives vital information on the demanding situations and directions for the in addition 6G conversation technology development. This arises the significance of envisioning 6G wi-fi communique device to tally all traits of the new smart programs and additionally to cater several IOE based services.

Vehicular Ad Hoc Networks (VANETs) are integral to modern intelligent transportation systems, enabling vehicles to communicate for enhanced road safety, traffic management, and efficient routing. However, the reliability and security of VANETs are often challenged by various anomalies, such as cyberattacks, rogue vehicles, or sensor failures, which can disrupt normal operations. To address this, an efficient and real-time anomaly detection system is crucial. This research work proposes a hybrid anomaly detection framework Integrated Anomaly Detection Model (IFAE-IADM) that combines Isolation Forest and Autoencoder models through a majority voting mechanism to identify anomalous patterns in VANET data. The Isolation Forest model detects anomalies by isolating data points with fewer partitions, while the Autoencoder neural network identifies irregularities through reconstruction error. Both models are trained on a synthetic dataset simulating vehicular movement, speed, communication parameters, and data traffic. The framework is evaluated using performance metrics such as accuracy, precision, recall, and F1-score, with the combined model achieving improved results over individual models. This work demonstrates the effectiveness of hybrid anomaly detection in VANETs and provides valuable tools for maintaining network security and reliability.

The article presents the results of a study of the working conditions of operators of one of the call centers in Tashkent and employees of the information technology department of the department for the development and design of telecommunication networks of a communications enterprise. The studies found that the levels of noise, magnetic field in the range of 5 Hz - 2 kHz, electric field in the range of 5 Hz - 2 kHz and illumination at the workplaces of call center operators exceed the requirements of hygienic standards. Non-compliance with regulatory requirements is noted in 50.9% of cases of noise levels, magnetic field - 3.6%, electric field - 67.3% and illumination - 10.9%. At the workplaces The communications enterprise revealed that the standard values of the alternating electric field in the range of 5 Hz – 2 kHz were exceeded by 45%, by illumination by 31.8%, and by noise values by 63.6%.

The revolutionary effects of AI and ML on managing financial risks are examined in this comprehensive article, with an emphasis on the potential and difficulties these technologies provide. Forecasting, investing strategies, financial services, and risk management are just a few of the crucial fields that AI and ML have transformed. As a result, forecasts are now more accurate and operations are more efficient. Better financial performance is eventually the result of financial organisations being able to make better judgements because to these improvements. Deployment of artificial intelligence and machine learning is not without major challenges, though. Many algorithms' "black box" nature makes it more difficult to explain and hold accountable models, which erodes stakeholder and regulatory trust. Financial institutions are also more susceptible to cybersecurity risks and data privacy issues as a result of their growing reliance on AI, which may jeopardise customer trust and legal compliance. Additionally crucial is the robustness of AI systems; these models need to withstand hostile attacks and unforeseen changes in the market. Furthermore, the widespread use of AI in banking may jeopardise the equilibrium of the economy overall as linked systems may transfer shocks more rapidly. This article provides a comprehensive evaluation of the current level of AI and machine learning in managing financial risk, along with significant legislative concerns to lower associated risks. Financial institutions can make the most of AI and ML to build a more secure and efficient financial system. ecosystem by tackling these issues, which will ultimately educate policymakers and practitioners about the two-pronged nature of financial innovation.