Vision based intelligent traffic light management system using Faster R‐CNN

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

Continual Reinforcement Learning for Intelligent Agricultural Management under Climate Changes

Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.

Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

An artificial intelligence diabetes management architecture based on 5G

Along with the development of 5G network and Internet of Things technologies,there has been an explosion in personalized healthcare systems.When the 5G and Artificial Intelligence(Al)is introduced into diabetes management architecture,it can increase the efficiency of existing systems and complications of diabetes can be handled more effectively by taking advantage of 5G.In this article,we propose a 5G-based Artificial Intelligence Diabetes Management architecture(AIDM),which can help physicians and patients to manage both acute complications and chronic complications.The AIDM contains five layers:the sensing layer,the transmission layer,the storage layer,the computing layer,and the application layer.We build a test bed for the transmission and application layers.Specifically,we apply a delay-aware RA optimization based on a double-queue model to improve access efficiency in smart hospital wards in the transmission layer.In application layer,we build a prediction model using a deep forest algorithm.Results on real-world data show that our AIDM can enhance the efficiency of diabetes management and improve the screening rate of diabetes as well.

Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era

Esophageal squamous cell carcinoma(ESCC)is the most common histological type of esophageal cancer with a poor prognosis.Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients.With the advancement of artificial intelligence(AI)technology and the proliferation of medical digital information,AI has demonstrated promising sensitivity and accuracy in assisting precise detection,treatment decision-making,and prognosis assessment of ESCC.It has become a unique opportunity to enhance comprehen-sive clinical management of ESCC in the era of precision oncology.This review examines how AI is applied to the diagnosis,treatment,and prognosis assessment of ESCC in the era of precision oncology,and analyzes the challenges and potential opportunities that AI faces in clinical translation.Through insights into future prospects,it is hoped that this review will contribute to the real-world application of AI in future clinical settings,ultimately alleviating the disease burden caused by ESCC.

Potential of Applying Artificial Intelligence to Hot Metal Logistics Management

The steel industry,known for its complexity and the need to reduce CO_(2)emissions,is adopting advanced digitalization tools to move towards a more sustainable,integrated,and agile operating model.Digital twins with artificial intelligence-based optimization and scheduling models can improve decision-making in logistics,refractory maintenance,and energy efficiency.By incorporating advanced AI algorithms into this decision support system,the hot metal route scenarios can be evaluated,resulting in minimized hot metal temperature losses and increased scrap utilization.This paper integrated digital twins with reinforcement learning algorithms to investigate the logistics of torpedoes and hot metal ladles.It considered important input parameters such as the ladles and torpedoes'thermal state and location,refractory thickness,hot metal volume and temperature,and crane availability.By incorporating advanced AI algorithms into this decision support system,energy-efficient scenarios can be evaluated,increasing scrap utilization and resulting in a possible reduction of 15°C in hot metal temperature losses.

Impact of artificial intelligence in the management of esophageal,gastric and colorectal malignancies

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate.Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality.Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes.Artificial intelligence(AI)-assisted diagnostic,prognostic,and therapeutic tools can assist in expeditious diagnosis,treatment planning/response prediction,and post-surgical prognostication.AI can intercept neoplastic lesions in their primordial stages,accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic,histopathological,and/or endoscopic analyses,and eliminate over-dependence on clinicians.AI-based models have shown to be on par,and sometimes even outperformed experienced gastroenterologists and radiologists.Convolutional neural networks(state-of-the-art deep learning models)are powerful computational models,invaluable to the field of precision oncology.These models not only reliably classify images,but also accurately predict response to chemotherapy,tumor recurrence,metastasis,and survival rates post-treatment.In this systematic review,we analyze the available evidence about the diagnostic,prognostic,and therapeutic utility of artificial intelligence in gastrointestinal oncology.

Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual agriculture.Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture.Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques.Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist.With this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent agriculture.The MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination purposes.Besides,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not needed.The MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

Intelligent Student Mental Health Assessment Model on Learning Management System

A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning procedure.Generally,the LMS offers a method of creating and delivering content to the instructor,monitoring students’involvement,and validating their outcomes.Since mental health issues become common among studies in higher education globally,it is needed to properly determine it to improve mental stabi-lity.This article develops a new seven spot lady bird feature selection with opti-mal sparse autoencoder(SSLBFS-OSAE)model to assess students’mental health on LMS.The major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression,anxiety,and stress(DAS).The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of features.In addition,OSAE model is applied for the classification of mental health conditions and the performance can be improved by the use of cuckoo search optimization(CSO)based parameter tuning process.The design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classifica-tion outcomes.For examining the improved classifier results of the SSLBFS-OSAE model,a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.

Intelligent Parking Management System by Multi-Agent Approach:The Case of Urban Area of Tunis

By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined solution is designed to help drivers in finding a parking space at anytime and anywhere. Three services are offered: the search for a vacant place, directions to a parking space and booking a place for parking. The results of this study generated by the platform MATSim transport simulation, show that our approach optimizes the operation of vehicles in a parking need with the aim of reducing congestion, and improve traffic flow in urban area. A comparison between the first method where the vehicles are random and the second method where vehicles are steered to vacant parking spaces shows that the minimization of time looking for a parking space could improve circulation by reducing the number of cars in the morning of 2% and 0.7% of the evening. In addition, the traffic per hour per day was reduced by approximately 4.17%.

Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

MigroGrid(MG)has emerged to resolve the growing demand for energy.But because of its inconsistent output,it can result in various power quality(PQ)issues.PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources.Similarly,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system.Thus,this research focuses on power quality enhancement in the MG using hybrid shunt filters.However,the performance of the filter mainly depends upon the design,and stability of the controller.The efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)controller.The performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this approach.Further,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in MG.From the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

Research on the intelligent internet nursing model based on the child respiratory and asthma control test scale for asthma management of preschool children

BACKGROUND Childhood asthma is a common respiratory ailment that significantly affects preschool children.Effective asthma management in this population is particularly challenging due to limited communication skills in children and the necessity for consistent involvement of a caregiver.With the rise of digital healthcare and the need for innovative interventions,Internet-based models can potentially offer relatively more efficient and patient-tailored care,especially in children.AIM To explore the impact of an intelligent Internet care model based on the child respiratory and asthma control test(TRACK)on asthma management in preschool children.METHODS The study group comprised preschoolers,aged 5 years or younger,that visited the hospital's pediatric outpatient and emergency departments between January 2021 and January 2022.Total of 200 children were evenly and randomly divided into the observation and control groups.The control group received standard treatment in accordance with the 2016 Guidelines for Pediatric Bronchial Asthma and the Global Initiative on Asthma.In addition to above treatment,the observation group was introduced to an intelligent internet nursing model,emphasizing the TRACK scale.Key measures monitored over a six-month period included the frequency of asthma attack,emergency visits,pulmonary function parameters(FEV1,FEV1/FVC,and PEF),monthly TRACK scores,and the SF-12 quality of life assessment.Post-intervention asthma control rates were assessed at six-month follow-up.RESULTS The observation group had fewer asthma attacks and emergency room visits than the control group(P<0.05).After six months of treatment,the children in both groups had higher FEV1,FEV1/FVC,and PEF(P<0.05).Statistically significant differences were observed between the two groups(P<0.05).For six months,children in the observation group had a higher monthly TRACK score than those in the control group(P<0.05).The PCS and MCSSF-12 quality of life scores were relatively higher than those before the nursing period(P<0.05).Furthermore,the groups showed statistically significant differences(P<0.05).The asthma control rate was higher in the observation group than in the control group(P<0.05).CONCLUSION TRACK based Intelligent Internet nursing model may reduce asthma attacks and emergency visits in asthmatic children,improve lung function,quality of life,and the TRACK score and asthma control rate.The effect of nursing was significant,allowing for development of an asthma management model.

Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

Personal Thermal Management by Radiative Cooling and Heating

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being.By merely broadening the setpoint of indoor temperatures,we could significantly slash energy usage in building heating,ventilation,and air-conditioning systems.In recent years,there has been a surge in advancements in personal thermal management(PTM),aiming to regulate heat and moisture transfer within our immediate surroundings,clothing,and skin.The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering.An emerging research area in PTM is personal radiative thermal management(PRTM),which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation.However,it is less taken into account in traditional textiles,and there currently lies a gap in our knowledge and understanding of PRTM.In this review,we aim to present a thorough analysis of advanced textile materials and technologies for PRTM.Specifically,we will introduce and discuss the underlying radiation heat transfer mechanisms,fabrication methods of textiles,and various indoor/outdoor applications in light of their different regulation functionalities,including radiative cooling,radiative heating,and dual-mode thermoregulation.Furthermore,we will shine a light on the current hurdles,propose potential strategies,and delve into future technology trends for PRTM with an emphasis on functionalities and applications.

Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

Patient satisfaction and follow-up adherence to glaucoma case management clinic in China

AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a total of 119 patients completed a Patient Satisfaction Questionnaire-18 and a sociodemographic questionnaire.Clinical data was obtained from the case management system.Follow-up adherence was defined as completing each follow-up within±30d of the scheduled time set by ophthalmologists during the study period.RESULTS:Average satisfaction scored 78.65±7,with an average of 4.39±0.58 across the seven dimensions.Age negatively correlated with satisfaction(P=0.008),whilst patients with follow-up duration of 2 or more years reported higher satisfaction(P=0.045).Multivariate logistics regression analysis revealed that longer follow-up durations were associated with lower follow-up adherence(OR=0.97,95%CI,0.95-1.00,P=0.044).Additionally,patients with suspected glaucoma(OR=2.72,95%CI,1.03-7.20,P=0.044)and those with an annual income over 100000 Chinese yuan demonstrated higher adherence(OR=5.57,95%CI,1.00-30.89,P=0.049).CONCLUSION:The case management model proves effective for glaucoma patients,with positive adherence rates.The implementation of this model can be optimized in the future based on the identified factors and extended to glaucoma patients in more hospitals.