Cognitive computing is a ground-breaking notion in AI which replicates the thought pattern of the human mind. As a result, Industry 4.0 robotics is slowly flourishing owing to cognitive computing. The development of a...
Cognitive computing is a ground-breaking notion in AI which replicates the thought pattern of the human mind. As a result, Industry 4.0 robotics is slowly flourishing owing to cognitive computing. The development of a variety of machinelearning and artificial intelligence techniques has already made it possible to see the beginning of the march toward improved decision-making and data-driven intelligent manufacturing. However, there are a number of additional issues that need to be addressed, such as issues with performance, limitations of data resources, and attacks including poisoning. Recent research efforts have just scratched the surface of the problem, which often leads to erratic performance, inefficiency, and the disclosure of private information. We built a decentralized paradigm for massive data-driven cognitive computing (D2C) in this research by mixing blockchain technology with federated learning. D2C stands for data-driven cognitive computing. The problem of a "data island" may be solved by federated learning if privacy is protected and efficient processing is carried out, while blockchain technology gives incentives in the form of a potent anti-poisoning system in a truly decentralized method. Convergence may be sped up with the use of blockchain technology enabled federated learning by taking advantage of better verifications and member choices. The results of a comprehensive review and assessment demonstrate that D2C is more effective than the designs and models that are now the industry standard.
This The growing need for highly directive and reconfigurable antennas in contemporary communication systems has fueled significant research in antenna array synthesis. This study introduces a new approach to antenna ...
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ISBN:
(数字)9798331507671
ISBN:
(纸本)9798331507688
This The growing need for highly directive and reconfigurable antennas in contemporary communication systems has fueled significant research in antenna array synthesis. This study introduces a new approach to antenna array synthesis employing a neural network model built in TensorFlow. This model predicts optimal parameter combinations to achieve desired radiation patterns at an operating frequency of 2.4 GHz. Utilizing a 3-layer architecture and incorporating ReLU activation functions in the hidden layers, the model effectively manages non-linear relationships within the data. Training is conducted on simulation data acquired from a 2xl patch antenna array designed in ANSYS HFSS software. The antenna elements are positioned at a distance of 0.28λ to examine the influence of deep learning on closely coupled arrays. Results confirm the model's accuracy in predicting optimal phase combinations for both validation and custom radiation patterns, emphasizing its potential to simplify antenna design and optimization. This research offers valuable insights into the use of machinelearning in antenna array synthesis, advancing the development of high-performance antenna systems for future communication technologies.
Several industries have embraced blockchain technology due to the change it has brought, especially in the way records are kept. However, one major problem inherent in the concept of blockchain from the participants p...
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ISBN:
(数字)9798331504861
ISBN:
(纸本)9798331504878
Several industries have embraced blockchain technology due to the change it has brought, especially in the way records are kept. However, one major problem inherent in the concept of blockchain from the participants perspective is the presence of unpredictable rewards in proof-of-work and proof- of-stake models. That is why this research aims at developing a new strategy to solve this problem by incorporating artificial intelligence (AI) as well as machinelearning (ML) algorithms into blockchain compensation prediction. This project seeks to analyze how artificial intelligence (AI) and machinelearning (ML) methods may be incorporated into blockchain technology to solve the problem of the unpredictability of rewards. Using the EtherScan API, we have obtained 1000 timestamps and block reward values, and for prediction of rewards for certain timestamps, we use the KNN algorithm, linear regression algorithm, and random forest regressor algorithm. Thus, a What We KNN attained an accuracy of performance specifies, and a random forest regressor attained an impression accuracy range of 74% particular timestamps. These outcomes show the benefits of advanced ML to the blockchain ecosystem as a tool that helps stakeholders make informed decisions concerning activities like mining, staking, or investing in blockchains with potentially high rewards.
Attention deficit hyperactivity disorder (ADHD) develops in children's brains, portrayed by symptoms of inattentiveness, hyperactivity, and impulsivity. Early identification is very much essential, but traditional...
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ISBN:
(数字)9798331519582
ISBN:
(纸本)9798331519599
Attention deficit hyperactivity disorder (ADHD) develops in children's brains, portrayed by symptoms of inattentiveness, hyperactivity, and impulsivity. Early identification is very much essential, but traditional diagnostic methods might be subjective in practice and consuming time. Hence this work intends to introduce an end-to-end hybrid deep-learning system which fuses the techniques CNN and Bi-LSTM Networks to develop a system using handwriting to detect ADHD in children. The Bi-LSTM layers capture the sequential dependencies that exist in the handwriting data, capturing the temporal irregularities, whereas the CNN layers extract spatial features such as stroke patterns and pressure variations. The training and evaluation of the proposed model was done by synthetically balancing the dataset with accuracy, Confusion matrix, ROC curve and AUC for assessing performance. A method that has surpassed an accuracy of 90% in classification, this proposed system has been able to properly point out ADHD-related traits by hand. This method is scalable as it approaches to do fewer computations for the detection, thus making it a real data-driven alternative to traditional methods. Future work: Add attention mechanisms to increase the model's ability toward generalization, expand available datasets, and improve generalized capabilities.
The IoT-based Smart Water Management System represents modern state-of-the-art technology which provides effective control of water resources for urban and rural areas. This system enables real-time assessment of wate...
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ISBN:
(数字)9798331525439
ISBN:
(纸本)9798331525446
The IoT-based Smart Water Management System represents modern state-of-the-art technology which provides effective control of water resources for urban and rural areas. This system enables real-time assessment of water levels as well as stream flow and quality and usage patterndata by using advanced sensors and actuators and Internet of Things devices. The water delivery system benefits from precise wastage reduction through NodeMCU processing of data acquired from Ultrasonic Sensor and flow sensor inputs that monitor water levels and leak detection. Motor pumps and valves receive data through the Driver Circuit. Cookies send the data to cloud infrastructure to display vital information through web-based dashboards for remote monitoring purposes. The system employs machinelearning algorithms for detecting anomalies while also predicting future water needs to adjust the water distribution. The system uses Java's animation function to display water tank levels chaotically which provides both improved visual interface and instant system performance monitoring. The method saves 30% of water resources while reducing human labor expenses and ensures more sustainable water management and energy-efficient solutions. The proposed plan shows successful implementation for eco-conscious water resource management and environmental preservation within industrial spaces and agricultural zones and smart urban areas.
Using available channels in a wireless spectrum, cognitive radio (CR) may automatically adjust transmission settings to optimize radio operational behaviour. To function properly, a CR ad hoc network (CRAHN) has to be...
Using available channels in a wireless spectrum, cognitive radio (CR) may automatically adjust transmission settings to optimize radio operational behaviour. To function properly, a CR ad hoc network (CRAHN) has to be dynamically capable construct autonomous and decentralized networks without negatively impacting licensed main user (PU) systems. For this reason, an effective spectrum necessitates a system structure based on artificial intelligence. This research provides a model for network planning, learning, and dynamic configuration that is based on a distributed autonomous CRAHN network system that uses reinforcement learning. The proposed optimization techniques for spectrum sensing, ad hoc network design, and context-aware signal categorization are all derived from the system model and are based on machinelearning. The cognitive and detection engines may be used to examine the spectrum utilization and neighbour network status in the immediate area. To adapt to the ever-changing nature of the wireless environment, the suggested policy engine may generate network operating policies, identify policy conflicts, and infer the best course of action. Together with the erudition engine, whereby apply the recommended machine-learning methods, the decision engine arrives at the best possible settings for the CRAHN. In addition, guarantee peaceful cohabitation with surrounding systems to have excellent signal context recognition ability.
Land use and land cover (LULC) classification is a fundamental approach for landscape pattern mapping and monitoring of natural ecosystems and anthropogenic activities and is, therefore, an essential base for resource...
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ISBN:
(数字)9798331543891
ISBN:
(纸本)9798331543907
Land use and land cover (LULC) classification is a fundamental approach for landscape pattern mapping and monitoring of natural ecosystems and anthropogenic activities and is, therefore, an essential base for resource management to support sustainability. The land cover classes were classified in this study using multispectral data. The study utilizes Sentinel-2 data captured in February 2024, and a median composite image was generated using the Google Earth Engine (GEE) platform. Three different classification algorithms, random forest (RF), classification and regression tree (CART), and naive bayes (NB), were used to produce LULC maps. The algorithms are efficient in capturing different land cover patterns with spectral diversity. The performance with essential measures such as overall accuracy (OA) and Kappa coefficient (KC) has been evaluated to assess classification outcomes. Among these classifiers, the RF classifier with the maximum OA (96.91%) and KC (96.10%) exhibited optimal results and robustness for LULC classification. CART followed this, with OA of 95.88% and KC of 94.81%. NB achieved an OA of 85.57% and KC of 81.93%. It confirms the validity of the suggested method for obtaining LULC maps through Sentinel-2 data and ML algorithms, achieving maximum accuracy in semi-arid regions.
Cancer imaging examination is based mainly on doctors’ manual interpretation, which requires professional abilities, clinical experience, and attentiveness. However, as the volume of medical imaging data grows, radio...
Cancer imaging examination is based mainly on doctors’ manual interpretation, which requires professional abilities, clinical experience, and attentiveness. However, as the volume of medical imaging data grows, radiologists face increasing challenges. Artificial Intelligence (AI) detection of Digestive System Cancer (DSC) can provide a solution for automatic picture analysis and assist doctors in achieving high-precision intelligent cancer diagnosis. The primary purpose of this study is to explain the critical research methodologies for AI-based DSC detection, as well as to give meaningful learning and reference for relevant researchers. Meanwhile, it outlines the essential issues with these methodologies and provides better direction for future research. DSC automated categorization, recognition, and segmentation can be improved by utilizing machinelearning and deep learning detection techniques, which reduce internal image information that humans find difficult to uncover. Using AI to assist imaging surgeons in diagnosing DSC for multiple organs and high incidence can achieve rapid and effective cancer identification while saving doctors’ diagnosis time. These can be the foundation for better clinical diagnosis, therapeutic planning, and quantitative DSC evaluation.
AI-based student monitoring system provides an innovative approach that enables schools and institutions to monitor student attendance performance and behavior. By utilizing cutting-edge technologies like face recogni...
AI-based student monitoring system provides an innovative approach that enables schools and institutions to monitor student attendance performance and behavior. By utilizing cutting-edge technologies like face recognition, the system makes it easier for teachers to keep track of students' attendance while also giving administrators useful information. The technology automatically logs student attendance, doing away with the necessity for manual human attendance taking. Each student's attendance rate is determined, and the attendance analysis is made available. Administrators can more easily identify students with strong or poor attendance records thanks to the visualization module of the system, which presents attendance data as a graphical representation. Administrators can use this function to ensure that students attend classes on a regular basis. Additionally, the system notifies students and their parents through email when a particular level of attendance is not met, encouraging students to improve their attendance by taking appropriate action and fostering accountability. The AI-based student monitoring system is a useful tool for teachers and students. It helps to raise academic attainment by promoting an accountability and attendance culture in organizations and institutions. The system also provides administrators with useful data and minimizes the effort for educators.
The huge amount of data, complexity and changeability are difficulties that technological financial service platforms have always struggled to solve. Only by overcoming this difficulty can we carry out data analysis a...
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ISBN:
(数字)9798350376173
ISBN:
(纸本)9798350376180
The huge amount of data, complexity and changeability are difficulties that technological financial service platforms have always struggled to solve. Only by overcoming this difficulty can we carry out data analysis and decision support efficiently and accurately. This article solves this problem by introducing artificial intelligence algorithms. The article discusses the specific application methods of artificial intelligence algorithms in technology and financial data service platform systems. These methods include algorithm integration, model optimization, feature selection and extraction. Algorithm integration improves overall performance and stability by combining multiple models; model optimization improves the performance and generalization capabilities of the model by adjusting hyperparameters and optimization algorithms; feature selection and extraction reduces the complexity of the model and improves efficiency by selecting the most relevant and useful features. Experimental results show that the system response time of this algorithm is between 0.01-0.08 seconds, and it has achieved remarkable results in science and technology financial data analysis, prediction and decision support.
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