This article analyzes the power consumption of nodes in ad hoc IEEE 802.11 networks. The main objective of this analysis is to discover theoretic restrictions for the gains obtained in node lifetime through different ...
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New cooperative spectrum sensing (CSS), it encounters better spectrum efficiency, has come into existence as a new strategy. In this study, techniques based on machine learning (ML) work together with CSS to improve u...
New cooperative spectrum sensing (CSS), it encounters better spectrum efficiency, has come into existence as a new strategy. In this study, techniques based on machine learning (ML) work together with CSS to improve user understanding; this is only possible when ML algorithms predict channel states. Additionally, many popular regressions machine learning models such as linear regression, nonlinear regression, generalized linear model, regression tree, and support vector machine regression (SVM) ensembles are discussed along with statistical analysis. This article assumes that 20 main users have different power, controls the maximum power of 20 units, and fixes the threshold of 4 units for all four cases, and analyses the results. In this respect, we came to the conclusion that a best fit line should be used for obtaining characteristics such that linear regression could be used. To further enhance regression and achieve the best results, other changes were also employed.
Access to reliable modes of transportation is essential. Postal delivery to multi-million-dollar freight transports, practically everything is linked to its connections. Wheeled motor vehicles and manned aeroplanes ar...
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The transportation sector is a key component of tackling global climate change, which facilitates sustainable development. An effective and environmentally friendly way to decarbonize the transportation industry is th...
The transportation sector is a key component of tackling global climate change, which facilitates sustainable development. An effective and environmentally friendly way to decarbonize the transportation industry is through electric vehicles (EVs). This paper proposes EV charging station, using hybrid converter technique along with Maximum Power Point Tracking (MPPT) approach. In this work charging of EV is accomplished by Photovoltaic (PV) system. The voltage from PV system is boosted with the support of Cuk-Zeta converter. Furthermore, Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT approach is established to control the converter along with maximum extraction of PV voltage. Finally, the voltage across DC link is transferred to EV battery from where, it is offered for Vehicles for charging. The exhaustive construction is examined and validated using MATLAB, proving that the proposed setup results in improved performance with 95.32% converter efficiency respectively.
A cognitive radio network is an intelligent system that can detect the presence of unused spectrum space without affecting the primary user. The CRN is responsible for managing the allocation of channels, which leads ...
A cognitive radio network is an intelligent system that can detect the presence of unused spectrum space without affecting the primary user. The CRN is responsible for managing the allocation of channels, which leads to the scarcity of spectrum. This issue should be addressed in order to ensure that the network can continue to provide long-term and profitable service to its users. The issue of spectrum handoff is considered a major issue that needs to be resolved in order to improve the efficiency of the network. One of the most common factors that can affect the network's performance is the power consumption and communication delay. This paper proposes a method that can detect the availability of channels during the handover. The accuracy and network latency of various ML algorithms are evaluated through resampling techniques. The Nave Bayes Classifier and KNN algorithms performed better than their benchmarks. For a total of 500 and 100 users, respectively, the networks experienced a network latency of 10.91 and 13.08 seconds.
This research work involves data access and control of Unmanned Ground Vehicles (UGVs) which is designed for health monitoring of paddy crops as an exception from traditional monitoring system. The proposed scheme of ...
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This research work involves data access and control of Unmanned Ground Vehicles (UGVs) which is designed for health monitoring of paddy crops as an exception from traditional monitoring system. The proposed scheme of autonomous navigation of drones is planned to be carried out as per the six milestones for disease detection and control in paddy crops. Drones serve as Unmanned Robotic Vehicles (URV) capable of performing desired tasks in unstructured, uncertain and potentially hostile environments and are remotely-operated without human intervention. URVs function completely as autonomous entities in diversified environments. Current UGVs adhere to different levels of automaticity. Typically the vehicle follows high level waypoints spaced for few hundred meters of distance to provide monitoring of agricultural fields and early detection of the various diseases that may occur in the paddy fields in a polyhouse. To increase the vehicle's abilities, tracking efficiency, obstacle avoidance, path planning or lead and follow up augmented control with Fuzzy Logic Controller (FLC) is incorporated. The distributed autonomous system for information gathering related to the paddy crops in polyhouse is enabled using different sensors, which is a data-intensive task. To increase the robustness of the system, fuzzy controllers are proposed to control the navigation of the proposed UGV in "All terrain conditions". They are needed to offer problem specific heuristic control knowledge for the Inference Engine Design which occurs due to imprecision and uncertainty of the sensor readings. It also requires low computation time which favours the polyhouse situations. The navigation of UGV and the FLC action will in turn depend on "All terrain traversability" and "dead zone" monitoring. The proposed UGV model is capable of measuring the parameters associated with the paddy crops inside a polyhouse. The various diseases in paddy crops are False Smut (FS), Sheath Blight (SB), Rice Blast (RB
Named entity recognition is a natural language processing technique that effectively recognizes and categorizes named entities in a document. The named entity recognition helps to bring out dynamic information about a...
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Named entity recognition is a natural language processing technique that effectively recognizes and categorizes named entities in a document. The named entity recognition helps to bring out dynamic information about a document or gather critical data to store in a database. Deep learning helps to develop over time, while NLP examines the structure and standards of language and produces an automated system that can discern meaning from text. Mining the essential entities in a text helps identify related data, which is vital when functioning with enormous datasets. The proposed system has a feature that can retrieve and identify sensitive data such as PAN numbers, bank account numbers, and Aadhar numbers from unstructured text data.. The proposed model is designed using Hybrid CNN and it attains 95% F1-Score, 97.5% precision, and 98.3% recall.
In an era where communication is key, the gap in accessible tools for those with hearing impairments or speech disabilities is significant. These individuals often face obstacles in education and social interaction du...
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ISBN:
(数字)9798350389449
ISBN:
(纸本)9798350389456
In an era where communication is key, the gap in accessible tools for those with hearing impairments or speech disabilities is significant. These individuals often face obstacles in education and social interaction due to a heavy reliance on spoken language and a lack of sign language resources. The Interactive Sign Language Learning System (ISLLS) addresses this gap by providing an innovative platform for learning sign language, enhanced with voice output to assist individuals with speech disabilities. This feature allows for auditory feedback alongside visual sign learning, enriching the educational experience. The ISLLS employs advanced technologies like computer vision and deep learning to facilitate sign recognition and text-to-sign conversion. With the new voice output, it further aids those with speech impairments, expanding its inclusivity. This system offers a comprehensive learning tool that caters to a diverse user base, enabling people with speech difficulties to engage more fully with the world. The ISLLS is a significant step towards a more inclusive society, offering a user-friendly platform that not only improves the learning of sign language but also empowers people with speech disabilities to connect and thrive, representing progress in both technology and social inclusivity.
In this study, we proposed a moved-view analysis, a method for obtaining the Soret coefficient ST by analyzing the interference fringe change during field-of-view movement in the steady state. This analysis was design...
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Alzheimer's disease is a pressing healthcare concern worldwide, highlighting the importance of early and accurate detection. Deep learning models, particularly those utilizing transfer learning, have shown promise...
Alzheimer's disease is a pressing healthcare concern worldwide, highlighting the importance of early and accurate detection. Deep learning models, particularly those utilizing transfer learning, have shown promise for AD classification. This research study explores the effectiveness of transfer learning in training models based on deep learning for the classification of Alzheimer's condition. We trained several transfer models using DenseNet121, InceptionV3, Xception, and ResNet101. Based on performance evaluation, InceptionV3 was selected as the base model, outperforming in comparison to the accuracy of the other models. To enhance the accuracy of the InceptionV3 base model, we added capacity, tuned hyperparameters using Keras tuner, and utilized data augmentation techniques. The final model was trained on the Kaggle Alzheimer's Dataset, consisting of 4 classes of images, and achieving an AUC value of 87%. Our research demonstrates that transfer learning and other data augmentation approaches are useful in improving the accuracy of deep learning models for the categorization of Alzheimer's condition. This research has practical implications for improving public health outcomes, facilitating timely intervention and effective treatment. It contributes to the development of more accurate diagnostic tools for AD and can help address the challenges associated with this disease.
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