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Optimized Sensor Network Design for Real-Time Environmental Monitoring

Optimized Sensor Network Design for Real-Time Environmental ...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Syed Harron Gaurav Shukla Harshita Kaushik Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

A sensor community is a set of interconnected sensors that can degree, monitor, and report phenomena within the environment. Sensor networks have many packages consisting of actual-time environmental tracking. A good way to effectively use these networks for monitoring, an optimized network layout needs to be employed to ensure that the network can meet the necessities of its operational demands. This paper proposes a fashionable-motive sensor network design framework for real-time environmental tracking. The proposed framework gives a step-by-step technique to optimize a sensor network design for the project at hand. It begins with a version of the surroundings that the sensor community will screen, which is used to generate sensor node configurations and topologies. These configurations and topologies are then used to lay out the sensor community such that its deployment is optimized for the surroundings. The design framework makes use of a community optimization set of rules to attain a configuration that maximizes the network's performance and is capable of meeting its operational requirements. The set of rules operates with the aid of looking through the distance of all feasible sensor community topologies, looking for those that have the first-rate value-benefit ratio. The algorithm can be further progressed by way of the usage of the device, gaining knowledge of strategies, which makes it capable of managing extra complex eventualities.

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Utilizing Deep Learning in Pre-Clinical and Clinical Diagnosis of Peripheral Neuropathies

Utilizing Deep Learning in Pre-Clinical and Clinical Diagnos...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Akhilendra Pratap Singh Vikas Kumar Kharbas Prerna Mahajan Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

Peripheral neuropathies are a group of problems that affect the peripheral frightened machine, often leading to a spread of symptoms and impairments. Diagnosis of these situations is frequently imprecise and time-consuming, leading to delays in remedy. Deep studying techniques provide the capacity to automate and enhance the analysis of peripheral neuropathies. Through deep gaining knowledge, clinicians can obtain more excellent correct diagnoses based on enter from MRI pics, photographs of nerve biopsies, or different imaging facts. Moreover, deep studying may be used to create characteristic vectors from different medical features and EEGs that may facilitate the popularity of signs and symptoms and diagnoses extra speedy and more appropriate than traditional methods. This paper explores the capacity of deep learning to accurately diagnose and stratify peripheral neuropathies in pre-scientific and medical settings. It gives a top-level view of contemporary studies on deep getting-to-know strategies for spotting signs and symptoms and diagnosing peripheral neuropathies. Similarly, the paper outlines capacity applications and blessings of deep studying for diagnosing peripheral neuropathies and discusses areas of destiny studies. The software of deep mastering in the pre-medical and clinical prognosis of peripheral neuropathies has been increasingly studied in latest years. Deep mastering fashions, consisting of convolutional neural networks, have shown promising effects in analyzing electromyography (EMG) alerts for the early detection, quantification, and class of peripheral neuropathies, complementing and, in a few cases surpassing conventional diagnostic methods. Moreover, those methods have been advanced as they should be detected and differentiated between myopathic and neuropathic abnormalities and muscle activity. Those algorithms can also distinguish between the diverse kinds of peripheral neuropathies and are potentially useful for early detection and reme

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Prediction of Reliability of Data in Underwater Communication Network Using Fuzzy Logic

Prediction of Reliability of Data in Underwater Communicatio...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Surendra Yadav Gobi N Rakesh Kumar Yadav Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

This paper uses fuzzy logic to propose a reliability prediction set of rules for the underwater communication community. The set of rules uses fixed fuzzy rules to expect communication reliability among nodes in an underwater environment. Those fuzzy rules are derived from a reliability model based on the characteristics of an ordinary underwater conversation system, such as physical and environmental elements. The algorithm is then examined using accurate statistics from an underwater communication test. The effects imply that this approach is promising in predicting the reliability of the community. It has also been discovered that reliability prediction accuracy can be advanced if the bushy guidelines include extra factors, the sign channel's records rate, and the machine's performance parameters. This study offers a practical approach to improving the performance and reliability of underwater communique networks.

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Presentation Analysis of Reasoning Radio-Based Routing Protocols in Wireless Mesh Networks

Presentation Analysis of Reasoning Radio-Based Routing Proto...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ritesh Kumar Vikas Kumar Kharbas Deepak Mehta Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

Reasoning radio-based total routing protocols present a promising technique for permitting powerful conversation in wi-fi mesh networks. By using the spectrum sensing capabilities of Reasoning radio, routers can intelligently pick out and pick out spectrum assets to ahead records, thereby allowing for improved network throughput and reliability. In this paper, we carry out a comprehensive Presentation evaluation of routing protocols primarily based on Reasoning radio in wi-fi mesh networks. Mainly, we Presentation metrics including packet shipping fee, latency, jitter, and energy Presentation. We examine various routing protocols from an empirical and theoretical angle and speak about the strengths and weaknesses of every technique. Subsequently, our consequences provide insights into the capability advantages and tradeoffs of Reasoning radio-based routing protocols in wireless mesh networks, offering practical steerage to designers and engineers within the subject.

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Correlating Time Series Analysis with Radiotherapy for the Early Detection of Hodkin Lymphoma

Correlating Time Series Analysis with Radiotherapy for the E...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： M N Varun Somanna Rakesh Kumar Yadav Inzimam Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

This study aimed to quantify the temporal capabilities of radiotherapy facts from sufferers with Hodgkin lymphoma (HL) and correlate those functions with time collection evaluation (TSA) measures to perceive potential temporal relationships with early analysis of HL. Excessive-decision 3-dimensional computed tomography (CT) scans of HL sufferers have been analyzed using batch-clever TSA methods to derive the temporal collection that characterizes the radiotherapy facts. In the end, a set of temporal features from the measured temporal series have been extracted and correlated with HL's medical and laboratory analysis. The outcomes showed that the derived temporal capabilities had undoubtedly correlated with the analysis, indicating a capability for using radiotherapy information to diagnose HL early. The mean and general deviation of the temporal collection in the radiotherapy curves from HL sufferers has been notably one-of-a-kind from wholesome patients. Furthermore, the temporal series of radiotherapy statistics from HL sufferers showed notably better peaks in the temporal capabilities than healthful patients, indicating an elevated stage of radiotherapy for HL sufferers. The effects of this observation recommend that TSA may be a viable tool for early analysis of HL, presenting a more comprehensive view of the temporal traits of radiotherapy data for HL sufferers.

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A Robust Ensemble Learning Approach for Medical Image Segmentation with Transfer Learning

A Robust Ensemble Learning Approach for Medical Image Segmen...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ritu Shree Kavitha R Shweta Singh Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

This paper proposes a robust ensemble mastering approach for clinical picture segmentation. The proposed technique combines a convolution neural community (CNN) with a switch studying-based totally ensemble model. The CNN is pre-educated with a dataset containing clinical photo modalities, particularly Magnetic Resonance Imaging (MRI) and computed tomography (CT)., a weight-averaged ensemble model is acquired between the two scientific photograph modalities. This version is an initial way to the clinical image segmentation problem. Eventually, the ensemble model is great-tuned with additional imaging statistics from an unmarried modality to improve the segmentation accuracy. The proposed technique is evaluated on a range of datasets, and the effects display that it achieves competitive performance compared to the country of the artwork. Moreover, the experiments reveal that combining the transfer gaining knowledge of-based totally ensemble mastering approach with extra imaging information enhances the accuracy of medical picture segmentation.

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Hierarchical Representation Learning for Improved Diagnosis of Prostate Cancer on MRI Scans

Hierarchical Representation Learning for Improved Diagnosis ...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Prabhu A Megha Pandeya Harshita Kaushik Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

It focuses on using hierarchical illustration mastering (HRL) for the progressed prognosis of most prostate cancers on MRI scans. HRL is a gadget getting-to-know technique using a hierarchy of function vectors to encode record sets, allowing extra complicated non-linear styles to be recognized and applied. In this examination, HRL is compared to a fashionable convolution neural network (CNN) classifier for the challenge of prostate cancer analysis on MRI scans. The effects show that HRL outperforms the CNN classifier, providing a higher accuracy fee and universal predictive performance. Furthermore, HRL results in fewer fake positives and extra correct type accuracy. Its improved overall performance suggests that utilizing HRL in medical decision-making can offer a more correct, practical analysis of most prostate cancers.

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An Enhanced Optimization of Automated Detection of Cardiac Abnormalities Using Deep Learning

An Enhanced Optimization of Automated Detection of Cardiac A...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ananta Ojha Dhyan Chandra Yadav Manish Kumar Goyal Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

Deep gaining knowledge of is a place of artificial intelligence that is becoming increasingly famous inside the scientific area. This paper provides an improved optimization of computerized detection of cardiac abnormalities the use of deep learning. particularly, the authors recommend using a convolutional neural community (CNN) to stumble on abnormalities from ECG records. They use an ensemble of models to further improve accuracy and reduce false superb quotes. moreover, they apply transfer learning techniques to higher generalize the mastering from the EEG facts. The authors take a look at their optimized set of rules on two datasets of ECG recordings and file an normal accuracy of 88.9%. This demonstrates the potential for deep getting to know techniques to end up an increasing number of reliable and sturdy for detecting cardiac abnormalities. The authors also talk the feasible directions of future studies and the potentials of deep learning for clinical safety and diagnostics in phrases of fee and efficiency.

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Investigating the Integrity of Irrigation Systems Through Hyper Spectral Image Analysis

Investigating the Integrity of Irrigation Systems Through Hy...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Zahid Ahmed Preethi D Dhananjay Kumar Yadav Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

This research aims to use hyperspectral picture analysis to research the integrity of irrigation systems, hoping to reduce power and water utilization. Traditional methods for measuring irrigation integrity are timeingesting, luxurious, and unreliable. With the software of the advanced imaging era, this project seeks to create a detailed model of an irrigation system so one can discover and diagnose troubles quickly and appropriately. A huge-statistics technique utilizing spectral statistics mixed with professional gadget wisdom gives the potential irrigation structures control. analysis might be conducted using a combination of airborne imaging and computational strategies along with satellite tv for pc and in-area evaluation. Those techniques will assist in offering a value-effective monitoring machine for identifying and diagnosing irrigation troubles and detecting modifications that can suggest water misuse or inefficient layout. Using this era, we intend to enhance irrigation structures' overall performance and integrity.

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Maximizing the Accuracy of Time Series Analysis for the Early Detection of Rhabdomyosarcomas

Maximizing the Accuracy of Time Series Analysis for the Earl...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ritu Shree Prabhu A Anupam Kumar Gautam Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

Rhabdomyosarcomas are a rare malignant tender tissue tumor that generally gives in younger children and teenagers. Early prognosis and remedy are essential for successful outcomes. Time collection evaluation is a valuable tool for recognizing styles and trends in medical facts, mainly for rare situations, which include Rhabdomyosarcomas. It has consequently been increasingly employed to detect early signs and symptoms of the ailment. On this look, we are conscious of investigating and optimizing techniques for time collection analysis. It is an excellent way to enhance its application and accuracy in identifying early symptoms and signs of rhabdomyosarcoma. We examine present strategies and suggest improvement techniques, along with function extraction and system mastering techniques. We further inspect the effectiveness of our strategies by conducting experiments on a dataset installed from scientific facts and literature of rhabdomyosarcoma instances. Those experiments show promising effects, indicating that our proposed strategies can considerably increase the accuracy and sensitivity of time series evaluation for the early detection of rhabdomyosarcoma and cause higher prognoses for affected sufferers. The focal point of this study is to maximize the accuracy of time series analysis for the early detection of rhabdomyosarcoma. Time collection analysis includes: • The gathering of temporal information from multiple sources. • The assessment of these records. • The interpretation of correlations between the facts points. This study aims to utilize these techniques to discover diffused adjustments in affected person information so that you can perceive the onset of the disorder in advance than would be possible with traditional techniques. The examination will expand algorithms to systematically and accurately procedure the temporal statistics and discover adjustments indicative of Rhabdomyosarcomas. In addition, the look will rent gadget learning to boost the dete

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