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Machine Learning Approaches for Efficient Traffic Flow in Smart Cities 3

Machine Learning Approaches for Efficient Traffic Flow in Sm...

3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Kulkarni, Aniket Anitha, P. Valluri, John Yesudas Sunena Rose, M.V. Hemavathi, U. Hussein, Omar M. Software Qa Manager Drata Inc San DiegoCA United States Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu Srivilliputhur India Faculty of Management Studies Bengaluru India Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Avadi Chennai India Al-Farahidi University Medical Technical College Medical Instrumentation Engineering Baghdad Iraq

ISBN: (纸本)9798350354379

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction;this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible. © 2024 IEEE.

关键词： Traffic congestion

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Blood Cell Classification and Counting in Hematology through Deep Learning

Blood Cell Classification and Counting in Hematology through...

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Sustainable Expert Systems (ICSES), International Conference on

作者： P. Ananthi K Nirmala Devi T. E. Ramya P. Shanmugapriya T. Aradhya Department of Computer Technology Kongu Engineering College Perundurai Erode Tamil Nadu Department of Computer Applications Madanapalle Institute of Technology & Science M.Sc Software Systems Kongu Engineering College Perundurai Erode Tamil Nadu

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Medical diagnosis is one of the areas that has been greatly influenced by the progress of computer vision technologies. This study presents a unique approach for the detection and counting of blood cells in hematology: You Only Look One algorithm version 7 (YOLOv7). YOLO is designed for real-time object detection, making it ideal for applications such as blood vessel detection where speed is critical. The architecture allows the model to process images faster than other recognition models, such as R-CNN or SSD, which is very important for situations that require fast results, such as analysis automatic blood at clinical sites. The purpose of the proposed method is to overcome the shortcomings of conventional methods and to accurately and quickly identify blood cells in mechanical images. The YOLOv7 model has been utilizedbecause it can detect objects in real-time with highprecision and speed. The methodology for bloodcell counting involves the use of YOLOv7 togenerate bounding boxes around each detectedblood cell in an image, where the count of these bounding boxes directly corresponds to the number of cells. This advancement in hematology not onlyimproves blood cell analysis efficiency but also expedites the process of diagnosing and planningtreatment in various medical situations. This paper details how YOLOv7 was modified and adjusted to meet the specific requirements of hematologicalimage analysis, including training models, preparing datasets, and evaluating performance. The study also addresses the potential impact of this technology on clinical workflows, highlightinghow it might help medical practitioners make decisions more quickly and intelligently. Hematology analysis systems including YOLOv7 are able to improve laboratory diagnostics and help patients by providing better care and results. This workconcludes by demonstrating the revolutionary potential of YOLOv7 in blood cell identificationand counting in hematology, paving the way for accurate and better

关键词： Analytical models Accuracy Computational modeling Cells (biology) Real-time systems Reliability Object recognition Medical diagnosis Blood Medical diagnostic imaging

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WSN-IoT Clustering for Secure Data Transmission in E-Health Sector using Green Computing Strategy 9

WSN-IoT Clustering for Secure Data Transmission in E-Health ...

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9th International Conference on Cyber and IT Service Management, CITSM 2021

作者： Srivastava, Anurag Singh, Abhishek Joseph, Susheel George Rajkumar, M. Borole, Yogini Dilip Singh, Hari Kumar Lakshmi Narain College of Technology and Science Indore India Software Developer IT Researcher NorthboroughMA United States Kristu Jyoti College of Management and Technology Department of Computer Applications Kerala India Department of Information Technology Tamilnadu India G.H. Raisoni Institute of Engineering and Technology Department of Electronics and Telecommunications Engineering Pune India M.J.P Rohilkhand University Department of Electronics and Communication Uttar Pradesh Bareilly India

ISBN: (纸本)9781665435482

Energy was one of the 21st century's most critical components. Electronics and computer technologies have been widely used nowadays to simplify work every day. The crisis and the constant lack of power have popularized and integrated Green Computing algorithms for multidimensional applications on a broad scale. The author attempts to defuse times of crisis related to medical crises using the Green Computing method. Wireless Sensor Networks (WSN) are made up of numerous sensor nodes (SN) that are linked to and play a key part in many applications and the Internet of Things (IoT). To establish a dynamic information exchange network, IoT links physical items like sensors. In a number of areas, the Internet of Things was used. Wearable medical sensors are used to monitor health indicators in patients in this crisis situation. This battery-powered medical sensor has a limited quantity of energy. This is an important hurdle for increased network lifetime. In order to address the current issue effectively, a new technique dubbed Jarvis Patrick regressive resource-efficient gaussian process clustering was suggested to prolong the lifetime of the network. This method is based on the green technology concept that requires the hour. IoT devices are first used in SN to detect and collect patient information. SN is categorized in several clusters using the Jarvis Patrick clustering method after the data collection process. Clustering by Jarvis Patrick is a graphical clustering technique used for dividing SN using the Gaussian process regression function. The regression function reviews the SN and performs the process of grouping based on anticipated bandwidth and energy. The Cluster Head (CH) is selected to enhance data transmission and decrease latency after the clustering process. The information is transmitted to the CH from the source node. Then CH locates the nearest CH using the flight-time method. After then, data are sent through a cluster head from the source node to th

关键词： Internet of things

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Accurate Anomaly Detection in Medical Images using Transfer Learning and Data Optimization: MRI and CT as Case Studies

Accurate Anomaly Detection in Medical Images using Transfer ...

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Neural Networks and Neurotechnologies (NeuroNT), II International Conference on

作者： Yasser Nizamli Anton Yu. Filatov Weaam Fadel Yulia A. Shichkina Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation Department of Computer Science Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation

ISBN: (数字)9798350363739

ISBN: (纸本)9798350363746

Today, medical imaging techniques are widely used to detect a variety of human conditions and diseases. To speed up the diagnostic process, systems are often automated using deep learning methods, which have been proven to yield outstanding results. However, these models may be limited to a specific domain, prone to overfitting, and difficult to update. In this paper, we present a transfer learning approach that achieves high accuracy for detecting anomalies in both MRI and CT images. Our system utilizes the ConvNeXt network, inspired by the Swin Transformer architecture, as a high-level feature extractor from processed and augmented medical images. The resulting data is then optimized using the Edited Nearest Neighbors algorithm, which selects the most effective observations and discards the rest. Finally, the data is passed to the K-Nearest Neighbors classifier to detect anomalies in numerically represented images. Our model achieves high performance for two different types of medical images and can easily be updated without the need for retraining.

关键词： Accuracy Magnetic resonance imaging Computed tomography Transfer learning Feature extraction Brain modeling Numerical models

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Smart Investigations into the Development of an Effective computer-Assisted Diagnosis System for CT Scan Brain Depictions

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SN computer Science 2023年第5期4卷 1-11页

作者： Attuluri, Sasidhar Bhupati, Ch Ramya, Lavu Tiwari, Amit Budaraju, Raja Rao Cotrina-Aliaga, Juan Carlos Software Developer Applications 671 E Royal Ln APT 1074 Irving 75039 TX United States Department of IoT K L Deemed to be University Vaddeswaram Guntur 522302 India Dr. Pinnamaneni Siddhartha Institute of Medical Sciences and Research Foundation Chinna Avutapalli Andhra Pradesh Vijayawada 521286 India Department of Computer Applications ABES Engineering College Ghaziabad India Oracle 3990 Scottfield Street Dublin 94568 CA United States Faculty of Human Medicine Universidad Privada San Juan Bautista Chincha Peru

Recent advances in medical image analysis on computers are anticipated to help radiologists and other healthcare workers with numerous diagnostic tasks including medical image interpretation. Accurate diagnosis and/or assessment of a condition in medical imaging relies on both the quality of the acquired images and the quality of the interpretation of those images. To accomplish this, large amounts of picture data and medical records must be combined. computer-aided diagnosis (CAD) systems have been developed in response to a lack of accuracy to boost the radiologist's productivity and precision in their interpretations. Given the importance of computerised tomography (CT) imaging in this field, we have made an effort in our research to examine CT scan brain images by applying a number of feature extraction and selection techniques, as well as classification techniques, to diagnose different types of brain disorders. Brain CT scans are analysed here and classified as either normal, benign tumours, or malignant tumours. Finding the best characteristics to use in a classification system is called “feature selection,” and it requires sifting through a vast amount of extracted features to locate the most relevant ones. To evaluate the efficacy of the implemented classifiers, we used measures of accuracy, specificity, sensitivity, positive prediction value, and negative prediction value. Traditional classifiers are also examined alongside the suggested method's results. The proposed decision support systems outperform the standard classifiers in terms of accuracy. An FSVM is generated by applying the RBF kernel function to this dataset. This method is compared to the support vector machine (SVM) and the multi-layer perceptron neural network (MLPNN) in terms of accuracy, sensitivity, and specificity of the classifiers they produce. Accuracy (96.25%), sensitivity (96.67%), and specificity (95.83%) are all significantly higher when using the proposed method as compared to t

关键词： CAD CT Fuzzy SVM NPV PPV SVM

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Enhanced real-Time performance using a secondary bus for cache write-backs

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International Journal of computers and applications 2015年第1期37卷 1-9页

作者： O'Farrell, John Baskiyar, Sanjeev Auburn University AuburnAL United States Science Applications International Corporation HuntsvilleAL United States Department of Computer Science and Software Engineering Auburn University AuburnAL United States

In spite of advances to improve cache efficiency, memory access bottlenecks still prevent processors from executing at full speed. This paper introduces the concept of using a secondary bus, connecting the level-2 cache to memory, for retiring cache write-backs. Simulations, using the sim-Alpha version of the SimpleScalar tool set, demonstrate the feasibility and possible advantages of such a secondary bus. Based on simulation results, the added secondary bus can decrease queuing delays on the system bus by 75-90% when sufficient write-backs are present. Such reduction in queuing delays offers superior temporal determinacy in real-Time environments. Real-Time and near real-Time embedded applications that depend on intense graphics processing and movement of large blocks of data, such as printer controllers and medical imaging, are prime candidates for applications of the secondary bus. The feasibility of implementing such a secondary bus using a serial line or wireless technology is also discussed. © 2015 Taylor and Francis.

关键词： Buses

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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment

Double Encryption Technique for Sharing and Storing the Imag...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： P. Anusha R. Maruthi D. Maghesh Kumar H. Parveen Begum Department of Software Engineering Periyar Maniammai Institute of Science and Technology Thanjavur Tamil Nadu India Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end–to end encryption for sharing and storing files in the cloud environment.

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uWave: Accelerometer-based personalized gesture recognition and its applications

uWave: Accelerometer-based personalized gesture recognition ...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Jiayang Liu Zhen Wang Lin Zhong Jehan Wickramasuriya Venu Vasudevan Department of Electrical Computer Engineering Rice University Houston TX USA Pervasive Platforms & Architectures Laboratory Applications & Software Research Center Motorola Research Laboratories USA

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.

关键词： Vocabulary Accelerometers Statistical analysis Application software Consumer electronics Pervasive computing Libraries Hardware computer vision Microcontrollers

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Elective participation in ad hoc networks based on energy consumption

Elective participation in ad hoc networks based on energy co...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： M.R. Pearlman Jing Deng B. Liang Z.J. Haas New York State Center for Advanced Technology in Computer Applications and Software Engineering (CASE) and the Center for Systems Assume Department of Electrical Engineering and Computer Science (EECS) Syracuse University Syracuse NY USA School of Electrical and Computer Engineering Cornell University Ithaca NY USA Department of Electrical and Computer Engineering University of Toronto Toronto ONT Canada

In ad hoc networks, each node utilizes its limited resources to carry out the collective operation of the network. It is not always in the best interests of the network's nodes to demand the continuous participation of all nodes in the network operations. We propose an energy dependent participation (EDP) scheme, where a node periodically re-evaluates its participation in the network based on the residual energy in its battery. More importantly, a node gives special consideration to supporting the communication needs of its active network applications and preventing further network partitioning. EDP's localized partition checking algorithm is particularly well suited for the zone routing protocol, where the link-state information is proactively maintained within each node's local zone and routes to faraway nodes are reactively obtained via global queries. Through simulations, we evaluate the impact of our proposed scheme on battery life and network connectivity. Our results suggest that the EDP scheme can increase the usable lifetime of a battery-constraint ad hoc network by over 50%.

关键词： Intelligent networks Energy consumption Ad hoc networks Batteries Routing protocols Power engineering and energy Radio transceivers Mobile communication Broadcasting computer networks

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A basic study on power storage capacitor systems

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ELECTRICAL engineering IN JAPAN 1996年第3期116卷 40-51页

作者： Okamura, M Okamura Laboratory Inc. [?]B.S. degree in 1959 from the Department of Electrical Engineering Waseda University. He joined Nippon Atomic Industry Group Co. the same year and studied at Argonne National Laboratories (U.S.) in 1964–65. He left the company and established the Okamura Laboratory Inc. in 1987. He is engaged in R&D of radiation measurements electronic circuits computer applications and software. Member of the Japan Nuclear Society and the Electrical Society.

Storage batteries or pumped hydraulic power stations have been used to store electric energy. However, there are problems with storage batteries, e.g., long charging time, limited cycle life, low Coulomb efficiency, and inaccurate residual power meters. Because of these problems, practical power applications have not been achieved. To solve these problems, a complex system of capacitors and electronic circuits called the ECS (Energy Capacitor System) is proposed. The capacitor part of the ECS is a group of electric double-layer capacitors of increased energy density. The circuits part is built around switching regulators. Although the capacitors alone are not capable of delivering stable output, the accompanying circuits compensate for various characteristics throughout the charge and discharge cycle. This combination of capacitors and electronic circuits increases the energy density by about 16 times that of the conventional electric double-layer capacitors, close to the level of lead-acid batteries but with lower output resistance and better voltage stability. The ECS inherently features short charging time, very long cycle life, and good Coulomb efficiency. The cells are completely sealed and made from materials which are not harmful to the environment.

关键词： electrical power storage device secondary battery electric double-layer capacitor switching regulator energy density power density

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