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Designing of Financial Time Series Forecasting Model Using Stochastic Algorithm Based Extreme Learning Machine 2nd

Designing of Financial Time Series Forecasting Model Using S...

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2nd international conference on Intelligent and Cloud computing, ICICC 2021

作者： Hota, Sarbeswara Mohanty, Arup Kumar Mishra, Debahuti Satapathy, Pranati Jena, Biswaranjan Department of Computer Application Siksha O Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Information Technology Siksha O Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha O Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Applications Utkal University Odisha Bhubaneswar India Tata Consultancy Services Odisha Bhubaneswar India

ISBN: (纸本)9789811698729

The financial forecasting research domain includes many directions, out of which forecasting of currency exchange rate attracts many researchers. The researchers have used various neural networks for the development of forecasting models due to the nonlinear and chaotic nature of exchange rate data. Different stochastic optimization algorithms have been hybridized with neural networks for further optimization of the network parameters. In this paper, Extreme Learning Machine (ELM) technique is applied to predict the exchange rate and the recently developed Sine Cosine Algorithm (SCA) is hybridized with this technique. The proposed ELM-SCA model is studied with respect to its prediction performance on a sample dataset for one-day ahead and seven-day ahead prediction. The experimental results demonstrate that the suggested ELM-SCA model produces better results than the ELM model in forecasting of SGD to INR. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Machine Learning Approach for Detection of Speech Emotions for RAVDESS Audio dataset

Machine Learning Approach for Detection of Speech Emotions f...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Yogesh R. Rochlani Anjali B. Raut Department of Computer Science and Engineering H. V. P. M’s College of Engineering & Technology Amravati Maharashtra India

The most effective technique to convey one's thoughts and actions to another is through the use of emotion. Emotion recognition from one's own voice is the most urgently needed technology right now. Emotions are attached to everything that a person owns. Every customer's feelings might provide light on what they really need from the customer service professional. Since human communication relies heavily on the ability to read one another's emotions, speech emotion recognition is crucial for understanding another person's inner workings. The time and frequency-based acoustic characteristics integrated with machine learning classifiers that can categorize emotions to enhance the performance of the proposed intelligent system. This research makes use of the audio records available in the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS). Emotion is gleaned from speech via temporal and spectral characteristics. Three machine learning classifiers, the SVM, DT, and RF classifiers, are then trained on the retrieved data. The RF classifier achieved 88.78% higher accuracy rate than the state-of-the-art approaches.

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Detection and Control System for Automotive Products Applications by Artificial Vision Using Deep Learning 2nd

Detection and Control System for Automotive Products Applica...

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2nd international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： El Wahabi, Abdelhamid Baraka, Ibrahim Hadj Hamdoune, Salaheddine El Mokhtari, Karim LIST UAE Faculty of Science and Technology Tangier Morocco Data Science Laboratory Ryerson University Toronto Canada

ISBN: (纸本)9783030366704

Object recognition is among the most important subjects in computer vision, it has undergone a huge evolution during these last decades, but in the last years artificial intelligence has seen the appearance of Deep Learning, and through the efforts of researchers, Deep Learning is having great success, its applications have touched on different fields, such as robotics, industry, automotive … In this context, in collaboration with an Automotive components manufacturer and FST faculty of sciences and technologies of tangier (UAE University) have taken the initiative to develop and implement an object recognition and inspection system for automotive products application that requires a good accuracy of image classification using the Deep Learning which is the purpose of this paper. This report summarizes the work done within this Company concerning the development and implementation of a system aims to realize an artificial vision system for the inspection of automotive products based mainly on the "Deep Learning" method. Before, during and after manufacturing, many products in automotive sector (electronic components, …) are subjected to a visual inspection phase, in this context we have replace this phase by our vision system so that the piece will be accepted or not accepted, as well as to act to parameters (for example: winding shape, welding quality …) in the case of not accepted. The convolutional neural networks have become advanced methods for classification and detection of objects over the last five years. At present, they work better than conventional image processing method set, on many image classification data sets. Most of these datasets are based on the notion of concrete classes. In this paper, we present a new set of image classification data as well as object detection data, which should be easy for humans to solve, but its variations are difficult for CNN. The classification performance of popular CNN architectures is evaluated on this dataset and

关键词： Automotive industry

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An Energy Efficient Routing for Emergency Rescue in IoT-Based WSN 4th

An Energy Efficient Routing for Emergency Rescue in IoT-Base...

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4th international conference on Futuristic Trends in Networks and computing technologies, FTNCT 2021

作者： Shreyas, J. Shilpa, S. Udayaprasad, P.K. Srinidhi, N.N. Dilip Kumar, S.M. Department of Computer Science and Engineering UVCE Bengaluru India

ISBN: (纸本)9789811950360

The rapid expansion demand of sensor devices have attained significant attention for wireless sensor network (WSN) and demands many Internet of Things (IoT) applications for real-time usage. The routing technique must capable of efficiently delivering packets to sink to achieve large-scale IoT application’s reliability. How to guarantee real-time emergency reaction capabilities during data transmission is now a difficult topic for research under the demand of large IoT network scale expansion. Existing routing scheme fails to deliver data and has higher routing latency due to transmissions failures and lower energy nodes. In this article, an energy efficient emergency response routing scheme (EEER) is proposed. The proposed EEER delivers the critical data efficiently under the emergency with lower latency. Extended transmission range covers wide range of intermediate nodes for data communication. This scheme selects nodes with higher residual energy for forwarding emergency data towards sink. To ensure this, the output of the proposed device is validated with an comprehensive simulation test. Results tested using NS2 simulation method indicate an improvement of 5% decrease in energy usage, 6% increase in packet delivery ratio and 7% decrease in end to end delay. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Automated Recognition of Wild Animal Species in Camera Trap Images Using Deep Learning Models

Automated Recognition of Wild Animal Species in Camera Trap ...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Rajasekaran Thangaraj Sivaramakrishnan Rajendar Sanjith M Rithick Saran K Sudev Sasikumar Chandhru L Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

Wild animal monitoring is one of the essential factors in balancing the ecosystem in protecting the animal habitat. Camera traps are used to capture the images, which play a vital role in the automatic monitoring of wildlife owing to their effectiveness in collecting wildlife data. The images captured from the camera traps are large volumes in nature, and they are inexpensive. However, the manual processing of such a large number of images is a time-consuming, expensive, and challenging task. Such a kind of process provides major hurdles to ecologists in monitoring wildlife. This paper evaluates deep learning models such as DeneseNet169, InceptionResNetV2, ResNetV2-50, and Xception for the automatic identification and recognition of individual animals in different patterns. The effectiveness of the models is measured using precision, recall, and F1 score indicators. Finally, the experimental findings show that the InceptionResNetV2 model outperforms the other models with an accuracy of 99%.

关键词： Deep learning Image recognition Biological system modeling Wildlife Ecosystems Transfer learning Cameras

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A Systematic Comparison on Prevailing Intrusion Detection Models 23rd

A Systematic Comparison on Prevailing Intrusion Detection M...

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23rd international conference on Parallel and Distributed computing, Applications, and technologies, PDCAT 2022

作者： Liu, Jianxuan Xue, Haotian Wang, Junfeng Hong, Shuo Fu, Hao Dib, Omar Department of Computer Science Wenzhou-Kean University Wenzhou China

ISBN: (纸本)9783031299261

Modern vehicles have become connected via On-Board Units (OBUs) involving many complex embedded and networked devices with steadily increasing processing and communication resources. Those devices exchange information through intra-vehicle networks to implement various functionalities and perform actions. Vehicles’ connectivity has also been extended to external networks through vehicle-to-everything technologies, enabling communications with other vehicles, infrastructures, and smart devices. In parallel to the significant increase in quality of service, the connectivity of modern vehicles raises their vulnerabilities to cyber-attacks targeting both intra-vehicle and external networks. To secure communications in vehicular networks, there has been a consistent effort to develop intrusion detection systems based on machine learning techniques to detect and ultimately react to malicious cyber-attacks. In this article, we study several machine learning algorithms, deep learning models, and hyper-parameter optimization techniques to detect vulnerability attacks on vehicular networks. Experimental results on well-known data sets such as CICIDS2017, NSL-KDD, IoTID20, KDDCup99, and UNSW-NB15 indicate that learning-based algorithms can detect various types of intrusion detection attacks with significant performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicles

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Enhanced and Effective Ensemble Model for Breast Cancer Detection and Prediction in Modern Medical System in Machine Learning 1st

Enhanced and Effective Ensemble Model for Breast Cancer Dete...

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1st international conference on Deep sciences for computing and Communications, IconDeepCom 2022

作者： Saravanan, T.R. Mahariba, A. Jackulin Priya, S. Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9783031276217

Humanoid fitness, is the wide-ranging state-run of corporal, societal, and intellectual well-being and not just the absenteeism of infection, sickness, or disability, is as vibrant a source as H2O, foodstuff, or liveliness. Human has undergone phenomenal transformations during last six decades. This has affected health a very large extent. As technology has enhanced and cancer has increased in recent years. Cancer is most widely detected in both men and women. But the female are widely affected by cancer. In Existing system for breast cancer there are many repositories available with cancer data, many online sources, many hospital information etc. As all are separate entity predictions of cancer is very less. All the medical resources available are underutilized. There are many Artificial Intelligence and Machine Learning approaches Logistic Regression, KNeighbors, Random Forest and Decision Tree Classifier, are available for prediction and the effectiveness needs to be improved. In proposed work breast cancer dataset is taken as input and all the best possibilities of the predicted output using various classifications algorithms are combined and a ensemble model is generated. The proposed ensemble model achieves high accuracy, precision and recall for breast cancer detection and prediction, effectiveness outperforms all existing classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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A Comparative Study of Artificial Intelligence based Vehicle Classification Algorithms used to Provide Smart Mobility

A Comparative Study of Artificial Intelligence based Vehicle...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Prarthana V Sushma Narayan Hegde Sushmitha T P Savithramma R M R Sumathi Computer Science & Engineering Siddaganga Institute of Technology Tumakuru India

Due to the rising number of vehicles on the road and the limited resources supplied by current infrastructures, traffic problems are becoming more prevalent. Signalized junctions are the prime locations of congestions where commuters need to wait for long time in front of the signals to get their turn to move. This leads to several issues including wastage of time, additional fuel consumption, and green gas emissions. Optimization of traffic signals based on traffic behavior is widely explored topic in which vehicle detection and classification is one of the leading areas of research of Intelligent Transportation System (ITS). Among the technologies Artificial Intelligence (AI) has emerged as a giant in which vehicle classification has developed as a prominent subject of study because of its usefulness in several applications such as traffic control and surveillance, security systems, traffic congestion, avoidance, and accident prevention. Numerous algorithms and techniques for classifying vehicles have been proposed and implemented so far globally which mimics human intelligence. The goal of the paper is to familiarize the reader with the existing AI-based vehicle classification algorithms and to give a comparison of various vehicle detection and classification methods. The existing vehicle classification algorithms are summarized under two categories based on input type i.e., image or video. When the technologies such as AI, image processing, data mining and sensors are combined, the ITS can observe the road, initiate autonomous vehicle detection and thereby control traffic on road efficiently.

关键词： Support vector machines Roads Image processing Surveillance Urban areas Transportation Traffic control

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Supporting Green Neuromorphic computing: Machine Learning Guided Microfabrication for Resistive Random Access Memory

Supporting Green Neuromorphic Computing: Machine Learning Gu...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Abdi Yamil Vicenciodelmoral Md Mehedi Hasan Tanim Feng Zhao Xinghui Zhao School of Engineering and Computer Science Washington State University Vancouver Vancouver WA

The growing popularity of big data and machine learning applications call for a more powerful and energy-efficient way to execute deep learning workflows. Neuromorphic chips provide a potential solution, as they attempt to mimic the neuronal architectures in human brain and show great potentials in reducing energy consumption in the order of magnitude and also improve the computational performance. However, the fabrication process for neuromophic chips is costly and currently based on trial-and-error, which adds complexity to the design process. In this paper, we address this challenge by designing and developing machine learning guided microfabrication process for Resistive Random Access Memory (RRAM), which is a key device in neuromorphic chips. Experimental results show that our approach is effective in terms of predicting the performance of RRAM devices fabricated under various process conditions.

关键词： Performance evaluation Deep learning Energy consumption Neuromorphic engineering Microfabrication Resistive RAM Big data

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Towards an Efficient DDoS Attack Detection in SDN: An Approach with CNN-GRU Fusion

Towards an Efficient DDoS Attack Detection in SDN: An Approa...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Konda Srikar Goud Giduturi Srinivasa Rao Department of Computer Science and Engineering GITAM School of Technology GITAM University Visakhapatnam AP India BVRIT HYDERABAD College of Engineering for Women Hyderabad Telangana India

The emergence of Distributed Denial of Service (DDoS) attacks presents a significant risk to Software-Defined Networking (SDN) services. This study focuses on developing an effective method for detecting DDoS attacks in SDN by leveraging Deep Learning (DL) techniques, precisely combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). This research proposes a novel approach that utilizes CNN-GRU-based DL models to detect DDoS attacks in SDN accurately. This approach learns intricate patterns and temporal dependencies associated with DDoS attacks by analyzing network traffic data and detecting such threats. In order to assess the effectiveness of our model, we conduct extensive experiments using diverse datasets comprising both normal network traffic and various types of DDoS attacks. We present a comparative analysis against traditional ML algorithms and existing methodologies. To assess the effectiveness of our approach, we used various performance metrics. The experimental results prove that the CNN-GRU-based DL model outperforms other techniques, exhibiting superior accuracy and overall performance in detecting DDoS attacks in SDN.

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