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  • Open Access

    ARTICLE

    A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

    Sara A. Alameen*, Areej M. Alhothali

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 895-912, 2023, DOI:10.32604/csse.2023.024643 - 01 June 2022

    Abstract Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D convolutional neural network (3D-CNN) and long short-term memory (LSTM). The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features… More >

  • Open Access

    ARTICLE

    Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

    G. Ravikumar1, K. Venkatachalam2, Mohammed A. AlZain3, Mehedi Masud4, Mohamed Abouhawwash5,6,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 945-959, 2023, DOI:10.32604/csse.2023.024605 - 01 June 2022

    Abstract Sleep apnea syndrome (SAS) is a breathing disorder while a person is asleep. The traditional method for examining SAS is Polysomnography (PSG). The standard procedure of PSG requires complete overnight observation in a laboratory. PSG typically provides accurate results, but it is expensive and time consuming. However, for people with Sleep apnea (SA), available beds and laboratories are limited. Resultantly, it may produce inaccurate diagnosis. Thus, this paper proposes the Internet of Medical Things (IoMT) framework with a machine learning concept of fully connected neural network (FCNN) with k-nearest neighbor (k-NN) classifier. This paper describes More >

  • Open Access

    ARTICLE

    Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

    V. R. Kavitha1, K. Nimala2, A. Beno3, K. C. Ramya4, Seifedine Kadry5, Byeong-Gwon Kang6, Yunyoung Nam7,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 579-593, 2023, DOI:10.32604/csse.2023.024553 - 01 June 2022

    Abstract Image Captioning is an emergent topic of research in the domain of artificial intelligence (AI). It utilizes an integration of Computer Vision (CV) and Natural Language Processing (NLP) for generating the image descriptions. It finds use in several application areas namely recommendation in editing applications, utilization in virtual assistance, etc. The development of NLP and deep learning (DL) models find useful to derive a bridge among the visual details and textual semantics. In this view, this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning (OHHO-DLIC) technique. The OHHO-DLIC technique involves… More >

  • Open Access

    ARTICLE

    Proof of Activity Protocol for IoMT Data Security

    R. Rajadevi1, K. Venkatachalam2, Mehedi Masud3, Mohammed A. AlZain4, Mohamed Abouhawwash5,6,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 339-350, 2023, DOI:10.32604/csse.2023.024537 - 01 June 2022

    Abstract The Internet of Medical Things (IoMT) is an online device that senses and transmits medical data from users to physicians within a time interval. In, recent years, IoMT has rapidly grown in the medical field to provide healthcare services without physical appearance. With the use of sensors, IoMT applications are used in healthcare management. In such applications, one of the most important factors is data security, given that its transmission over the network may cause obtrusion. For data security in IoMT systems, blockchain is used due to its numerous blocks for secure data storage. In… More >

  • Open Access

    ARTICLE

    Development of Algorithm for Person Re-Identification Using Extended Openface Method

    S. Michael Dinesh1,*, A. R. Kavitha2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 545-561, 2023, DOI:10.32604/csse.2023.024450 - 01 June 2022

    Abstract Deep learning has risen in popularity as a face recognition technology in recent years. Facenet, a deep convolutional neural network (DCNN) developed by Google, recognizes faces with 128 bytes per face. It also claims to have achieved 99.96% on the reputed Labelled Faces in the Wild (LFW) dataset. However, the accuracy and validation rate of Facenet drops down eventually, there is a gradual decrease in the resolution of the images. This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.… More >

  • Open Access

    ARTICLE

    An Ophthalmic Evaluation of Central Serous Chorioretinopathy

    L. K. Shoba1,*, P. Mohan Kumar2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 613-628, 2023, DOI:10.32604/csse.2023.024449 - 01 June 2022

    Abstract Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter (BADWUMF). The classifier used here is to More >

  • Open Access

    ARTICLE

    Design of Hierarchical Classifier to Improve Speech Emotion Recognition

    P. Vasuki*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 19-33, 2023, DOI:10.32604/csse.2023.024441 - 01 June 2022

    Abstract Automatic Speech Emotion Recognition (SER) is used to recognize emotion from speech automatically. Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influenced by the variations in gender, age, the cultural and acoustical background of the speaker. The acoustical resemblance between emotional expressions further increases the complexity of recognition. Many recent research works are concentrated to address these effects individually. Instead of addressing every influencing attribute individually, we would like to design a system, which reduces the effect that arises on any factor. We propose a two-level Hierarchical… More >

  • Open Access

    ARTICLE

    Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

    N. Dharini1,*, Jeevaa Katiravan2, S. M. Udhaya Sankar3

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 249-264, 2023, DOI:10.32604/csse.2023.024419 - 01 June 2022

    Abstract This work utilizes a statistical approach of Principal Component Analysis (PCA) towards the detection of Methane (CH4)-Carbon Monoxide (CO) Poisoning occurring in coal mines, forest fires, drainage systems etc. where the CH4 and CO emissions are very high in closed buildings or confined spaces during oxidation processes. Both methane and carbon monoxide are highly toxic, colorless and odorless gases. Both of the gases have their own toxic levels to be detected. But during their combined presence, the toxicity of the either one goes unidentified may be due to their low levels which may lead to an… More >

  • Open Access

    ARTICLE

    Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

    R. Bhaskaran1,*, S. Saravanan1, M. Kavitha2, C. Jeyalakshmi3, Seifedine Kadry4, Hafiz Tayyab Rauf5, Reem Alkhammash6

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 235-247, 2023, DOI:10.32604/csse.2023.024399 - 01 June 2022

    Abstract Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data More >

  • Open Access

    ARTICLE

    An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

    R. Prabhu1,*, S. Rajesh2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 281-295, 2023, DOI:10.32604/csse.2023.024339 - 01 June 2022

    Abstract Cloud computing distributes task-parallel among the various resources. Applications with self-service supported and on-demand service have rapid growth. For these applications, cloud computing allocates the resources dynamically via the internet according to user requirements. Proper resource allocation is vital for fulfilling user requirements. In contrast, improper resource allocations result to load imbalance, which leads to severe service issues. The cloud resources implement internet-connected devices using the protocols for storing, communicating, and computations. The extensive needs and lack of optimal resource allocating scheme make cloud computing more complex. This paper proposes an NMDS (Network Manager based… More >

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