Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Khaled Tarmissi³, Ayman Yafoz⁴, Amal S. Mehanna⁵, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3303-3319, 2023, DOI:10.32604/csse.2023.034823 - 03 April 2023

Abstract The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the… More >

Saman Riaz¹, Ali Arshad², Shahab S. Band^3,*, Amir Mosavi⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2301-2312, 2023, DOI:10.32604/cmc.2023.028333 - 06 February 2023

Abstract Personality distinguishes individuals’ patterns of feeling, thinking, and behaving. Predicting personality from small video series is an exciting research area in computer vision. The majority of the existing research concludes preliminary results to get immense knowledge from visual and Audio (sound) modality. To overcome the deficiency, we proposed the Deep Bimodal Fusion (DBF) approach to predict five traits of personality-agreeableness, extraversion, openness, conscientiousness and neuroticism. In the proposed framework, regarding visual modality, the modified convolution neural networks (CNN), more specifically Descriptor Aggregator Model (DAN) are used to attain significant visual modality. The proposed model extracts More >

Mesfer Alduhayyem^1,*, Mrim M. Alnfiai^2,3, Nabil Almalki⁴, Fahd N. Al-Wesabi⁵, Anwer Mustafa Hilal⁶, Manar Ahmed Hamza⁶

Computer Systems Science and Engineering, Vol.46, No.1, pp. 475-489, 2023, DOI:10.32604/csse.2023.034067 - 20 January 2023

Abstract Visual impairment is one of the major problems among people of all age groups across the globe. Visually Impaired Persons (VIPs) require help from others to carry out their day-to-day tasks. Since they experience several problems in their daily lives, technical intervention can help them resolve the challenges. In this background, an automatic object detection tool is the need of the hour to empower VIPs with safe navigation. The recent advances in the Internet of Things (IoT) and Deep Learning (DL) techniques make it possible. The current study proposes IoT-assisted Transient Search Optimization with a… More >

Nga Nguyen Thi Thanh, Quang H. Nguyen^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 491-504, 2023, DOI:10.32604/csse.2023.032107 - 20 January 2023

Abstract Nowadays, web systems and servers are constantly at great risk from cyberattacks. This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory (LSTM) network in combination with the ensemble learning technique. First, the binary classification module was used to detect the current abnormal flow. Then, the abnormal flows were fed into the multilayer classification module to identify the specific type of flow. In this research, a deep learning bidirectional LSTM model, in combination with the convolutional neural network and attention technique, was deployed to identify a specific attack. More >

Donghun Wang, Jonghyun Lee, Minchan Kim, Insoo Lee^*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2025-2040, 2023, DOI:10.32604/iasc.2023.034749 - 05 January 2023

Abstract Lithium-ion batteries are commonly used in electric vehicles, mobile phones, and laptops. These batteries demonstrate several advantages, such as environmental friendliness, high energy density, and long life. However, battery overcharging and overdischarging may occur if the batteries are not monitored continuously. Overcharging causes fire and explosion casualties, and overdischarging causes a reduction in the battery capacity and life. In addition, the internal resistance of such batteries varies depending on their external temperature, electrolyte, cathode material, and other factors; the capacity of the batteries decreases with temperature. In this study, we develop a method for estimating… More >

S. Kavi Priya^*, K. Pon Karthika

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160 - 05 January 2023

Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and… More >

Y. C. A. Padmanabha Reddy¹, Shyam Sunder Reddy Kasireddy², Nageswara Rao Sirisala³, Ramu Kuchipudi⁴, Purnachand Kollapudi^5,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6389-6409, 2023, DOI:10.32604/cmc.2023.034072 - 28 December 2022

Abstract The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages. The digital document needs to be evaluated physically through the Cross-Language Text Summarization (CLTS) involved in the disparate and generation of the source documents. Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents. The digital documents need to be processed with the contextual semantic data with the decoding scheme. This paper presented a multilingual cross-language processing of the documents with the abstractive and summarising of the documents. The proposed model is More >

Anitha Thangasamy^*, Bose Sundan, Logeswari Govindaraj

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2553-2567, 2023, DOI:10.32604/csse.2023.032078 - 21 December 2022

Abstract The recent development of cloud computing offers various services on demand for organization and individual users, such as storage, shared computing space, networking, etc. Although Cloud Computing provides various advantages for users, it remains vulnerable to many types of attacks that attract cyber criminals. Distributed Denial of Service (DDoS) is the most common type of attack on cloud computing. Consequently, Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks. Since DDoS attacks have become increasingly widespread, it becomes difficult for some DDoS attack methods based on individual… More >

El-Sayed M. El-kenawy¹, Abdelaziz A. Abdelhamid^2,3, Fadwa Alrowais^4,*, Mostafa Abotaleb⁵, Abdelhameed Ibrahim⁶, Doaa Sami Khafaga⁴

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2885-2899, 2023, DOI:10.32604/csse.2023.034206 - 21 December 2022

Abstract Rainfall plays a significant role in managing the water level in the reservoir. The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir. Many individuals, especially those in the agricultural sector, rely on rain forecasts. Forecasting rainfall is challenging because of the changing nature of the weather. The area of Jimma in southwest Oromia, Ethiopia is the subject of this research, which aims to develop a rainfall forecasting model. To estimate Jimma’s daily rainfall, we propose a novel approach based on optimizing the parameters of long… More >

Karthick Panneerselvam^1,2,*, K. Mahesh¹, V. L. Helen Josephine³, A. Ranjith Kumar²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1047-1061, 2023, DOI:10.32604/csse.2023.030513 - 03 November 2022

Abstract Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing high-quality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different… More >