Published: May 26, 2022
Converted to Gold OA:
DOI: 10.4018/IJDAI.301212
Volume 14
Oreoluwa Carolyn Tinubu, Adesina Simon Sodiya, Olusegun Ayodeji Ojesanmi, Emmanuel Oyeyemi Adeleke, Ahmad Alfawwaz Timehin
Distributed Denial of Service (DDoS) attacks are the foremost security concerns on the Internet. DDoS attacks and a similar occurrence called Flash Event (FE) signify anomalies in the normal network...
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Distributed Denial of Service (DDoS) attacks are the foremost security concerns on the Internet. DDoS attacks and a similar occurrence called Flash Event (FE) signify anomalies in the normal network traffic, requiring intelligent interventions. This study presents the design and implementation of an intelligent model for the detection of application-layer DDoS attacks and the prevention of service degradations during FE. A Multi-Layer Perceptron (MLP) classifier was used for detecting DDoS attacks on application servers. The FE management system consists of asynchronous processing of requests on a First-In, First-Out (FIFO) basis. A demo application was set up wherein HTTP flood attack was launched and a Flash Event was simulated. The experimental results clearly show that the MLP classifier in comparison with other machine learning classifiers performs best in terms of speed and accuracy. Also, the evaluation of the FE management system shows a great reduction in service degradation. This reflects that the designed model is capable of averting service unavailability on the web.
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Tinubu, Oreoluwa Carolyn, et al. "An Intelligent Model for DDoS Attack Detection and Flash Event Management." IJDAI vol.14, no.1 2022: pp.1-15. http://doi.org/10.4018/IJDAI.301212
APA
Tinubu, O. C., Sodiya, A. S., Ojesanmi, O. A., Adeleke, E. O., & Timehin, A. A. (2022). An Intelligent Model for DDoS Attack Detection and Flash Event Management. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-15. http://doi.org/10.4018/IJDAI.301212
Chicago
Tinubu, Oreoluwa Carolyn, et al. "An Intelligent Model for DDoS Attack Detection and Flash Event Management," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-15. http://doi.org/10.4018/IJDAI.301212
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Published: May 27, 2022
Converted to Gold OA:
DOI: 10.4018/IJDAI.301213
Volume 14
Ishak H. A Meddah, Fatiha Guerroudji, Nour Elhouda Remil
The processing of big data across different axes is becoming more and more difficult and the introduction of the Hadoop MapReduce framework seems to be a solution to this problem. With this...
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The processing of big data across different axes is becoming more and more difficult and the introduction of the Hadoop MapReduce framework seems to be a solution to this problem. With this framework, large amounts of data can be analyzed and processed. It does this by distributing computing tasks between a group of virtual servers operating in the cloud or a large group of devices. The mining process forms an important bridge between data mining and business process analysis. Its techniques make it possible to extract information from event reports. The extraction process generally consists of two phases: identification or discovery and innovation or education. Our first task is to extract small patterns from the log effects. These templates represent the implementation of the tracking from a business process report file. In this step we use the available technologies. Patterns are represented by finite state automation or regular expressions. And the final model is a combination of just two different styles.
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Meddah, Ishak H. A, et al. "Distributed Business Process Discovery in Cloud Clusters." IJDAI vol.14, no.1 2022: pp.1-18. http://doi.org/10.4018/IJDAI.301213
APA
Meddah, I. H., Guerroudji, F., & Remil, N. E. (2022). Distributed Business Process Discovery in Cloud Clusters. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-18. http://doi.org/10.4018/IJDAI.301213
Chicago
Meddah, Ishak H. A, Fatiha Guerroudji, and Nour Elhouda Remil. "Distributed Business Process Discovery in Cloud Clusters," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-18. http://doi.org/10.4018/IJDAI.301213
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Published: Jul 21, 2022
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DOI: 10.4018/IJDAI.304896
Volume 14
Khadidja Bouchenga, Bouabdellah Kechar, Vincent Rodin
The paper presents a complex simulation system for demonstrating the evacuation process in a building, whereby people attempt to escape from a dangerous scenario. It is novel in that it integrates a...
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The paper presents a complex simulation system for demonstrating the evacuation process in a building, whereby people attempt to escape from a dangerous scenario. It is novel in that it integrates a range of different models: agent-based model, social force model, and psychological behaviour with emotions and norms. The method uses the communication network based on the message queuing telemetry transport protocol that assists to gather information from the environment. The paths are modified using feelings and rule-based expert system. The authors conduct some simulations and conclude with recommendations for management of safer environments.
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Bouchenga, Khadidja, et al. "Smart and Dynamic Indoor Evacuation System (SDIES)." IJDAI vol.14, no.1 2022: pp.1-23. http://doi.org/10.4018/IJDAI.304896
APA
Bouchenga, K., Kechar, B., & Rodin, V. (2022). Smart and Dynamic Indoor Evacuation System (SDIES). International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-23. http://doi.org/10.4018/IJDAI.304896
Chicago
Bouchenga, Khadidja, Bouabdellah Kechar, and Vincent Rodin. "Smart and Dynamic Indoor Evacuation System (SDIES)," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-23. http://doi.org/10.4018/IJDAI.304896
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Published: Sep 30, 2022
Converted to Gold OA:
DOI: 10.4018/IJDAI.309743
Volume 14
Upendra Kumar
Sentiment analysis is a sub-field of natural language processing (NLP). In sentiment analysis the sentiment behind the piece of data is tried to know, this data can be a review of a product by a...
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Sentiment analysis is a sub-field of natural language processing (NLP). In sentiment analysis the sentiment behind the piece of data is tried to know, this data can be a review of a product by a customer or a comment on some social media platform. Analysing large amounts of data is still an easy task for small retail websites and business owners. Deep learning (DL) has made a great revolution in the field of speech and image recognition. Mature deep learning neural network i.e. convolution neural network (CNN) has completely changed the field of NLP. This paper proposed a high accuracy, efficient, scalable, reliable and secure solution to cater all the needs of business owners and institutes for sentiment analysis with DL model, a browser based GUI interface for easy accessibility to all the non-technical folks and a dashboard having graphical representations of their results. The proposed sentiment analysis based model has achieved 93.55% accuracy which has outperformed other models.
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Add to Your Personal Library: Article Published: Oct 7, 2022
Converted to Gold OA:
DOI: 10.4018/IJDAI.311063
Volume 14
Nikhil Chaturvedi, Jigyasu Dubey
Named entity recognition is an important factor that has a direct and significant impact on the quality of neural sequence labelling. It entails choosing encoding input data to create grammatical...
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Named entity recognition is an important factor that has a direct and significant impact on the quality of neural sequence labelling. It entails choosing encoding input data to create grammatical and semantic representation vectors. The main goal of this research is to provide a hybrid neural network model for a specific sequence labelling task such as named entity recognition. Three subnetworks are used in this hybrid model to ensure that information at the character, capitalization levels, and word-level contextual representation is fully utilized. The authors used different samples for training and development sets on the CoNLL-2003 dataset to show that the model could compare its performance to that of other state-of-the-art models.
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Chaturvedi, Nikhil, and Jigyasu Dubey. "Hybrid Model for Named Entity Recognition." IJDAI vol.14, no.1 2022: pp.1-12. http://doi.org/10.4018/IJDAI.311063
APA
Chaturvedi, N. & Dubey, J. (2022). Hybrid Model for Named Entity Recognition. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-12. http://doi.org/10.4018/IJDAI.311063
Chicago
Chaturvedi, Nikhil, and Jigyasu Dubey. "Hybrid Model for Named Entity Recognition," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-12. http://doi.org/10.4018/IJDAI.311063
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Published: Feb 24, 2022
Converted to Gold OA:
DOI: 10.4018/IJDAI.297110
Volume 14
Binay Kumar Pandey, Digvijay Pandey, Ashi Agarwal
A deep neural network is used to develop a covert communication and textual data extraction strategy based on steganography and picture compression in such work. The original input textual image and...
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A deep neural network is used to develop a covert communication and textual data extraction strategy based on steganography and picture compression in such work. The original input textual image and cover image are both pre-processed before the covert text-based pictures are separated and implanted into the least significant bit of the cover object picture element using spatial steganography. Following that, stego-images are compressed and transformed(by using Leh Transformation) to provide a higher-quality image while also saving storage space at the sender's end. After then, the stego-image will be transmitted to the receiver over a communication link. At the receiver's end, steganography and compression are then reversed. This work contains a plethora of issues, making it an intriguing subject to pursue. The most crucial component of this task is choosing the right steganography and picture compression technology. The proposed technology, which combines picture steganography with compression and transformation, delivers higher peak signal-to-noise efficiency.
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Pandey, Binay Kumar, et al. "Encrypted Information Transmission by Enhanced Steganography and Image Transformation." IJDAI vol.14, no.1 2022: pp.1-14. http://doi.org/10.4018/IJDAI.297110
APA
Pandey, B. K., Pandey, D., & Agarwal, A. (2022). Encrypted Information Transmission by Enhanced Steganography and Image Transformation. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-14. http://doi.org/10.4018/IJDAI.297110
Chicago
Pandey, Binay Kumar, Digvijay Pandey, and Ashi Agarwal. "Encrypted Information Transmission by Enhanced Steganography and Image Transformation," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-14. http://doi.org/10.4018/IJDAI.297110
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Published: Jan 21, 2022
Converted to Gold OA:
DOI: 10.4018/IJDAI.291084
Volume 14
Digvijay Pandey, Subodh Wairya
The emerging 5G telecommunication technology uses novel aspects to fulfill the challenges of high data rate, ultra-low latency, broad bandwidth with the best user experience for text detection in...
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The emerging 5G telecommunication technology uses novel aspects to fulfill the challenges of high data rate, ultra-low latency, broad bandwidth with the best user experience for text detection in sign board and thereafter transmission of identified information to the vehicles. This is performed on the images which are amorphous in nature or containing scenarios which are random or that cannot be determined. Detecting and transmission of textsover 5G wireless network from the unstructured images aids in many of the additional applications like Optical Character Recognition (OCR) and 5G technolog such as an eMBB, mMTC, and URLLC for quality of service and customer satisfaction.This approach can be used to alert a driver about any road sign even from a captured video by using 5G wireless network irrespective of the weathercondition or any obstacle which may make sign boards difficult to see for drivers. The algorithm uses Maximally Stable Extremal Regions (MSER) feature detector. The algorithm contains several steps which are briefly described in the paper.
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Pandey, Digvijay, and Subodh Wairya. "A Novel Algorithm to Detect and Transmit Human-Directed Signboard Image Text to Vehicle Using 5G-Enabled Wireless Networks." IJDAI vol.14, no.1 2022: pp.1-11. http://doi.org/10.4018/IJDAI.291084
APA
Pandey, D. & Wairya, S. (2022). A Novel Algorithm to Detect and Transmit Human-Directed Signboard Image Text to Vehicle Using 5G-Enabled Wireless Networks. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-11. http://doi.org/10.4018/IJDAI.291084
Chicago
Pandey, Digvijay, and Subodh Wairya. "A Novel Algorithm to Detect and Transmit Human-Directed Signboard Image Text to Vehicle Using 5G-Enabled Wireless Networks," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-11. http://doi.org/10.4018/IJDAI.291084
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Published: Feb 11, 2022
Converted to Gold OA:
DOI: 10.4018/IJDAI.291085
Volume 14
Mohamed Merabet, Ali Kourtiche
One of the major environmental challenges is forest fires, each year millions of hectares of forest are destroyed throughout the world, resulting in economic and ecological damages, as well as the...
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One of the major environmental challenges is forest fires, each year millions of hectares of forest are destroyed throughout the world, resulting in economic and ecological damages, as well as the loss of human life. Therefore, predicting forest fires is of great importance for governments; However, there is still limited study on this topic in Algeria. In this paper, we present an application of artificial neural networks to predict forest fires in embedded devices. We used meteorological data obtained from wireless sensor networks. In the experimentation, nine machine learning model are compared. The findings from this study make several contributions to the current literature. First, our model is suitable for embedded and real-time training and prediction. Moreover, it should provide better performances and accurate predictions against other models.
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Merabet, Mohamed, and Ali Kourtiche. "Embedded ANN-Based Forest Fire Prediction Case Study of Algeria." IJDAI vol.14, no.1 2022: pp.1-18. http://doi.org/10.4018/IJDAI.291085
APA
Merabet, M. & Kourtiche, A. (2022). Embedded ANN-Based Forest Fire Prediction Case Study of Algeria. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-18. http://doi.org/10.4018/IJDAI.291085
Chicago
Merabet, Mohamed, and Ali Kourtiche. "Embedded ANN-Based Forest Fire Prediction Case Study of Algeria," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-18. http://doi.org/10.4018/IJDAI.291085
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Published: Feb 11, 2022
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DOI: 10.4018/IJDAI.291086
Volume 14
Upendra Kumar, Pawan Kumar Tiwari, Tejasvi Mishra, Lalita Jaiswar, Safiya Ali
India as a country has 17.7% of the world’s population with the limited availability of land resource which is about only 2.4% of the world’s land. Being a developing nation and such huge population...
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India as a country has 17.7% of the world’s population with the limited availability of land resource which is about only 2.4% of the world’s land. Being a developing nation and such huge population to accommodate, a number of problems can be seen on a daily basis such as high traffic congestion and unmanaged traffic on the roads. Irritating rush, wastage of time and fuel, are being severe hindrance to make the transportation comfortable. As a country, due to availability of limited lands, the only option is to manage the traffic smartly. Hitherto, a number of attempts have been made in this regard, still the statically managed traffic lights can be seen at the junction of roads. So in this work, it was tried to give an easy, but implementable method to manage traffic lights effectively. A hybrid approach based enhanced Convolution Neural Network model was used for the classification and have given the comparison with other model based technique i.e. Support Vector Machine. Our proposed enhanced model produced 91.01% accuracy and it is able to outperform the existing model.
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Kumar, Upendra, et al. "Significant Enhancement of Classification Efficiency for Automated Traffic Management System." IJDAI vol.14, no.1 2022: pp.1-16. http://doi.org/10.4018/IJDAI.291086
APA
Kumar, U., Tiwari, P. K., Mishra, T., Jaiswar, L., & Ali, S. (2022). Significant Enhancement of Classification Efficiency for Automated Traffic Management System. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-16. http://doi.org/10.4018/IJDAI.291086
Chicago
Kumar, Upendra, et al. "Significant Enhancement of Classification Efficiency for Automated Traffic Management System," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-16. http://doi.org/10.4018/IJDAI.291086
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Published: Mar 18, 2022
Converted to Gold OA:
DOI: 10.4018/IJDAI.296389
Volume 14
Stephen Opoku Oppong, Benjamin Ghansah, Evans Baidoo, Wilson Osafo Apeanti, Daniel Danso Essel
Complex computational problems are occurrences in our daily lives that needs to be analysed effectively in order to make meaningful and informed decision. This study performs empirical analysis...
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Complex computational problems are occurrences in our daily lives that needs to be analysed effectively in order to make meaningful and informed decision. This study performs empirical analysis into the performance of six optimisation algorithms based on swarm intelligence on nine well known stochastic and global optimisation problems, with the aim of identifying a technique that returns an optimum output on some selected benchmark techniques. Extensive experiments show that, Multi-Swarm and Pigeon inspired optimisation algorithm outperformed Particle Swarm, Firefly and Evolutionary optimizations in both convergence speed and global solution. The algorithms adopted in this paper gives an indication of which algorithmic solution presents optimal results for a problem in terms of quality of performance, precision and efficiency.
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Oppong, Stephen Opoku, et al. "Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem." IJDAI vol.14, no.1 2022: pp.1-26. http://doi.org/10.4018/IJDAI.296389
APA
Oppong, S. O., Ghansah, B., Baidoo, E., Apeanti, W. O., & Essel, D. D. (2022). Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-26. http://doi.org/10.4018/IJDAI.296389
Chicago
Oppong, Stephen Opoku, et al. "Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-26. http://doi.org/10.4018/IJDAI.296389
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