Rice Disease Recognition Using Transfer Learning Xception Convolutional Neural Network

Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua

doi:10.52436/1.jutif.2023.4.6.1529

Cited by 10 publications

(11 citation statements)

References 16 publications

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“…(Eboka & Ojugo, 2020). This agrees with (Muslikh et al, 2023;Okonta et al, 2013Okonta et al, , 2014Oyemade et al, 2016). Bako et al (2020) proposed a new mode to detect worms by monitoring the rate of their outgoing connections.…”

Section: Honeypots In Malware Detectionsupporting

confidence: 77%

Pilot Study on Web Server HoneyPot Integration Using Injection Approach for Malware Intrusion Detection

Malasowe,

Aghware,

Edim

2024

AIMS Research Journal

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The digital world is rapidly coming together as well as transforming a lot of our valued data onto digital forms. Its consequent dissemination eased via the advent of the Internet has also encountered many attacks due to predictable responses from many users – leading up to social engineering and exploits on trust-level of users. The use of deception is now playing a very prominent role in enhancing data security. Several approaches abound to discourage and redirect challenges (via the use of honeypot), and to detect such intrusive activities (via an intrusion detection systems). These have been successfully used to minimize security breaches. We explore a deep learning deception-based honeypot to minimize breaches by adversaries. Used on web servers, it is equipped with identification capabilities as the system learns and defends a user system against intrusive actions. Our confusion matrix shows model has sensitivity of 0.81, specificity 0.08, and prediction accuracy of 0.991 with an improvement rate of 0.39 for data that were not originally used to train. . Keywords: Web Server, HoneyPot, Integratio, Injection Approach, Malware Intrusion Detection CISDI Journal Reference Format Malasowe B., Aghware, F. & Edim, B.E. (2024): Pilot Study on Web Server HoneyPot Integration Using Injection Approach for Malware Intrusion Detection. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 1, Pp 13-.28. dx.doi.org/10.22624/AIMS/CISDI/V15N1P2. Available online at www.isteams.net/cisdijournal

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Section: Honeypots In Malware Detectionsupporting

confidence: 77%

Pilot Study on Web Server HoneyPot Integration Using Injection Approach for Malware Intrusion Detection

Malasowe,

Aghware,

Edim

2024

AIMS Research Journal

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“…This yields the model in Eq. ( 3), and also represents our formalization of the market basket problem [63], which consists of the following, and agrees with [64]:…”

Section: A Problem Formulationmentioning

confidence: 86%

Handling Transactional Data Features via Associative Rule Mining for Mobile Online Shopping Platforms

Akazue,

Okofu,

Ojugo

et al. 2024

IJACSA

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Transactional data processing is often a reflection of a consumer's buying behavior. The relational records if properly mined, helps business managers and owners to improve their sales volume. Transaction datasets are often rippled with the inherent challenges in their manipulation, storage and handling due to their infinite length, evolution of product features, evolution in product concept, and oftentimes, a complete drift away from product feat. The previous studies' inability to resolve many of these challenges as abovementioned, alongside the assumptions that transactional datasets are presumed to be stationary when using the association ruleshave been found to also often hinder their performance. As it deprives the decision support system of the needed flexibility and robust adaptiveness to manage the dynamics of concept drift that characterizes transaction data. Our study proposes an associative rule mining model using four consumer theories with RapidMiner and Hadoop Tableau analytic tools to handle and manage such large data. The dataset was retrieved from Roban Store Asaba and consists of 556,000 transactional records. The model is a 6-layered framework and yields its best result with a 0.1 value for both the confidence and support level(s) at 94% accuracy, 87% sensitivity, 32% specificity, and a 20-second convergence and processing time.

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“…To harness the inherent benefits of these models, the conflicts must be resolved -so we can exploit historic data as well as explore the domain space to yield an optimal solution. We must select appropriate feats to devoid the model of poor generalization, overtraining and overfit (Akazue et al, 2023;Muslikh et al, 2023;Oladele et al, 2024).…”

Section: Findings and Discussionmentioning

confidence: 99%

Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection

Odiakaose,

Emordi,

Ejeh

et al. 2024

AIMS Research Journal

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Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier. Keywords: Hybrid Genetic Algorithm, Trained Bayesian Ensemble, Short Messages Spam Detection Odiakaose, C. Emordi, F. Ejeh, P., Ashioba, N., Odeh, C., Attoh, O. & Azaka, M. (2024): Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 37-52. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P4

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Rice Disease Recognition Using Transfer Learning Xception Convolutional Neural Network

Cited by 10 publications

References 16 publications

Pilot Study on Web Server HoneyPot Integration Using Injection Approach for Malware Intrusion Detection

Pilot Study on Web Server HoneyPot Integration Using Injection Approach for Malware Intrusion Detection

Handling Transactional Data Features via Associative Rule Mining for Mobile Online Shopping Platforms

Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection

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