Academia.eduAcademia.edu

Outline

Title
Abstract
Figures
Introduction
Literature Review
Conventional Methods for Food Pathogen Detection
Results
Discussion-Conclusions
References

Lagocephalus_Sceleratus.pdf

Profile image of Kostantinos DemertzisKostantinos Demertzis
https://doi.org/10.1007/978-3-319-23983-5_9
visibility

…

description

15 pages

link

1 file

Abstract

Climate change combined with the increase of extreme weather phenomena, has significantly influenced marine ecosystems, resulting in water overheating, increase of sea level and rising of the acidity of surface waters. The potential impacts in the biodiversity of sensitive ecosystems (such as Mediterranean sea) are obvious. Many organisms are under extinction, whereas other dangerous invasive species are multiplied and thus they are destroying the ecological equilibrium. This research paper presents the development of a sophisticated, fast and accurate Food Pathogen Detection (FPD) system, which uses the biologically inspired Artificial Intelligence algorithm of Extreme Learning Machines. The aim is the automated identification and control of the extremely dangerous for human health invasive fish species "Lagocephalus Sceleratus". The matching is achieved through extensive comparisons of protein and DNA sequences, known also as DNA barcodes following an ensemble learning approach.

Figures (6)
Fig. 1. The structure of the proposed algorithm
Fig. 1. The structure of the proposed algorithm
Graph 1. Original Lagocephalus Family Dataset
Graph 1. Original Lagocephalus Family Dataset
Graph 2. Lagocephalus Family Dataset after application of SMOTE
Graph 2. Lagocephalus Family Dataset after application of SMOTE
Table 1. Accuracy (ACC) and Performance Matrices (PM) comparison between ELM, MLP, SVM and RF algorithms in usual_fish_dataset
Table 1. Accuracy (ACC) and Performance Matrices (PM) comparison between ELM, MLP, SVM and RF algorithms in usual_fish_dataset
Table 2. Accuracy (ACC) and Performance Matrices (PM) of ELM after resampling by Bootstrap with replacement method Due to the fact that the number of the used features was too high for all three data- sets (lago_family_dataset 582 independent parameters, usual_fish_dataset 519 and lago_fasta_dataset 558) several feature selection attempts were done for the reduction of the training time and for the enhancement of the generalization in order to avoid overfitting. The Particle Swarm Optimization was used to search for the optimal fea- ture subset. The assessment of each subset was done by considering the value of each subset which is based on the contribution and the degree of redundancy of each cha-
Table 2. Accuracy (ACC) and Performance Matrices (PM) of ELM after resampling by Bootstrap with replacement method Due to the fact that the number of the used features was too high for all three data- sets (lago_family_dataset 582 independent parameters, usual_fish_dataset 519 and lago_fasta_dataset 558) several feature selection attempts were done for the reduction of the training time and for the enhancement of the generalization in order to avoid overfitting. The Particle Swarm Optimization was used to search for the optimal fea- ture subset. The assessment of each subset was done by considering the value of each subset which is based on the contribution and the degree of redundancy of each cha-
Table 3. Accuracy (ACC) and Performance Matrices (PM) of ELM after feature selection by PSO search method The final conclusion is that the proposed method has proven to be reliable and effi- cient and has outperformed at least for these datasets the other approaches.
Table 3. Accuracy (ACC) and Performance Matrices (PM) of ELM after feature selection by PSO search method The final conclusion is that the proposed method has proven to be reliable and effi- cient and has outperformed at least for these datasets the other approaches.

Related papers

A Machine Learning Approach for Early Detection of Fish Diseases by Analyzing Water Quality
Mim Saha

Trends in Sciences, 2021

Early detection of fish diseases and identifying the underlying causes are crucial for farmers to take necessary steps to mitigate the potential outbreak and thus to avert financial losses with apparent negative implications to the national economy. Typically, fish diseases are caused by viruses and bacteria; according to biochemical studies, the presence of certain bacteria and viruses may affect the level of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N in water, resulting in the death of fishes. Besides, natural processes, e.g., photosynthesis, respiration, and decomposition, also contribute to the alteration of water quality that adversely affects fish health. Being motivated by the recent successes of machine learning techniques, a state-of-art machine learning algorithm has been adopted in this paper to detect and predict the degradation of water quality timely and accurately. Thus, it helps to take preemptive steps against potential fish diseases. The experimental r...

View PDFchevron_right
Early Detection of Fish Diseases by Analyzing Water Quality Using Machine Learning Algorithm
Joyeta Saha

arXiv (Cornell University), 2021

Early detection of fish diseases and identifying the underlying causes are crucial for farmers to take necessary steps to mitigate the potential outbreak and thus to avert financial losses with apparent negative implications to the national economy. Typically, fish diseases are caused by viruses and bacteria; according to biochemical studies, the presence of certain bacteria and viruses may affect the level of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N in water, resulting in the death of fishes. Besides, natural processes, e.g., photosynthesis, respiration, and decomposition, also contribute to the alteration of water quality that adversely affects fish health. Being motivated by the recent successes of machine learning techniques, a state-of-art machine learning algorithm has been adopted in this paper to detect and predict the degradation of water quality timely and accurately. Thus, it helps to take preemptive steps against potential fish diseases. The experimental results show high accuracy in detecting fish diseases specific to water quality based on the algorithm with real datasets.

View PDFchevron_right
Machine Learning in identification and control of biological disasters
Journal of Computer Science IJCSIS

Vol. 19 No. 4 APRIL 2021 International Journal of Computer Science and Information Security (IJCSIS), 2021

Biological invasion is a very common phenomenon. All alien species must have sites to grow and reproduce, which allows them to be called "colonizer" in a general sense. They would have adverse effects on the agricultural ecosystem by reducing the growth and output of the desired species [1]. In this case, the invasion of wasps caused serious potential adverse effects on bee populations in Europe, as well as on the other local biological populations. To reduce this adverse effect, avoiding invalid identify and reducing the possibility of the biological invasion, we construct model to identify wasps. In this paper, we use Grey Forecast Model and Convolution Neural Network Model to improve the accuracy of identification and better control the disaster. A. Data Cleaning Drop All Data with Wrong Format Data in the Submission Date Column Drop Rows with Null Value.

View PDFchevron_right
Artificial Neural Network Prediction of Viruses in Shellfish
R. Girones

Applied and Environmental Microbiology, 2005

A database was probed with artificial neural network (ANN) and multivariate logistic regression (MLR) models to investigate the efficacy of predicting PCR-identified human adenovirus (ADV), Norwalk-like virus (NLV), and enterovirus (EV) presence or absence in shellfish harvested from diverse countries in Europe (Spain, Sweden, Greece, and the United Kingdom). The relative importance of numerical and heuristic input variables to the ANN model for each country and for the combined data was analyzed with a newly defined relative strength effect, which illuminated the importance of bacteriophages as potential viral indicators. The results of this analysis showed that ANN models predicted all types of viral presence and absence in shellfish with better precision than MLR models for a multicountry database. For overall presence/absence classification accuracy, ANN modeling had a performance rate of 95.9%, 98.9%, and 95.7% versus 60.5%, 75.0%, and 64.6% for the MLR for ADV, NLV, and EV, respectively. The selectivity (prediction of viral negatives) was greater than the sensitivity (prediction of viral positives) for both models and with all virus types, with the ANN model performing with greater sensitivity than the MLR. ANN models were able to illuminate site-specific relationships

View PDFchevron_right
AquaVision: AI-Powered Marine Species Identification
ALAN DEIDUN

Information, 2024

This study addresses the challenge of accurately identifying fish species by using machine learning and image classification techniques. The primary aim is to develop an innovative algorithm that can dynamically identify the most common (within Maltese coastal waters) invasive Mediterranean fish species based on available images. In particular, these include Fistularia commersonii, Lobotes surinamensis, Pomadasys incisus, Siganus luridus, and Stephanolepis diaspros, which have been adopted as this study’s target species. Through the use of machine-learning models and transfer learning, the proposed solution seeks to enable precise, on-the-spot species recognition. The methodology involved collecting and organising images as well as training the models with consistent datasets to ensure comparable results. After trying a number of models, ResNet18 was found to be the most accurate and reliable, with YOLO v8 following closely behind. While the performance of YOLO was reasonably good, it exhibited less consistency in its results. These results underline the potential of the developed algorithm to significantly aid marine biology research, including citizen science initiatives, and promote environmental management efforts through accurate fish species identification.

View PDFchevron_right
Ornamental Fish Disease Prediction System
Narmada Gamage

International Journal of Computer Applications

Now, many individuals practice ornamental fish breeding as a part time, while others do it as their primary source of income. Some people practice ornamental fish breeding while having little expertise in the subject. Some individuals do this without knowing what illnesses might affect fish under varied environmental circumstances or what treatments are available. As a solution, we intend to find out what ailment's animals may have by monitoring the aquatic environment's environmental conditions and developing remedies for them. The key technology we utilized to create the I o T device was Arduino. To transport data from the device to the database, the PHP programming language is employed. We gathered data from prior articles. It is anticipated which aquatic plants are ideal for growing in this habitat by monitoring the aquatic environment of this water using an IoT device. If the aquatic habitat is unsuitable for fish, they will investigate what diseases the fish might contract in that setting and what treatments are available. Simultaneously, the number of fish that can be held in a tank, the amount of food they require, and the amount of food are projected. We illustrate the source of fish illnesses and how to avoid them. It is more useful for fish dealers and consumers to have a thorough awareness of what fish diseases are and what treatments can be done. We can surely declare that the accuracy of our study is 99%.

View PDFchevron_right
Application of Neural Networks to Biological Data Mining for Automatic Species Identification
Seetharam Narasimhan

2008

The paper aims at designing a scheme for automatic identification of a species from its genome sequence. A set of 64 three-tuple keywords is first generated using the four types of bases: A, T, C and G. These keywords are searched on N randomly sampled genome sequences, each of a given length (10,000 elements) and the frequency count for each of the 43=64 keywords is performed to obtain a DNA-descriptor for each sample. Principal Component analysis is then employed on the DNA-descriptors for N sampled instances. The principal component analysis yields a unique feature descriptor for identifying the species from its genome sequence. The variance of the descriptors for a given genome sequence being negligible, the proposed scheme finds extensive applications in automatic species identification. Next, a computational map is trained by the Self-Organizing Feature Map algorithm using the DNAdescriptors from different species as the training inputs. The map is shown to provide an easier technique for recognition and classification of a species based on its genomic data.

View PDFchevron_right
Early Warning Systems for Shellfish Safety: The Pivotal Role of Computational Science
David O'Neill

Lecture Notes in Computer Science, 2019

Toxins from harmful algae and certain food pathogens (Escherichia coli and Norovirus) found in shellfish can cause significant health problems to the public and have a negative impact on the economy. For the most part, these outbreaks cannot be prevented but, with the right technology and know-how, they can be predicted. These Early Warning Systems (EWS) require reliable data from multiple sources: satellite imagery, in situ data and numerical tools. The data is processed and analyzed and a short-term forecast is produced. Computational science is at the heart of any EWS. Current models and forecast systems are becoming increasingly sophisticated as more is known about the dynamics of an outbreak. This paper discusses the need, main components and future challenges of EWS.

View PDFchevron_right
Extreme learning machine towards dynamic model hypothesis in fish ethology research
Yoan Miche

Neurocomputing, 2014

In this paper, we present one dynamic model hypothesis to perform fish trajectory tracking in the fish ethology research and develop the relevant mathematical criterion on the basis of the Extreme Learning Machine (ELM). It is shown that the proposed scheme can conduct the non-linear and non Gaussian tracking process by multiple historical cues and current predictionsthe state vector motion, the color distribution and the appearance recognition, all of which can be extracted from the single-hidden layer feedforward neural network (SLFN) at diverse levels with ELM. The strategy of the hierarchical hybrid ELM ensemble then combines the individual SLFN of the tracking cues for the performance improvements. The simulation results have shown the excellent performance in both robustness and accuracy of the developed approach.

View PDFchevron_right
Optimized support vector machines for unveiling mortality incidence in Tilapia fish
Professor Aboul Ella Hassanien

Ain Shams Engineering Journal, 2021

In this paper, a new classification approach based on swarm-optimization is introduced to investigate the various effects of the ammonia concentration on the protein level and bioactivity that directly affect the Egyptian Nile's fish health and mortality rate (i.e. Tilapia fish ''O. Niloticus"). This approach enhances the Support Vector (SVM) Machines to classify the fish based on the protein level by Moth-Flame Optimization (MFO) algorithm. The experiment was divided into sub-phases: lab experiments and computational experiments. The primary purposes of the proposed approach, guiding decision-makers to review the pathophysiological status of the fish. The proposed MFO-SVM approach utilizes physical and chemical measurements to finally show revolutionary advances against the classic SVM and other well-known optimizers and classifiers. By achieving 99.983% of classification accuracy, the proposed approach outperforms other machine learning approaches on the same dataset. We believe that such an approach could be useful for many other real-world challenging tasks.

View PDFchevron_right

References (46)

  1. Frank, J.R., Olden, J.D.: Assessing the Effects of Climate Change on Aquatic Invasive Species. Conservation Biology 22(3), 521-533 (2008). doi:10.1111/j.1523-1739.2008.00950. x. Society for Conservation Biology
  2. Kheifets, J., Rozhavsky, B., Solomonovich, Z.G., Rodman, M., Soroksky, A.: Severe Te- trodotoxin Poisoning after Consumption of Lagocephalus sceleratus (Pufferfish, Fugu) Fished in Mediterranean Sea, Treated with Cholinesterase Inhibitor. Case Reports in Criti- cal Care 2012, Article ID 782507, 3 p. (2012). doi:10.1155/2012/782507
  3. Akova, F., Dundar, M., Davisson, V.J., Hirleman, D.E., Bhunia, A.K., Robinson, J.P., Rajwa, B.: A Machine-Learning Approach to Detecting Unknown Bacterial Serovars. Sta- tistical Analysis and Data Mining (2011). doi:10.1002/sam.10085
  4. Pan, W., Zhao, J., Chen, Q.: Classification of foodborne pathogens using near infrared la- ser scatter imaging system with multivariate calibration (2015). doi:10.1038/srep09524
  5. Rajwa, B., Dundar, M.M., Akova, F., Bettasso, A., Patsekin, V., Hirleman, E.D., Bhunia, A.K., Robinson, J.P.: Discovering the Unknown: Detection of Emerging Pathogens Using a Label-Free Light-Scattering System. Cytometry Part A 77A, 1103-1112 (2010)
  6. Rajwa, B., Venkatapathi, M., Ragheb, K., Banada, P.P., Hirleman, E.D., Lary, T., Robin- son, J.P.: Automated classification and recognition of bacterial particles in flow by multi- angle scatter measurement and a support-vector machine classifier. Cytometry A 73(4), 369-379 (2008). doi:10.1002/cyto.a.20515
  7. Pan, Y.: Protein structure prediction and understanding using machine learning methods. In: 2005 IEEE Granular Computing, vol. 1 (2005). doi:10.1109/GRC.2005.1547225
  8. Ma, X., Hu, L.: Extracting sequence features to predict DNA-binding proteins using sup- port vector machine. In: 2013 Fifth International Conference on Computational and Infor- mation Sciences (ICCIS) (2013). doi:10.1109/ICCIS.2013.48
  9. Yu, D.-J., Hu, J., Li, Q.M., Tang, Z.M., Yang, J.Y., Shen, H.B.: Constructing Query- Driven Dynamic Machine Learning Model With Application to Protein-Ligand Binding Sites Prediction. NanoBioscience, IEEE, 14(1) (2015)
  10. Leigh, D., Thredgold, E.A.V., Lenehan, C.E.: Direct detection of histamine in fish flesh using microchip electrophoresis with capacitively coupled contactless conductivity detec- tion. Anal. Methods, 1802-1808 (2015). doi:10.1039/C4AY02866J
  11. Lipman, D.J., Pearson, W.R.: Rapid and sensitive protein similarity searches. Science 227(4693), 1435-1441 (1985). doi:10.1126/science.2983426. PMID 2983426
  12. Moraglio, A., Di Chio, C., Poli, R.: Geometric Particle Swarm Optimization 2008, Article ID 143624, 14 p. (2008). doi:10.1155/2008/143624
  13. Rokach, Lior: Ensemble-based classifiers. Artificial Intelligence Review 33(1-2), 1-39 (2010). doi:10.1007/s10462-009-9124-7
  14. Cambria, E., Huang, G.-B.: Extreme Learning Machines. IEEE Intelligent Systems (2013)
  15. Huang, G.-B.: An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels (2014). doi:10.1007/s12559-014-9255-2, Springer 17. http://www.cabi.org/isc/
  16. Nitesh, V., Chawla, B.K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16 (2002)
  17. Anezakis, V.-D., Demertzis, K., Iliadis, L., 2018. Classifying with fuzzy chi-square test: The case of invasive species. AIP Conference Proceedings 1978, 290003. https://doi.org/10/gdtm5q
  18. Anezakis, V.-D., Demertzis, K., Iliadis, L., Spartalis, S., 2017a. Hybrid intelligent modeling of wild fires risk. Evolving Systems 1-17. https://doi.org/10/gdp863
  19. Anezakis, V.-D., Demertzis, K., Iliadis, L., Spartalis, S., 2016a. A Hybrid Soft Computing Approach Producing Robust Forest Fire Risk Indices, in: Artificial Intelligence Applications and Innovations, IFIP Advances in Information and Communication Technology. Presented at the IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer, Cham, pp. 191-203. https://doi.org/10.1007/978-3-319-44944-9_17
  20. Anezakis, V.-D., Dermetzis, K., Iliadis, L., Spartalis, S., 2016b. Fuzzy Cognitive Maps for Long-Term Prognosis of the Evolution of Atmospheric Pollution, Based on Climate Change Scenarios: The Case of Athens, in: Computational Collective Intelligence, Lecture Notes in Computer Science. Presented at the International Conference on Computational Collective Intelligence, Springer, Cham, pp. 175-186. https://doi.org/10.1007/978-3-319-45243-2_16
  21. Anezakis, V.-D., Iliadis, L., Demertzis, K., Mallinis, G., 2017b. Hybrid Soft Computing Analytics of Cardiorespiratory Morbidity and Mortality Risk Due to Air Pollution, in: Information Systems for Crisis Response and Management in Mediterranean Countries, Lecture Notes in Business Information Processing. Presented at the International Conference on Information Systems for Crisis Response and Management in Mediterranean Countries, Springer, Cham, pp. 87-105. https://doi.org/10.1007/978-3-319-67633-3_8
  22. Anezakis, V.D., Mallinis, G., Iliadis, L., Demertzis, K., 2018. Soft computing forecasting of cardiovascular and respiratory incidents based on climate change scenarios, in: 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). Presented at the 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1-8. https://doi.org/10.1109/EAIS.2018.8397174
  23. Bougoudis, I., Demertzis, K., Iliadis, L., 2016a. Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning. Integrated Computer-Aided Engineering 23, 115-127. https://doi.org/10/f8dt4t
  24. Bougoudis, I., Demertzis, K., Iliadis, L., 2016b. HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens. Neural Comput & Applic 27, 1191-1206. https://doi.org/10/f8r7vf
  25. Bougoudis, I., Demertzis, K., Iliadis, L., Anezakis, V.-D., Papaleonidas, A., 2018. FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens. Neural Computing and Applications 29. https://doi.org/10/gc9bbf
  26. Bougoudis, I., Demertzis, K., Iliadis, L., Anezakis, V.-D., Papaleonidas, A., 2016c. Semi- supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers, in: Engineering Applications of Neural Networks, Communications in Computer and Information Science. Presented at the International Conference on Engineering Applications of Neural Networks, Springer, Cham, pp. 51-63. https://doi.org/10.1007/978-3-319-44188-7_4
  27. Demertzis, K., Iliadis, L., 2018a. A Computational Intelligence System Identifying Cyber-Attacks on Smart Energy Grids, in: Modern Discrete Mathematics and Analysis, Springer Optimization and Its Applications. Springer, Cham, pp. 97-116. https://doi.org/10.1007/978-3-319-74325-7_5
  28. Demertzis, K., Iliadis, L., 2018b. The Impact of Climate Change on Biodiversity: The Ecological Consequences of Invasive Species in Greece, in: Handbook of Climate Change Communication: Vol. 1, Climate Change Management. Springer, Cham, pp. 15-38. https://doi.org/10.1007/978-3-319-69838-0_2
  29. Demertzis, K., Iliadis, L., 2017. Detecting invasive species with a bio-inspired semi- supervised neurocomputing approach: the case of Lagocephalus sceleratus. Neural Computing and Applications 28. https://doi.org/10/gbkgb7
  30. Demertzis, K., Iliadis, L., 2016a. Bio-inspired Hybrid Intelligent Method for Detecting Android Malware, in: Knowledge, Information and Creativity Support Systems, Advances in Intelligent Systems and Computing. Springer, Cham, pp. 289-304. https://doi.org/10.1007/978-3-319-27478-2_20
  31. Demertzis, K., Iliadis, L., 2016b. Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species, in: Advances in Big Data, Advances in Intelligent Systems and Computing. Presented at the INNS Conference on Big Data, Springer, Cham, pp. 333-345. https://doi.org/10.1007/978-3-319-47898-2_34
  32. Demertzis, K., Iliadis, L., 2015a. A Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security, in: Computation, Cryptography, and Network Security. Springer, Cham, pp. 161-193. https://doi.org/10.1007/978-3-319-18275-9_7
  33. Demertzis, K., Iliadis, L., 2015b. SAME: An Intelligent Anti-malware Extension for Android ART Virtual Machine, in: Computational Collective Intelligence, Lecture Notes in Computer Science. Springer, Cham, pp. 235-245. https://doi.org/10.1007/978-3- 319-24306-1_23
  34. Demertzis, K., Iliadis, L., 2015c. Evolving Smart URL Filter in a Zone-Based Policy Firewall for Detecting Algorithmically Generated Malicious Domains, in: Statistical Learning and Data Sciences, Lecture Notes in Computer Science. Presented at the International Symposium on Statistical Learning and Data Sciences, Springer, Cham, pp. 223-233. https://doi.org/10.1007/978-3-319-17091-6_17
  35. Demertzis, K., Iliadis, L., 2015d. Intelligent Bio-Inspired Detection of Food Borne Pathogen by DNA Barcodes: The Case of Invasive Fish Species Lagocephalus Sceleratus, in: Engineering Applications of Neural Networks, Communications in Computer and Information Science. Presented at the International Conference on Engineering Applications of Neural Networks, Springer, Cham, pp. 89-99. https://doi.org/10.1007/978-3-319-23983-5_9
  36. Demertzis, K., Iliadis, L., 2014. Evolving Computational Intelligence System for Malware Detection, in: Advanced Information Systems Engineering Workshops, Lecture Notes in Business Information Processing. Presented at the International Conference on Advanced Information Systems Engineering, Springer, Cham, pp. 322- 334. https://doi.org/10.1007/978-3-319-07869-4_30
  37. Demertzis, K., Iliadis, L., 2013. A Hybrid Network Anomaly and Intrusion Detection Approach Based on Evolving Spiking Neural Network Classification, in: E-Democracy, Security, Privacy and Trust in a Digital World, Communications in Computer and Information Science. Presented at the International Conference on e-Democracy, Springer, Cham, pp. 11-23. https://doi.org/10.1007/978-3-319-11710-2_2
  38. Demertzis, Konstantinos, Iliadis, L., Anezakis, V.-D., 2017a. Commentary: Aedes albopictus and Aedes japonicus-two invasive mosquito species with different temperature niches in Europe. Front. Environ. Sci. 5. https://doi.org/10/gdp865
  39. Demertzis, Kostantinos, Iliadis, L., Avramidis, S., El-Kassaby, Y.A., 2017. Machine learning use in predicting interior spruce wood density utilizing progeny test information. Neural Comput & Applic 28, 505-519. https://doi.org/10/gdp86z
  40. Demertzis, Konstantinos, Iliadis, L., Spartalis, S., 2017b. A Spiking One-Class Anomaly Detection Framework for Cyber-Security on Industrial Control Systems, in: Engineering Applications of Neural Networks, Communications in Computer and Information Science. Presented at the International Conference on Engineering Applications of Neural Networks, Springer, Cham, pp. 122-134. https://doi.org/10.1007/978-3-319-65172-9_11
  41. Demertzis, K., Iliadis, L.S., Anezakis, V.-D., 2018a. An innovative soft computing system for smart energy grids cybersecurity. Advances in Building Energy Research 12, 3-24. https://doi.org/10/gdp862
  42. Demertzis, K., Iliadis, L.S., Anezakis, V.-D., 2018b. Extreme deep learning in biosecurity: the case of machine hearing for marine species identification. Journal of Information and Telecommunication 0, 1-19. https://doi.org/10/gdwszn
  43. Dimou, V., Anezakis, V.-D., Demertzis, K., Iliadis, L., 2018. Comparative analysis of exhaust emissions caused by chainsaws with soft computing and statistical approaches. Int. J. Environ. Sci. Technol. 15, 1597-1608. https://doi.org/10/gdp864
  44. Anezakis, VD., Demertzis, K., Iliadis, L. et al. Evolving Systems (2017). https://doi.org/10.1007/s12530-017-9196-6, Hybrid intelligent modeling of wild fires risk, Springer.
  45. Demertzis K., Anezakis VD., Iliadis L., Spartalis S. (2018) Temporal Modeling of Invasive Species' Migration in Greece from Neighboring Countries Using Fuzzy Cognitive Maps. In: Iliadis L., Maglogiannis I., Plagianakos V. (eds) Artificial Intelligence Applications and Innovations. AIAI 2018. IFIP Advances in Information and Communication Technology, vol 519. Springer, Cham.
  46. Konstantinos Rantos, George Drosatos, Konstantinos Demertzis, Christos Ilioudis and Alexandros Papanikolaou. Blockchain-based Consents Management for Personal Data Processing in the IoT Ecosystem. In proceedings of the 15th International Conference on Security and Cryptography (SECRYPT 2018), part of ICETE, pages 572-577, SCITEPRESS, Porto, Portugal, 26-28 July 2018.

Related papers

Intelligent Bio-Inspired Detection of Food Borne Pathogen by DNA Barcodes: The Case of Invasive Fish Species Lagocephalus Sceleratus
Kostantinos Demertzis

Communications in Computer and Information Science, 2015

Climate change combined with the increase of extreme weather phenomena, has significantly influenced marine ecosystems, resulting in water overheating, increase of sea level and rising of the acidity of surface waters. The potential impacts in the biodiversity of sensitive ecosystems (such as Mediterranean sea) are obvious. Many organisms are under extinction, whereas other dangerous invasive species are multiplied and thus they are destroying the ecological equilibrium. This research paper presents the development of a sophisticated, fast and accurate Food Pathogen Detection (FPD) system, which uses the biologically inspired Artificial Intelligence algorithm of Extreme Learning Machines. The aim is the automated identification and control of the extremely dangerous for human health invasive fish species "Lagocephalus Sceleratus". The matching is achieved through extensive comparisons of protein and DNA sequences, known also as DNA barcodes following an ensemble learning approach.

View PDFchevron_right
Detecting invasive species with a bio-inspired semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus
Kostantinos Demertzis

Neural Computing and Applications, 2016

The need to protect the environment and biodiversity and to safeguard public health require the development of timely and reliable methods for the identification of particularly dangerous invasive species, before they become regulators of ecosystems. These species appear to be morphologically similar, despite their strong biological differences, something that complicates their identification process. Additionally, the localization of the broader space of dispersion and the development of invasive species are considered to be of critical importance in the effort to take proper management measures. The aim of this research is to create an advanced computational intelligence system for the automatic recognition, of invasive or another unknown species. The identification is performed based on the analysis of environmental DNA by employing machine learning methods. More specifically, this research effort proposes a hybrid bio-inspired computational intelligence detection approach. It employs extreme learning machines combined with an evolving Izhikevich spiking neuron model for the automated identification of the invasive fish species ''Lagocephalus sceleratus'' extremely dangerous for human health.

View PDFchevron_right
Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms
trinh thi Trang

G3 Genes|Genomes|Genetics

Assessments of genomic prediction accuracies using artificial intelligence (AI) algorithms (i.e.,, machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e.,, pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e.,, ML-KAML) and deep learning (i.e.,, DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e.,, survival status coded as 0 and 1 and survival time, i.e.,, days that the animals were still alive after the...

View PDFchevron_right
A CNN-OSELM Multi-Layer Fusion Network With Attention Mechanism for Fish Disease Recognition in Aquaculture
Yo-Ping Huang

IEEE Access

The increasing global population has escalated the demand for fish products. This calls for a stable supply which can only be met through improved aquaculture practices. The automatic recognition of fish diseases from diseased underwater images is one of such practices that aims to control disease, improve fish production and optimize profits. However, due to lack of public fish disease dataset, there has been limited research towards fish disease recognition. Moreover, due to low quality of underwater images and complex underwater environments, traditional hand-designed feature extraction methods or convolutional neural networks (CNNs)-based classifiers cannot adequately recognize fish diseases in real underwater scenes. Therefore, this paper proposes a novel hybrid approach based on multilayer fusion, attention mechanism and online sequential extreme learning machine (OSELM) to recognize fish diseases in aquaculture. We further compare the classification performance of the proposed model with baseline, attention-based, ConvNeXt and Swin transformer models. Feature extraction is enhanced by integrating same level features and focusing on salient features for fish disease recognition. The characteristic information of fish disease is refined by using strongly discriminative features of the infected fish regions and weakening regions of low interest using convolutional block attention module (CBAM). The module is added to the multilayer fusion network to sequentially infer attention maps along the channel and spatial dimensions for every intermediate feature map. Fish disease recognition is then done using OSELM for faster learning and improved classification performance. The models are trained, validated and tested on a custom dataset with image samples collected from various internet sources. The proposed method achieves 94.28% of accuracy, precision of 92.67%, recall of 92.17% and 92.42% of F1-score on dataset with background elimination. The proposed method can be used for fish disease identification in complex underwater environments in aquaculture. INDEX TERMS Fish disease, aquaculture, multilayer fusion, extreme learning machines, attention mechanism.

View PDFchevron_right
A Novel Approach to Fish Disease Diagnostic System based on Machine Learning
shaveta malik

Advances in Image and Video Processing, 2017

Real-Time identification automated system diagnoses fish disease i.e. Epizootic Ulcerative syndrome (EUS) which is caused by Aphanomyces invadans, a fungal pathogen. In this paper we propose a Real-Time fish disease diagnose system with better accuracy. In order to improve the accuracy we propose a combination (PCA-FAST-NN) which combine the Principle component analysis (PCA) with Features from Accelerated Segment Test (FAST)feature detector using Machine Learning Algorithm(Neural Network) i.e. (PCA-FAST-NN) .The Experimentation has been done on the real images of Epizootic Ulcerative syndrome (EUS) infected fish database and implemented in MATLAB environment.

View PDFchevron_right

Cited by

A Machine Hearing Framework for Real-Time Streaming Analytics Using Lambda Architecture
Kostantinos Demertzis

Engineering Applications of Neural Networks

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved