| 1. |
- Arunachalam, Ajay, 1985-, et al.
(författare)
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Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks
- 2021
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Ingår i: Computers, Materials and Continua. - : Tech Science Press. - 1546-2218 .- 1546-2226. ; 68:1, s. 983-1001
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Tidskriftsartikel (refereegranskat)abstract
- One of the key challenges in ad-hoc networks is the resource discovery problem. How efficiently & quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question? Broadcasting is a basic technique in the Mobile Ad-hoc Networks (MANETs), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique floods the network with query messages, while the random walk scheme operates by contacting subsets of each node's neighbors at every step, thereby restricting the search space. Many earlier works have mainly focused on the simulation-based analysis of flooding technique, and its variants, in a wired network scenario. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of mobile P2P networks. In this article, we mathematically model different widely used existing search techniques, and compare with the proposed improved random walk method, a simple lightweight approach suitable for the non-DHT architecture. We provide analytical expressions to measure the performance of the different flooding-based search techniques, and our proposed technique. We analytically derive 3 relevant key performance measures, i.e., the avg. number of steps needed to find a resource, the probability of locating a resource, and the avg. number of messages generated during the entire search process.
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| 2. |
- Arunachalam, Ajay, 1985-, et al.
(författare)
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Mathematical Model Validation of Search Protocols in MP2P Networks
- 2021
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Ingår i: Computers, Materials and Continua. - : Tech Science Press. - 1546-2218 .- 1546-2226. ; 68:2, s. 1807-1829
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Tidskriftsartikel (refereegranskat)abstract
- Broadcasting is a basic technique in Mobile ad-hoc network (MANET), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique, floods the network with query messages, while the random walk technique operates by contacting the subsets of every node's neighbors at each step, thereby restricting the search space. One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource. Many earlier works have mainly focused on the simulation-based analysis of flooding, and its variants under a wired network. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of P2P systems running over MANET. In this paper, we describe how P2P resource discovery protocols perform badly over MANETs. To address the limitations, we propose a new protocol named ABRW (Address Broadcast Random Walk), which is a lightweight search approach, designed considering the underlay topology aimed to better suit the unstructured architecture. We provide the mathematical model, measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques. Further, we also derive three relevant search performance metrics, i.e., mean no. of steps needed to find a resource, the probability of finding a resource, and the mean no. of message overhead. We validated the analytical expressions through simulations. The simulation results closely matched with our analyticalmodel, justifying our findings. Our proposed search algorithm under such highly dynamic self-evolving networks performed better, as it reduced the search latency, decreased the overall message overhead, and still equally had a good success rate.
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| 3. |
- Arunachalam, Ajay, 1985-, et al.
(författare)
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Toward Data-Model-Agnostic Autonomous Machine-Generated Data Labeling and Annotation Platform : COVID-19 Autoannotation Use Case
- 2023
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Ingår i: IEEE transactions on engineering management. - : IEEE. - 0018-9391 .- 1558-0040. ; 70:8, s. 2695-2706
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Tidskriftsartikel (refereegranskat)abstract
- Quick, early, and precise detection is important for diagnosis to control the spread of COVID-19 infection. Artificial Intelligence (AI) technology could certainly be used as a modulating tool to ease the detection, and help with the preventive steps further. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many visual recognition tasks. Nevertheless, most of these state-of-the-art networks highly rely on the availability of a high amount of labeled data, being an essential step in supervised machine learning tasks. Conventionally, this manual, mundane, and time-consuming process of annotating images is done by humans. Learning to localize or detect COVID-19 infection masks in our specific case study typically requires the collection of CT scan data that has been labeled with bounding boxes or similar annotations, which generally is limited. A technique that could perform such learning with much less annotations, and transfer the learned proposals that are algorithm-driven to generate more synthetic annotated samples would be helpful & quite valuable. We present such a technique inspired by weakly trained mask region based convolutional neural networks (R-CNN) architecture for localization, in which the number of images with their pixel-level masks can be a small proportion of the total dataset, and then further improvise CNNs by inversely generating dense annotations on-the-go using an algorithmic-based computational approach. We focus on alleviating the bottleneck associated with deep learning models needing annotated data for training in an intuitive reverse engineering fashion through this work. Our proposed solution can certainly provide the prospect of automated labeling on-the-fly, thereby reducing much of the manual work. As a result, one can quickly train a precise COVID-19 infection detector with the leverage of autonomous frame-by-frame machine generated annotations. The model achieved mean precision accuracy (%) of 0.99, 0.931, and 0.8 for train, validation, and test set, respectively. The results demonstrate that the proposed method can be adopted in a clinical setting for assisting radiologists, and also our fully autonomous approach can be generalized to any detection/recognition tasks at ease.
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| 4. |
- Pham, Tuan D., et al.
(författare)
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Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy
- 2023
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Ingår i: Exploration of Targeted Anti-tumor Therapy. - : Open Exploration Publishing. - 2692-3114. ; 4:1, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Aim: The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are indicators of pathogenesis or measures of responses to therapeutic treatments, and therefore, play a key role in new drug development. Proteins are among the candidates for biomarkers of rectal cancer, which need to be explored using state-of-the-art AI to be utilized for prediction, prognosis, and therapeutic treatment. This paper aims to investigate the predictive power of Ras homolog family member B (RhoB) protein in rectal cancer.Methods: This study introduces the integration of pretrained convolutional neural networks and support vector machines (SVMs) for classifying biopsy samples of immunohistochemical expression of protein RhoB in rectal-cancer patients to validate its biologic measure in biopsy. Features of the immunohistochemical expression images were extracted by the pretrained networks and used for binary classification by the SVMs into two groups of less and more than 5-year survival rates.Results: The fusion of neural search architecture network (NASNet)-Large for deep-layer feature extraction and classifier using SVMs provided the best average classification performance with a total accuracy = 85%, prediction of survival rate of more than 5 years = 90%, and prediction of survival rate of less than 5 years = 75%.Conclusions: The finding obtained from the use of AI reported in this study suggest that RhoB expression on rectal-cancer biopsy can be potentially used as a biomarker for predicting survival outcomes in rectal-cancer patients, which can be informative for clinical decision making if the patient would be recommended for preoperative therapy.
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| 5. |
- Pham, Tuan D., et al.
(författare)
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Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients
- 2023
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Ingår i: IEEE Journal of Translational Engineering in Health and Medicine. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2168-2372. ; 11, s. 87-95
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Tidskriftsartikel (refereegranskat)abstract
- Background: Over a decade, tissues dissected adjacent to primary tumors have been considered "normal " or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers. Methods: This paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs. Results: Ten-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods. Conclusion: Preliminary results not only add objective evidence to recent findings of NATs molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients. Clinical impact: The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.
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| 6. |
- Pham, Tuan D., et al.
(författare)
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Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathologic Predictive Power of RhoB in Rectal Cancer Biopsy
- 2023
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Ingår i: American Journal of Pathology. - : ELSEVIER SCIENCE INC. - 0002-9440 .- 1525-2191. ; 193:5, s. 579-590
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Tidskriftsartikel (refereegranskat)abstract
- RhoB protein belongs to the Rho GTPase family, which plays an important role in governing cell signaling and tissue morphology. Its expression is known to have implications in pathologic processes of diseases. In particular, the role of RhoB in rectal cancer is not well understood. Investigation in the regulation and communication of this protein, detected by immunohistochemical staining on the mi-croscope, can help gain insightful information leading to optimal disease treatment options. Herein, deep learning-based image analysis and the decomposition of multiway arrays were used to study the predictive factor of RhoB in two cohorts of patients with rectal cancer having survival rates of <5 and >5 years. The results show distinctions between the tensor decomposition factors of the two cohorts. (Am J Pathol 2023, 193: 579-590; https://doi.org/10.1016/j.ajpath.2023.01.007)
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| 7. |
- Ravi, Vinayakumar, et al.
(författare)
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Adversarial Defense : DGA-Based Botnets and DNS Homographs Detection Through Integrated Deep Learning
- 2023
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Ingår i: IEEE transactions on engineering management. - : IEEE. - 0018-9391 .- 1558-0040. ; 70:1, s. 249-266
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Tidskriftsartikel (refereegranskat)abstract
- Cybercriminals use domain generation algorithms (DGAs) to prevent their servers from being potentially blacklisted or shut down. Existing reverse engineering techniques for DGA detection is labor intensive, extremely time-consuming, prone to human errors, and have significant limitations. Hence, an automated real-time technique with a high detection rate is warranted in such applications. In this article, we present a novel technique to detect randomly generated domain names and domain name system (DNS) homograph attacks without the need for any reverse engineering or using nonexistent domain (NXDomain) inspection using deep learning. We provide an extensive evaluation of our model over four large, real-world, publicly available datasets. We further investigate the robustness of our model against three different adversarial attacks: DeepDGA, CharBot, and MaskDGA. Our evaluation demonstrates that our method is effectively able to identify DNS homograph attacks and DGAs and also is resilient to common evading cyberattacks. Promising results show that our approach provides a more effective detection rate with an accuracy of 0.99. Additionally, the performance of our model is compared against the most popular deep learning architectures. Our findings highlight the essential need for more robust detection models to counter adversarial learning.
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| 8. |
- Suliman, Wael, et al.
(författare)
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Convolutional Neural Networks and Support Vector Machines for Five-Year Survival Analysis of Metastatic Rectal Cancer
- 2022
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Ingår i: 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). - : IEEE. - 9781728186719 - 9781665495264
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Konferensbidrag (refereegranskat)abstract
- Rectal or colorectal cancer is one of the leading causes of cancer-related death. With the advancement in surgical techniques, the survival rate has been improved. Predicting the survival rate is an important factor for enabling optimal treatments to prolong rectal-cancer patients lives. Methods of artificial intelligence and machine learning have been applied for assisting physicians in cancer research. In this study, we investigated the use of pretrained convolutional neural networks and support vector machines for predicting the survival rate of a cohort of rectal-cancer patients using metastatic immunohistochemistry samples staining for protein RhoB. The combination of convolutional neural networks and support vector machines achieved better classification results than using individual pretrained deep networks in most cases, and where manual pathological analysis is encountered with great difficulty. In particular, the combination of ResNet-101 and SVM produced an average accuracy of 86% for non-radiotherapy, and Inception-v3 and SVM resulted in an average accuracy of 85% for radiotherapy.
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| 9. |
- Sureshkumar, Vidhushavarshini, et al.
(författare)
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A hybrid optimization algorithm-based feature selection for thyroid disease classifier with rough type-2 fuzzy support vector machine
- 2022
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Ingår i: Expert systems (Print). - : John Wiley & Sons. - 0266-4720 .- 1468-0394. ; 39:1
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Tidskriftsartikel (refereegranskat)abstract
- Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed-kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.
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