A comprehensive review of computational methods for automatic prediction of schizophrenia with insight into indigenous populations
- Authors: Ratana, Randall , Sharifzadeh, Hamid , Krishnan, Jamuna , Pang, Shaoning
- Date: 2019
- Type: Text , Journal article , Review
- Relation: Frontiers in Psychiatry Vol. 10, no. (2019), p.
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- Description: Psychiatrists rely on language and speech behavior as one of the main clues in psychiatric diagnosis. Descriptive psychopathology and phenomenology form the basis of a common language used by psychiatrists to describe abnormal mental states. This conventional technique of clinical observation informed early studies on disturbances of thought form, speech, and language observed in psychosis and schizophrenia. These findings resulted in language models that were used as tools in psychosis research that concerned itself with the links between formal thought disorder and language disturbances observed in schizophrenia. The end result was the development of clinical rating scales measuring severity of disturbances in speech, language, and thought form. However, these linguistic measures do not fully capture the richness of human discourse and are time-consuming and subjective when measured against psychometric rating scales. These linguistic measures have not considered the influence of culture on psychopathology. With recent advances in computational sciences, we have seen a re-emergence of novel research using computing methods to analyze free speech for improving prediction and diagnosis of psychosis. Current studies on automated speech analysis examining for semantic incoherence are carried out based on natural language processing and acoustic analysis, which, in some studies, have been combined with machine learning approaches for classification and prediction purposes. © Copyright © 2019 Ratana, Sharifzadeh, Krishnan and Pang.
Auto-identification of two Sitophilus sibling species on stored wheat using deep convolutional neural network
- Authors: Yang, Haiying , Zhao, Huiyi , Zhang, Dufeng , Cao, Yang , Teng, Shyh , Pang, Shaoning , Zhou, Xiaoguang , Li, Yanyu
- Date: 2022
- Type: Text , Journal article
- Relation: Pest Management Science Vol. 78, no. 5 (2022), p. 1925-1937
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- Description: BACKGROUND: Sitophilus oryzae and Sitophilus zeamais are the two main insect pests that infest stored grain worldwide. Accurate and rapid identification of the two pests is challenging because of their similar appearances. The S. zeamais adults are darker and shinier than S. oryzae in visible light. Convolutional neural network (CNN) can be applied for the effective differentiation due to its high effectiveness in object recognition. RESULTS: We propose a multilayer convolutional structure (MCS) feature extractor to extract insect characteristics within each layer of the CNN architecture. A region proposal network is adopted to determine the location of a potential pest in the wheat background. The precision of classification and the robustness of bounding box regression are increased by including deeper layer variables into the classification and bounding box regression subnets, as well as combining loss functions softmax and smooth L1. The proposed multilayer convolutional structure network (MCSNet) achieves the mean average precision of 87.89 ± 2.36% from the laboratory test, with an average detection speed of 0.182 ± 0.005 s per test. The model was further assessed with the field trials, and the obtained accuracy was 90.35 ± 3.12%. For all test conditions, the average precision for S. oryzae was higher than that for S. zeamais. CONCLUSION: The proposed MCSNet model has demonstrated that it is a fast and accurate method for detecting sibling species from visible light images in both laboratory and field trials. This will ultimately be applied for pest management together with an upgraded industrial camera system, which has been installed in over 100 000 grain depots of China. © 2022 Society of Chemical Industry.
Critical data detection for dynamically adjustable product quality in IIoT-enabled manufacturing
- Authors: Sen, Sachin , Karmakar, Gour , Pang, Shaoning
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Access Vol. 11, no. (2023), p. 49464-49480
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- Description: The IIoT technologies, due to the widespread use of sensors, generate massive data that are key in providing innovative and efficient industrial management, operation, and product quality control processes. The significance of data has prompted relevant research communities and application developers how to harness the values of these data in secure manufacturing. Critical data analysis, identification of critical factors to improve the manufacturing process and critical data associated with product quality have been investigated in the current literature. However, the current works on product quality control are mainly based on static data analysis, where data may change, but there is no way to adjust them dynamically. Thus, they are not applicable for product quality control, at which point their adjustment is instantly required. However, many manufacturing systems exist, like beverages and food, where ingredients must be adjusted instantaneously to maintain product quality. To address this research gap, we introduce a method that identifies the critical data based on their ranking by exploiting three criticality assessment criteria that capture the instantaneous product quality change during manufacturing. These three criteria are - (1) correlation, (2) percentage quality change and (3) sensitivity for the assessment of data criticality. The product quality is estimated using polynomial regression (POLY), SVM, and DNN. The proposed method is validated using wine manufacturing data. Our proposed method accurately identifies critical data, where SVM produces the lowest average production quality prediction error (10.40%) compared with that of POLY (11%) and DNN (14.40%). © 2013 IEEE.
Deep learning for combo object detection
- Authors: Zhao, Jing , Ardekani, Iman Tabatabaei , Pang, Shaoning
- Date: 2019
- Type: Text , Book chapter
- Relation: Chapter 11 p. 125-137
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- Description: Convolutional neural networks (CNNs) have become the most vigorous technique for a variety of different tasks in computer vision, due to their proficiency in automatically learning high-level visual representations for images. In this paper, we investigate the effect of deep neural networks on the accuracy in combo object detection setting. The insufficiency of labeled data, coupled with the uncertainty of spacial distribution and dynamic changes in luminance, creates situations where combo object detection is far more challenging. Using transfer learning, we present a system for combo object detection based on a deep CNN called ComboNN. The proposed ComboNN is pre-trained on a huge auxiliary dataset ImageNet and fine-tuned on our small dataset. The use of data augmentation and regularization technique significantly reduces overfitting and improves the robustness of the ComboNN. Experimental results demonstrate that our system is capable of making reliable prediction on combo object detection in the real-world images, and achieves much better accuracy than the state-of-the-art CNNs.
Diversified and scalable service recommendation with accuracy guarantee
- Authors: Wang, Lina , Zhang, Xuyun , Wang, Tian , Wan, Shaohua , Pang, Shaoning
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 5 (2021), p. 1182-1193
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- Description: As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user-item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users' requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users' satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability. © 2014 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**
Image encryption based on fractional-order chen hyperchaotic system
- Authors: Peng, Jun , Yang, Wu , Jin, Shangzhu , Pang, Shaoning , Tang, Dedong , Bai, Junjie , Zhang, Du
- Date: 2020
- Type: Text , Conference paper
- Relation: 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020, Virtual, Kristiansand, Norway, 9 to 13 November 2020, Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 p. 213-217
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- Description: This paper proposes a novel image encryption algorithm with the encrypted sequence encoded based on a fractional-order hyperchaotic Chen system. Specifically, a set of system parameters is firstly generated using an 8-byte secret key. Then we create the encrypted sequence by mixing the system parameters with the hyperchaos sequence generated by the fractional-order Chen system. In the end, the encryption sequence is XOR with the image plaintext to produce the encrypted image. In order to increase the ability to resist attack, the ciphertext of the previous pixel is employed for the encryption of subsequent pixels. Numerical experiments has demonstrated the cryptographic excellency of the proposed encryption algorithm. © 2020 IEEE.
Indoor emission sources detection by pollutants interaction analysis
- Authors: Pang, Shaoning , Song, Lei , Sarrafzadeh, Abdolhossein , Coulson, Guy , Longley, Ian , Olivares, Gustavo
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 16 (2021), p.
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- Description: This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
MCSNet+ : enhanced convolutional neural network for detection and classification of tribolium and sitophilus sibling species in actual wheat storage environments
- Authors: Yang, Haiying , Li, Yanyu , Xin, Liyong , Teng, Shyh , Pang, Shaoning , Zhao, Huiyi , Cao, Yang , Zhou, Xiaoguang
- Date: 2023
- Type: Text , Journal article
- Relation: Foods Vol. 12, no. 19 (2023), p.
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- Description: Insect pests like Tribolium and Sitophilus siblings are major threats to grain storage and processing, causing quality and quantity losses that endanger food security. These closely related species, having very similar morphological and biological characteristics, often exhibit variations in biology and pesticide resistance, complicating control efforts. Accurate pest species identification is essential for effective control, but workplace safety in the grain bin associated with grain deterioration, clumping, fumigator hazards, and air quality create challenges. Therefore, there is a pressing need for an online automated detection system. In this work, we enriched the stored-grain pest sibling image dataset, which includes 25,032 annotated Tribolium samples of two species and five geographical strains from real warehouse and another 1774 from the lab. As previously demonstrated on the Sitophilus family, Convolutional Neural Networks demonstrate distinct advantages over other model architectures in detecting Tribolium. Our CNN model, MCSNet+, integrates Soft-NMS for better recall in dense object detection, a Position-Sensitive Prediction Model to handle translation issues, and anchor parameter fine-tuning for improved matching and speed. This approach significantly enhances mean Average Precision (mAP) for Sitophilus and Tribolium, reaching a minimum of 92.67 ± 1.74% and 94.27 ± 1.02%, respectively. Moreover, MCSNet+ exhibits significant improvements in prediction speed, advancing from 0.055 s/img to 0.133 s/img, and elevates the recognition rates of moving insect sibling species in real wheat storage and visible light, rising from 2.32% to 2.53%. The detection performance of the model on laboratory-captured images surpasses that of real storage facilities, with better results for Tribolium compared to Sitophilus. Although inter-strain variances are less pronounced, the model achieves acceptable detection results across different Tribolium geographical strains, with a minimum recognition rate of 82.64 ± 1.27%. In real-time monitoring videos of grain storage facilities with wheat backgrounds, the enhanced deep learning model based on Convolutional Neural Networks successfully detects and identifies closely related stored-grain pest images. This achievement provides a viable solution for establishing an online pest management system in real storage facilities. © 2023 by the authors.
Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment
- Authors: Qi, Lianyong , Hu, Chunhua , Zhang, Xuyun , Khosravi, Mohammad , Pang, Shaoning
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 6 (2021), p. 4159-4167
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- Description: As one of the cyber-physical-social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial-temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users' context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones. © 2005-2012 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**
S-boxes construction based on quantum chaos and PWLCM chaotic mapping
- Authors: Peng, Jun , Pang, Shaoning , Zhang, Du , Jin, Shangzhu , Feng, Lixiao , Li, Zuojin
- Date: 2019
- Type: Text , Conference paper
- Relation: 18th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2019, Milan, 23-25 July 2019 p. 293-298
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- Description: For a security system built on symmetric-key cryptography algorithms, the substitution box (S-box) plays a crucial role to resist cryptanalysis (decoding). In this paper, we incorporate quantum chaos and PWLCM chaotic mapping into a new method of S-box design. Over the obtained 500 key-dependent S-boxes, we test the S-box cryptographical properties on bijection, nonlinearity, SAC, BIC, differential approximation probability, and sensitivity to the key, respectively. The results show that the cryptographic characteristics of proposed S-Box has met our design objectives and can be applied to data encryption, user authentication and system access control. © 2019 IEEE.