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Leveraging Artificial Intelligence to Reduce Human-Animal Conflict in India

COMMUNICATIONS OF THE ASSOCIATION FOR INFORMATION SYSTEMS 2024年 55卷 336-350页

作者： Pathak, Atul Arun Kaushik, Kapil Gawande, Abhishek Indian Inst Management Strategy & Entrepreneurship Nagpur India Indian Inst Management Decis Sci & Informat Syst Nagpur India

The case focuses on the use of artificial intelligence (AI) and computer vision (CV) technologies to solve a socially complex issue of human-animal conflict in the geographical regions surrounding the Tadoba Andhari Tiger Reserve (TATR) in Maharashtra state in central India. Due to an increasing tiger population, the villages surrounding the TATR often encountered tigers that strayed outside the reserve, sometimes leading to injuries or fatalities either to the humans or the wild animals involved. Valiance, a technology company, collaborated with the Forest department and other local stakeholders to create a computer vision based AI virtual wall system as a pilot project. The technology solution monitored the location 24hrs a day 7 days a week and had the capability of identifying tigers, leopards, and other predators, and sending out automated, real time alerts to help prevent human-animal conflict situations. Based on the outcome of the pilot, the solution could be refined and then a wider rollout could be planned.

关键词： Artificial Intelligence Human-Animal Conflict Mitigation Real-time Mobile Alerts Computer Vision classification algorithm

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Intelligent identification of effective reservoirs based on the random forest classification model

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JOURNAL OF HYDROLOGY 2020年 591卷 125324-125324页

作者： Li, Jieyu Zhong, Ping-an Yang, Minzhi Zhu, Feilin Chen, Juan Liu, Weifeng Xu, Sunyu Hohai Univ Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili 1 Xikang Rd Nanjing 210098 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China

In the real-time operation of a flood control system, identifying effective reservoirs accurately and adaptively is the premise of establishing a mull-reservoir real-time flood control hybrid operation (MRFCHO) model. The existing effective reservoir intelligent reasoning (ERIK) method is based on the inference rules, which causes the identification results to be greatly influenced by the training samples. This paper establishes a random forest classification (RFC) model for identifying effective reservoirs to solve this problem. The performance of the RFC model is evaluated in terms of model stability and model accuracy, and compared with the ERIK model and other machine learning (ML) models. First, different flood samples are used to establish the models to verify the model stability. Then, the expected total cost under cross-validation is taken as the index to evaluate the classification accuracy. Finally, the average increase of expected total cost with cross-validation is proposed to evaluate the criteria importance and analyze the sensitive factors that affect the classification accuracy of the RFC model. The proposed method is applied to a mull-reservoir system in the Huaihe River basin in China. The results indicate that the RFC model has the characteristic of high classification accuracy, low sensitivity to flood samples and high stability. It displays more dominance in the dynamic identification of effective reservoirs compared to other models.

关键词： Multi-reservoir system Flood control operation Effective reservoir identification classification algorithm Random forest

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classification of dry age-related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

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IET IMAGE PROCESSING 2020年第8期14卷 1571-1579页

作者： Mousavi, Elahe Kafieh, Rahele Rabbani, Hossein Isfahan Univ Med Sci Sch Adv Technol Med Student Res Comm Esfahan Iran Isfahan Univ Med Sci Sch Adv Technol Med Med Image & Signal Proc Res Ctr Esfahan Iran

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudates are the most significant signs of these diseases. In this paper, with the aim of automatic classification of DME, AMD, and normal subjects using Optical Coherence Tomography (OCT) images, a dictionary-learning based classification is proposed. The two important issues intended in this approach are avoiding retinal layer segmentation and attempting to mimic the authors' understanding based on normal and abnormal region identifications, considering that the signs of diseases appear in a small fraction of B-Scans. The histogram of oriented gradients feature descriptor was utilized to characterize the distribution of local intensity gradients and edge directions. To capture the structure of extracted features, different dictionary learning-based classifiers are employed. The dataset consists of 45 subjects: 15 patients with AMD, 15 patients with DME, and 15 normal subjects. The proposed classifier leads to an accuracy of 95.13, 100.00, and 100.00% for DME, AMD, and normal OCT images, respectively, only by considering 4% of all B-Scans of a volume, which outperforms the state-of-the-art methods.

关键词： feature extraction vision defects image classification diseases image segmentation eye biomedical optical imaging medical image processing optical tomography dry age-related macular degeneration diabetic macular oedema optical coherence tomography images AMD DME vision loss developed countries retinal layer structure diseases automatic classification classification algorithm retinal layer segmentation normal region identifications abnormal region identifications local intensity gradients dictionary learning-based classifiers normal subjects normal OCT images edge directions extracted features

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On the enumeration and classification of σ-LCD codes over finite commutative chain rings

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DISCRETE MATHEMATICS 2022年第8期345卷

作者： Yadav, Monika Sharma, Anuradha IIIT Delhi Dept Math New Delhi 110020 India

Let R-e denote a finite commutative chain ring with the maximal ideal (u) and the nilpotency index e, and let (R) over bar (e) = R-e/(u) be the residue field of R-e. Let sigma(0) be an automorphism of R-e, and let (sigma) over bar (0) be the corresponding automorphism of the residue field (R) over bar (e) of R-e, defined as sigma(0)(a + (u)) = sigma(0)(a) + (u) for all a + (u) is an element of(R) over bar (e). Let sigma be an automorphism of R-e(n) corresponding to the automorphism sigma(0) of R-e, defined as sigma (v(1), v(2), . . . , v(n)) = (sigma(0)(v(1)), sigma(0)(v(2)), . . . , sigma(0)(v(n))) for all (v(1), v(2), ... , v(n)) is an element of R-e(n). In this paper, we obtain explicit enumeration formulae for all sigma-LCD codes of an arbitrary length over the chain ring R-e when (sigma) over bar (2)(0) is the identity automorphism of (R) over bar (e). With the help of these enumeration formulae and by applying the classification algorithm, we classify all Euclidean LCD codes of lengths 2, 3, 4 and 5 over the chain ring F-2[u]/(u(2)) and of lengths 2, 3 and 4 over the chain ring F-3[u]/(u(2)), and all sigma-LCD codes of lengths 2, 3 and 4 over the chain ring F-4[u]/(u(2)), where sigma(0) is an automorphism of F-4[u]/((u2)) such that the corresponding automorphism sigma 0 of the residue field F4 has order 2. Besides this, we show that the class of sigma-LCD codes over Re is asymptotically good, and that every free linear [n, k, d]-code over Re is equivalent to a sigma-LCD [n, k, d]-code over Re when |(R) over bar (e)| > 4. We also explicitly determine all inequivalent sigma-LCD [n, 1, d]-codes and [n, n - 1, d]-codes over R-e for 1 <= d <= n. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Galois rings classification algorithm Witt decomposition

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Towards a harmonized approach for food authenticity marker validation and accreditation

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TRENDS IN FOOD SCIENCE & TECHNOLOGY 2024年 149卷

作者： Bayen, Stephane Elliott, Chris Arlorio, Marco Ballin, Nicolai Zederkopff Birse, Nicholas Brockmeyer, Jens Chahal, Shawninder Corradini, Maria G. Hanner, Robert Hann, Stephan Laursen, Kristian Holst Mihailova, Alina Steininger-Mairinger, Teresa Suman, Michele Tian, Lei van Ruth, Saskia Xia, Jianguo McGill Univ Dept Food Sci & Agr Chem 21111 Lakeshore Ste Anne De Bellevue PQ Canada Queens Univ Belfast Inst Global Food Secur Sch Biol Sci 19 Chlorine Gardens Belfast North Ireland Thammasat Univ Fac Sci & Technol Sch Food Sci & Technol 99 Mhu 18Pahonyothin Rd Khong Luang 12120 Pathum Thani Thailand Univ Piemonte Orientale Amedeo Avogadro Dept Pharmaceut Sci Food Chem Biotechnol & Nutr Unit Largo Donegani 2 Novara Italy Danish Vet & Food Adm Dept Food Chem Sondervang 4 DK-4100 Ringsted Denmark Univ Stuttgart Inst Biochem & Tech Biochem Dept Food Chem Allmandring 5b Stuttgart Germany Univ Guelph Dept Food Sci Arrell Food Inst 50 Stone Rd E Guelph ON N1G 2W1 Canada Univ Guelph Dept Integrat Biol 50 Stone Rd E Guelph ON N1G 2W1 Canada Arrell Food Inst 50 Stone Rd E Guelph ON Canada Univ Nat Resources & Life Sci BOKU Inst Analyt Chem Dept Chem Muthgasse 18 A-1190 Vienna Austria FFoQSI GmbH Technopk 1D A-3430 Tulln An Der Donau Austria Univ Copenhagen Dept Plant & Environm Sci Thorvaldsensvej 40 DK-1871 Copenhagen C Denmark IAEA Dept Nucl Sci & Applicat Joint FAO IAEA Ctr Nucl Tech Food & Agr Food Safety & Control LabVienna Int Ctr POB 100 A-1400 Vienna Austria Barilla GeR Flli SpA Analyt Food Sci Via Mantova 166 I-43122 Parma Italy Univ Cattolica Sacro Cuore Dept Sustainable Food Proc Piacenza Italy Univ Coll Dublin Sch Agr & Food Sci Belfield Campus Dublin 4 Ireland McGill Univ Inst Parasitol 21111 Lakeshore Ste Anne De Bellevue PQ Canada

Recent publications in the field of food authentication have reported using analytical methods which measure changes in sample composition. These changes can be due to a variety of causes such as the presence of adulterants, different production methods, or varying geographical origins of food. While the increasing use of marker -based approaches is beneficial in combating food fraud, there is a pressing need to adopt a harmonized approach for validating these markers. In this article, we make recommendations for harmonized terminologies and general definitions related to food authenticity markers. First, we propose the terms " primary " and " sec- ondary " markers to distinguish between direct and indirect authentication. The terms " single " and " dual " authenticity markers, and authentic " profiles " and " fingerprints " are suggested to distinguish between the number of analytical targets used. We also recommend that the terms: " threshold ", " binary ", and " interval " markers are applied depending on how they discriminate authentic from non -authentic samples. Second, we advocate for harmonization in marker discovery approaches. A summary of the main analytical techniques, published guidelines, data repositories, and data analysis approaches is presented for various marker classes while also stating their applicability and limitations. Finally, we propose guidelines for the analytical community concerning marker validation. In our view, the validation of the authentication method should include the following steps: 1) applicability statement;2) experimental design;3) marker selection and analysis;4) analytical method validation;5) method release;6) method monitoring. Implementing these approaches will represent a significant step towards establishing a wide range of fully validated and accredited methodologies that can be applied effectively in food authenticity monitoring and control programs.

关键词： Accreditation classification algorithm Food fraud Foodomics Machine learning Non-targeted analysis

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DBRF: Random Forest Optimization algorithm Based on DBSCAN

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第9期15卷 354-362页

作者： Zhuo, Wang Ahmad, Azlin Univ Teknol MARA Coll Comp Informat & Math Sch Comp Sci Shah Alam Malaysia

correlation and redundancy of features will directly affect the quality of randomly selected features, weakening the convergence of random forests (RF) and reducing the performance of random forest models. This paper introduces an improved random forest algorithm-A Random Forest algorithm Based on DBSCAN (DBRF). The algorithm utilizes the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to improve the feature extraction process, to extract a more efficient feature set. The algorithm first uses DBSCAN to group all features based on their relevance and then selects features from each group in proportion to construct a feature subset for each decision tree, repeating this process until the random forest is built. The algorithm ensures the diversity of features in the random forest while eliminating the correlation and redundancy among features to some extent, thereby improving the quality of random feature selection. In the experimental verification, the classification prediction results of CART, RF, and DBRF, three different classifiers, were compared through ten-fold cross-validation on six different-sized datasets using accuracy, precision, recall, F1, and running time as validation indicators. Through experimental verification, it was found that DBRF algorithm outperformed RF, and the prediction performance was improved, especially in terms of time complexity. This algorithm is suitable for various fields and can effectively improve the classification prediction performance at a lower complexity level.

关键词： Random forest DBSCAN feature selection feature redundancy classification algorithm

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A computational platform to identify origins of replication sites in eukaryotes

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BRIEFINGS IN BIOINFORMATICS 2021年第2期22卷 1940-1950页

作者： Dao, Fu-Ying Lv, Hao Zulfiqar, Hasan Yang, Hui Su, Wei Gao, Hui Ding, Hui Lin, Hao Univ Elect Sci & Technol China Ctr Informat Biol Chengdu 610054 Peoples R China

The locations of the initiation of genomic DNA replication are defined as origins of replication sites (ORIs), which regulate the onset of DNA replication and play significant roles in the DNA replication process. The study of ORIs is essential for understanding the cell-division cycle and gene expression regulation. Accurate identification of ORIs will provide important clues for DNA replication research and drug development by developing computational methods. In this paper, the first integrated predictor named iORI-Euk was built to identify ORIs in multiple eukaryotes and multiple cell types. In the predictor, seven eukaryotic (Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Pichia pastoris, Schizosaccharomyces pombe and Kluyveromyces lactis) ORI data was collected from public database to construct benchmark datasets. Subsequently, three feature extraction strategies which are k-mer, binary encoding and combination of k-mer and binary were used to formulate DNA sequence samples. We also compared the different classification algorithms' performance. As a result, the best results were obtained by using support vector machine in 5-fold cross-validation test and independent dataset test. Based on the optimal model, an online web server called iORI-Euk (http://***/server/iO RI- Euk/) was established for the novel ORI identification.

关键词： origins of replication site eukaryote feature extraction webserver classification algorithm

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Combination of NIR spectroscopy and algorithms for rapid differentiation between one-year and two-year stored rice

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023年第1期291卷 122343页

作者： Shi, Shijie Feng, Junheng Yang, Lichao Xing, Junyang Pan, Gaofeng Tang, Jichao Wang, Jing Liu, Juan Cao, Cougui Jiang, Yang Huazhong Agr Univ Coll Plant Sci & Technol Wuhan 430070 Peoples R China Huazhong Agr Univ Shuangshui Shuanglu Inst Wuhan 430070 Peoples R China Cangzhou Acad Agr & Forestry Sci Cangzhou 061001 Peoples R China Xiangyang Acad Agr Sci Xiangyang 441022 Peoples R China Huazhong Agr Univ Coll Econ & Management Wuhan 430070 Peoples R China

Storage is necessary for rice to ensure the year-round consumption of rice. With the increase in storage time, the taste quality and commercial value of rice gradually decrease. The accurate determination of the freshness of rice is critical to the rice trade. However, it is difficult to distinguish aging rice from fresh rice, so a quick and simple method is needed to identify the freshness of the rice. In this study, a combination of near-infrared spectroscopy (NIR) and various algorithms, such as partial least squares discriminant analysis (PLS-DA), support vector ma-chines (SVM), and classification and regression trees (CART), were used to differentiate the freshness of rice. PLS-DA and SVM demonstrated excellent classification ability in identifying the freshness of rice, with sensitivity and specificity of 1. The original spectra were used with 100% accuracy in the test set to determine the freshness of the rice. As a result, PLS-DA and SVM can be used to determine the freshness of the rice.

关键词： Rice classification algorithm Storage time Near-infrared spectroscopy

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Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review

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PROCESSES 2023年第8期11卷

作者： Fan, Zeyu He, Ziju Miao, Wenjun Huang, Rongrong Nanjing Tech Univ Sch Pharmaceut Sci Nanjing 211816 Peoples R China

The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms between each model, but have reached controversial conclusions. Thus, the performance of current machine-learning-based gastric cancer risk prediction models alongside the clinical relevance of different predictive factors needs to be evaluated to help build more efficient and feasible models in the future. In this systematic review, we summarize the current research progress related to the gastric cancer risk prediction model;discuss the predictive factors and methods used to construct the model;analyze the role of important predictive factors in gastric cancer, the preference of the selected classification algorithm, and the emphasis of evaluation criteria;and provide suggestions for the subsequent construction and improvement of the gastric cancer risk prediction model. Finally, we propose an improved approach based on the ethical issues of artificial intelligence in medicine to realize the clinical application of the gastric cancer risk prediction model in the future.

关键词： gastric cancer predictive factors risk prediction model machine learning classification algorithm

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Interpretable CNN for ischemic stroke subtype classification with active model adaptation

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2022年第1期22卷 3-3页

作者： Zhang, Shuo Wang, Jing Pei, Lulu Liu, Kai Gao, Yuan Fang, Hui Zhang, Rui Zhao, Lu Sun, Shilei Wu, Jun Song, Bo Dai, Honghua Li, Runzhi Xu, Yuming Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China Zhengzhou Univ Cooperat Innovat Ctr Internet Healthcare Zhengzhou Peoples R China Deakin Univ Inst Intelligent Syst Burwood NSW Australia Zhengzhou Univ Affiliated Hosp 1 Dept Neurol Zhengzhou Peoples R China

Background TOAST subtype classification is important for diagnosis and research of ischemic stroke. Limited by experience of neurologist and time-consuming manual adjudication, it is a big challenge to finish TOAST classification effectively. We propose a novel active deep learning architecture to classify TOAST. Methods To simulate the diagnosis process of neurologists, we drop the valueless features by XGB algorithm and rank the remaining ones. Utilizing active learning framework, we propose a novel causal CNN, in which it combines with a mixed active selection criterion to optimize the uncertainty of samples adaptively. Meanwhile, KL-focal loss derived from the enhancement of Focal loss by KL regularization is introduced to accelerate the iterative fine-tuning of the model. Results To evaluate the proposed method, we construct a dataset which consists of totally 2310 patients. In a series of sequential experiments, we verify the effectiveness of each contribution by different evaluation metrics. Experimental results show that the proposed method achieves competitive results on each evaluation metric. In this task, the improvement of AUC is the most obvious, reaching 77.4. Conclusions We construct a backbone causal CNN to simulate the neurologist process of that could enhance the internal interpretability. The research on clinical data also indicates the potential application value of this model in stroke medicine. Future work we would consider various data types and more comprehensive patient types to achieve fully automated subtype classification.

关键词： Interpretability Ischemic Stroke Active learning classification algorithm Loss function

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