Integrating Artificial Intelligence (AI) tools in Bangladesh's academic landscape has ignited concerns about potentially eroding students' creative writing and critical thinking abilities. While AI ...offers efficient and personalized learning, there is a looming risk of students using it as a shortcut to success. Educators and policymakers must emphasize the cultivation of writing skills and critical thinking while guiding students to recognize the limitations of AI. While plagiarism checking is crucial for academic integrity, it often falls short in acknowledging students' originality. Natural Language Processing (NLP) emerges as a promising solution, augmenting plagiarism detection by comprehending context, nuances, and novel expressions. This paper proposed a model based on NLP that can incorporate a robust token identification mechanism into AI tools used by the students. A unique identification token is generated at the time of system login and stored in a publicly accessible resource, acting as a distinct marker for user activity. These identification tokens support a plagiarism detection system that continually reviews and compares activity records. It also enables a thorough examination of user involvement across various academic supplementary tools, such as academic writing, presentation creation, image generation, etc.
Summary
Necrosis‐ and ethylene‐inducing peptide 1 (Nep1)‐like proteins (NLPs) constitute a superfamily of proteins toxic to dicot plants, but the molecular basis of this toxicity remains obscure.
...Using quantitative trait locus (QTL) analysis we investigated the genetic variation underlying ion leakage in Arabidopsis plants elicited with MoNLP1 derived from Magnaporthe oryzae. The QTL conditioning MoNLP1 toxicity was positionally cloned and further characterized to elucidate its mode of action.
MoNLP1‐triggered cell death varied significantly across > 250 Arabidopsis accessions and three QTLs were identified conferring the observed variation. The QTL on chromosome 4 was uncovered to encode a leucine‐rich repeat (LRR)‐only protein designated as NTCD4, which shares high sequence identity with a set of nucleotide‐binding LRR proteins. NTCD4 was secreted into the apoplast and physically interacted with multiple NLPs. Apoplastic NTCD4 facilitated the oligomerization of NLP, which was closely associated with toxicity in planta. The natural genetic variation causing D3N change in NTCD4 reduced the secretion efficiency of NTCD4 and the infection of Botrytis cinerea on Arabidopsis plants.
These observations demonstrate that the plant‐derived NTCD4 is recruited by NLPs to promote toxicity via facilitating their oligomerization, which extends our understanding of a key step in the toxic mode of action of NLPs.
in the last years, the relevance of sentiment analysis is broad and dominant. The capability to take out insights from social data is a tradition that is being extensively accepted by all over globe. ...Sentiment Analysis has turn out to be a hot-trend issue of technical and marketplace research in the area of Natural Language Processing (NLP) and Machine Learning. Sentiment analysis is enormously useful in social media supervising as it permits us to expand an impression of the wider open estimation behind definite topics. Investigation of social media streams is typically limited to just essential sentiment analysis and count based metrics. This is of the same kind to just scratching the outside and missing out on those elevated value insight that is ahead of you to be discovered. There’s a lot of effort to be done, but perfections are being prepared every day. It is a way to appraise on paper or verbal language to settle on if the expression is favorable, unfavorable, or unbiased, and to what level. Today’s algorithm-based sentiment analysis tools can touch vast amount of client response constantly and precisely. Balancing with text analytics, sentiment analysis exposes the customer’s estimation concerning topics ranging from your goods and services to your position, your advertisements, or even your challengers. These efforts scrutinize the crisis of studying texts, like posts and reviews, uploaded by user on Twitter. The Support Vector Machine (SVM), k-nearest neighbors algorithm (KNN) and proposed optimized feature sets model is offered to progression the tweet features and to recognize the out of sight sentiments from these tweets. These essential concepts when used in combinations become a very significant tool for analyzing millions of variety conversations with human echelon accurateness. The projected optimized feature sets model Sentiment Analysis exercise the assessment metrics of Precision, Recall, F-score, and Accuracy. Also, average measures weighted F1-scores are constructive for categorization of Positive, Negative and Neutral multi-class problems. The running time of the technique is evaluates by accomplishing diverse methods in the same investigational setup consisting a cluster of 8 nodes. Planned optimized feature sets model Sentiment Analysis reachs 82 % accuracy as compare with SVM 78.6 % and KNN 75 %. Further, while analyzing sentiments of tweets we have measured only tweets in English acknowledged by Twitter streaming API.
Learning from data streams in the presence of concept drift is among the biggest challenges of contemporary machine learning. Algorithms designed for such scenarios must take into an account the ...potentially unbounded size of data, its constantly changing nature, and the requirement for real-time processing. Ensemble approaches for data stream mining have gained significant popularity, due to their high predictive capabilities and effective mechanisms for alleviating concept drift. In this paper, we propose a new ensemble method named Kappa Updated Ensemble (KUE). It is a combination of online and block-based ensemble approaches that uses Kappa statistic for dynamic weighting and selection of base classifiers. In order to achieve a higher diversity among base learners, each of them is trained using a different subset of features and updated with new instances with given probability following a Poisson distribution. Furthermore, we update the ensemble with new classifiers only when they contribute positively to the improvement of the quality of the ensemble. Finally, each base classifier in KUE is capable of abstaining itself for taking a part in voting, thus increasing the overall robustness of KUE. An extensive experimental study shows that KUE is capable of outperforming state-of-the-art ensembles on standard and imbalanced drifting data streams while having a low computational complexity. Moreover, we analyze the use of Kappa versus accuracy to drive the criterion to select and update the classifiers, the contribution of the abstaining mechanism, the contribution of the diversification of classifiers, and the contribution of the hybrid architecture to update the classifiers in an online manner.
Penggunaan aplikasi chatbot telah berkembang pesat dalam berbagai bidang, mulai dari layanan pelanggan hingga bantuan interaktif dalam berbagai platform. Dalam rangka untuk meningkatkan kualitas dan ...responsivitas chatbot, penelitian ini fokus pada pemanfaatan Natural Language Processing (NLP) sebagai metode utama. NLP adalah teknologi yang memungkinkan komunikasi yang lebih alami antara manusia dan komputer, dan dengan demikian memungkinkan chatbot untuk memahami dan merespons permintaan pengguna dengan lebih akurat. Studi ini mencakup pengembangan aplikasi chatbot yang menggabungkan teknik NLP terkini, termasuk pemrosesan bahasa alami, analisis sentimen, dan pemahaman konteks. Hasilnya menunjukkan peningkatan signifikan dalam kinerja chatbot, meningkatkan interaksi pengguna dengan aplikasi, dan meningkatkan tingkat kepuasan pengguna. Tujuan dari penelitian ini untuk meringankan pekerjaan staff administrasi dalam memberikan informasi serta memudahkan mahasiswa dan calon mahasiswa dalam mendapat informasi yang ada saat ini. Sehingga dalam memberikan dan mendapatkan informasi menjadi lebih mudah dan efisien. Pengumpulan data berupa studi pustaka, wawancara dan observasi. Metode kuantitatif digunakan dalam proses penelitian ini untuk digunakan dalam melakukan testing. Sistem diuji menggunakan black box testing dengan berjalan sukses serta dilakukan User Acceptance Test (UAT) dengan memperoleh nilai rata- rata sebesar 88,6% dari respon mahasiswa dengan predikat “Sangat Setuju” yang berarti pengguna puas dengan aplikasi chatbot ini. Hasil penelitian ini membuktikan bahwa aplikasi chatbot menggunakan metode NLP ini dapat digunakan untuk mempermudah mahasiswa dan calon mahasiswa mendapatkan informasi yang mereka inginkan.
The F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning ...the appropriateness of combining two aspects of performance as conceptually distinct as precision and recall, and also questioning whether the harmonic mean is the best way to combine them. To ease this concern, we describe a simple transformation of the F-measure, which we call
F
∗
(F-star), which has an immediate practical interpretation.
For the first time, systematic research of auxiliary selection in Italian is proposed using corpus analysis and natural language processing (NLP). By combining these methods, we seek to find the most ...significant factors that influence the choice of auxiliary in intransitive verbs with double auxiliation. These verbs have often been studied in the literature (e.g., peripheral verbs Sorace 2000), but they have never been addressed in a comprehensive way (Giancarli 2015). The findings emphasize the most significant factors influencing the choice of ‘be’ or ‘have’ based on semantic, syntactic, and morphological aspects. On the basis of corpus analysis and statistical tools (CHAID and Random Forest) evidence, we propose the internal cause and the human trait as the possible factors useful in untangling the knot of auxiliary selection within Italian verbs with double auxiliation. This article also presents a reflection on semi-auxiliary verbs, a particular group of Italian verbs that operate as semi-auxiliary by being followed by an infinitive. For this group of verbs, we propose that auxiliary selection depends not only on the semantics of the verb or of the subject, but mainly on the auxiliary selection of the infinitive.
•Propose a new deep learning-based algorithm for calculating land use types in urban flooded areas.•Socio-economic risk differences in urban flooding locations are considered.•Compute the geographic ...semantic properties of land in urban flooding areas.
The aggregation of the same type of socio-economic activities in urban space generates urban functional zones, each of which has one function as the main (e.g., residential, educational or commercial), and is an important part of the city. With the development of deep learning technology in the field of remote sensing, the accuracy of land use decoding has been greatly improved. However, no finer remote sensing image could directly obtain economic and social information and it has a high revisit cycle (low temporal resolution), while urban flooding often lasts only a few hours. Cities contain a large amount of “social sensing” data that records human socio-economic activities, and GIS is a natural discipline with strong socio-economic ties. We propose a new GeoSemantic2vec algorithm for urban function recognition based on the latest advances in natural language processing technology (BERT model), which utilizes the rich semantic information in urban POI data to portray urban functions. Taking the Wuhan flooding event in summer 2020 as an example, we identified 84.55% of the flooding locations in social media. We also use the new algorithm proposed in this paper to divide the main urban area of Wuhan into 8 types of urban functional zones (kappa coefficient is 0.615) and construct a “City Portrait” of flooding locations. This paper summarizes the progress of existing research on urban function identification using natural language processing techniques and proposes a better algorithm, which is of great value for urban flood location detection and risk assessment.
Advancements in neural networks have led to developments in fields like computer vision, speech recognition and natural language processing (NLP). One of the most influential recent developments in ...NLP is the use of word embeddings, where words are represented as vectors in a continuous space, capturing many syntactic and semantic relations among them. AraVec is a pre-trained distributed word representation (word embedding) open source project which aims to provide the Arabic NLP research community with free to use and powerful word embedding models. The first version of AraVec provides six different word embedding models built on top of three different Arabic content domains; Tweets, World Wide Web pages and Wikipedia Arabic articles. The total number of tokens used to build the models amounts to more than 3,300,000,000. This paper describes the resources used for building the models, the employed data cleaning techniques, the carried out preprocessing step, as well as the details of the employed word embedding creation techniques.
Data Mining; Text Mining; Health Informatics; Health Care Information Systems; Medical Terminologies; Natural Language Processing; Text Analysis; Support Vector Machines
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