Adopting and implementing Agile principles and practices in a software development project is not new these days. With the flexibility to manage changes in requirements and the rapid delivery of work products, many or...
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This paper outlines the process of generating a Neo4j graph database powered by language Models (LLMs). The primary goal is to extract structured information from unstructured data, including user profiles, paper brie...
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ISBN:
(数字)9798331515683
ISBN:
(纸本)9798331515690
This paper outlines the process of generating a Neo4j graph database powered by language Models (LLMs). The primary goal is to extract structured information from unstructured data, including user profiles, paper briefs, and Slack messages, and convert them into Cypher queries. The data is then ingested into Neo4j to build a graph database that captures relationships between users, paper, technologies, and messages. A pipeline was developed to automate the process, ensuring accurate entity and relationship extraction using predefined templates. This approach allows for efficient data representation and supports consultancy in managing large datasets by generating insightful visualizations and querying capabilities.
With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by nov...
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ISBN:
(数字)9798350353648
ISBN:
(纸本)9798350353655
With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment.
Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or...
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Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect *** address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying *** approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic *** evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect *** BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)***,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous *** work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.
Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the comple...
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Sign language is a non-verbal communication method used to communicate between hard of hearing or deaf and ordinary people. Automatic Sign language detection is a complex computer vision problem due to the diversity o...
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Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions $(\text{TLA}^{+}...
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ISBN:
(数字)9798350351507
ISBN:
(纸本)9798350363067
Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions $(\text{TLA}^{+})$ , into computerscience education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computerscience programs, (2) construct level-appropriate examples that could be included midway into one's undergraduate studies, (3) demonstrate how to address successive “failuresy” through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding $\text { TLA }^{+}$ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in $\text { TLA }^{+}$ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudi
Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inade...
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Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.
With the existing deep learning models in predicting multiple diseases primarily focus on analyzing individual diseases in isolation, lacking a unified system for multi-disease prediction. This project presents an app...
With the existing deep learning models in predicting multiple diseases primarily focus on analyzing individual diseases in isolation, lacking a unified system for multi-disease prediction. This project presents an approach to predict multiple diseases using Flask API, with a specific focus on brain tumors, COVID-19 and pneumonia. The proposed work represents a significant contribution to the field of disease prediction, harnessing the power of deep learning algorithms and modern web application development. The primary focus is on disease prediction, with a particular emphasis on ensuring accuracy and accessibility for end-users. The initial phase of this research involves data collection, where relevant datasets of various diseases are gathered. These datasets serve as the foundation for training and validating the deep learning models. Two prominent deep learning algorithms, Sequential CNN and VGG16, are employed for this purpose. These algorithms are chosen for their ability to handle complex data and recognize patterns within medical images and other health-related data. The core of the research involves training the deep learning models using the collected datasets. This step is crucial in enabling the models to learn and generalize from the provided data, ultimately enhancing their predictive capabilities. The models are modified to elevate their performance and accuracy. Following the training phase, the models are rigorously tested to evaluate their predictive accuracy. This assessment is vital in gauging the real-world applicability of the models in medical diagnosis. To make these powerful disease prediction models accessible to a wider audience, a front-end web application is developed.
This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external need...
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