Purpose. The purpose of this study is to explore vectorization as an engineering technique to improve the performance and readability of Python code, particularly in data processing tasks. We aim to demonstrate the benefits of vectorization through practical examples involving the handling of missing data. Design / Method / Approach. To achieve the research goals, we performed a comparative analysis between loop-based and vectorized implementations. Specifically, two versions of a function were developed to identify columns containing missing values within (...) a dataset. These implementations were tested on two real-world datasets. We compared execution time and code readability. Findings. The findings showed that vectorization resulted in substantial performance improvements, reducing execution time by hundreds of times compared to traditional loop-based methods. Additionally, the vectorized code was more compact, leading to greater readability and ease of maintenance. Theoretical Implications. Vectorization provides a higher level of abstraction for performing operations on data structures. This allows developers to focus on algorithmic logic rather than managing iterative control structures, contributing to broader discussions on optimizing computational efficiency in Python. Practical Implications. For data engineers and analysts, vectorization represents a highly effective solution for optimizing Python code. It significantly accelerates data-intensive tasks, such as missing data imputation, data analysis, and machine learning, making it an essential tool for enhancing productivity in data-driven environments. Originality / Value. This study presents a practical approach to optimizing Python code through vectorization. It is valuable for professionals seeking to improve efficiency in their workflows. Research Limitations / Future Research. The limitation of this research lies in its focus on a single problem – missing data imputation. Future research should expand the scope to other computational areas, such as image processing and simulation modeling, or examine the use of vectorization alongside Just-In-Time (JIT) compilation using tools like Numba to further boost Python's performance. Paper Type. Practitioner Paper. (shrink)

This essay utilizes the work of the comedy group, Monty Python, as a means of introducing basic concepts in Existentialism, especially as it pertains to the writings of Nietzsche, Sartre, and Camus.

Evoke is a Python library for evolutionary simulations of signalling games. It offers a simple and intuitive API that can be used to analyze arbitrary game-theoretic models, and to easily reproduce and customize well-known results and figures from the literature.

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using (...) deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. (shrink)

The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [19] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [19]. This paper reports on (...) an open-source reimplementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of ‘echo chambers’ on online social media, as well as evidence of interdisciplinary diversity in an academic communications network. (shrink)

Wool production industries rely heavily on accurate demand forecasting to manage supply chains and production schedules effectively. Predicting wool demand allows companies to avoid overproduction and resource wastage while meeting market needs efficiently. Traditional forecasting models often struggle with the seasonality and variability of wool demand. The ARIMA (Auto Regressive Integrated Moving Average) model, a time series forecasting technique, is particularly suited for this task due to its ability to capture both trends and seasonal fluctuations in historical data. The data (...) for this research was collected from official records of wool demand across various Indian states for 2016-2023. After data pre-processing (cleaning, sorting, and organizing), the ARIMA model was implemented using Python for analysis and forecasting. Flask was employed to create a web-based interface, allowing users to interact with the predictive model easily. The model parameters (p, d, q) were fine-tuned to fit the data and generate accurate forecasts. The ARIMA model demonstrated a strong ability to predict wool demand, achieving a low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model's forecasts were closely aligned with actual demand figures across different states, capturing both long-term trends and seasonal variations. Graphical representations showed minimal deviation between predicted and actual values, validating the accuracy of the model. The implementation of the ARIMA model for wool demand forecasting has significant practical implications for production planning in the textile industry. Companies can use the system to optimize production schedules, reduce costs, and minimize wastage. This methodology can be extended to other commodities where seasonal demand plays a critical role, offering a versatile tool for various sectors. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...) to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three (...) key questions: i) How can the (causal) dependencies in games - either between variables, or between strategies - be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library. (shrink)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms that (...) allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

Abstract: Dates are the fruit of date palm trees, and it is one of the fruits famous for its high nutritional value. It is a summer fruit spread in the Arab world. In the past, the Arabs relied on it in their daily lives. Dates take an oval shape and vary in size from 20 to 60 mm in length and 8 to 30 mm in diameter. The ripe fruit consists of a hard core surrounded by a papery cover called (...) the tartar that separates the core from the fleshy part that is eaten. Historians disagreed about the place of its origin, so some historians expressed their belief that it had originated around the Arabian Gulf, and some of them say that the oldest known information about palm trees was in Babylon 4 thousand years ago BC. It is known about the ancient Egyptians using dates in wine. Dates contain a high nutritional value and are considered a basic food for man since ancient times. The fruits of dates are considered the highest fruits in containing sugars. These components vary according to the nature of the fruit, whether it is wet, semi-dry or dry, as well as according to the environmental conditions surrounding the trees. The components of the fruits also differ in different varieties and increase the percentage of sugars in dates is 70-78% of the fruit’s components. These sugars are characterized by their rapid absorption and transfer into the blood directly, digestion and burning. 10 dates (about 100 grams) per day provide a person with all his daily needs of magnesium, manganese, copper, sulfur and half of his needs of calcium and potassium. In this paper we presented a system that recognize the nine types of dates based on deep learning using python on Colab, and classifying using a dataset contain 1350 images. Our trained model achieved an accuracy of 99.44% on a proven test set. (shrink)

This research work presents a cost-effective facial recognition alert system using the ESP32 microcontroller to enhance real-time security. The system integrates high-resolution cameras with Python’s facial recognition libraries to identify individuals. When an unknown face is detected, it triggers an immediate alert via the ESP32, activating an alarm. This solution is both scalable and affordable, making advanced security accessible to small businesses and residential areas. The system improves overall security by automating surveillance, reducing the need for manual monitoring.

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Building upon the works of Gödel, Zalta ; and Benzmüller and Paleo, this paper introduces a formal system and testable system for Metaphysical Cosmology, referring to the study of the nature of existence, non-existence, and their interplay. The aim is to integrate metaphysics into a testable scientific framework, beyond speculative reasoning. The system abides by three principles which serve as a foundation for implementing a scientific methodology in metaphysics: (i) axioms must be minimized, incorporating Cartesian-like skepticism ; (ii) theorems must (...) be inferred through proof-theoretic derivations ; (iii) theorems must be tested experimentally through computation. The system borrows from type-theoretic concepts and introduces new logical notions as tools for Metaphysical Cosmology, such as a metric-space-based predicate system, and a revised version of the existential quantifier. These tools offer an exhaustive approach to metaphysical entities and provide mechanisms to address contradictions. Formulae and equations depicting the structure of the Metaphysical Cosmos are inferred from the system through proof-theoretic derivations. Theorems inferred from the system have been tested using the theorem-prover Z3, encoded in Python, which determines whether a formula is satisfiable. While testing a theorem, its negation is inputted in Z3, and if the output displays that its negation is satisfiable, then the theorem is falsified and rejected, otherwise it is accepted as valid. This method thus follows the falsifiability criteria, as per Karl Popper, reinforcing testable scientific methodology in metaphysics. The code used in computational experimentation is given in full in the appendix for reproducibility. (shrink)

Most research on image schema examined the meaning configuration of words connotation. However, previous studies of adjectives are meaningful in cognitive linguistics because they provide insight into how those adjectives are involved with psychological movement. In this sense, from the perspective of cognitive linguistics, one’s conceptualization and cognition are closely associated with their bodily experience and surroundings; adjectives are no exception. The varieties of transformations of image schemas lay the foundation for the conception and perception. Accordingly, this study is an (...) investigation into the perception of adjectives and psychological activities associated with image schema. Data in this study come from adjectives in the novel The Great Gatsby, which were tagged and calculated with the help of software Python and the NLTK package. The results demonstrate that a significant amount of adjectives were used in the novel. Moreover, Nick’s psychological activities changed significantly from the SOURCE to the DESTINATION schematic stage, which is uncovered by his expression of adjectives. Finally, the results further suggest that transformations of image schema as BALANCE, CONTAINER, and PATH work together to motivate one’s linguistic expression of adjectives. The present findings have several implications for research on lexicons and literacy interpretation from cognitive linguistics. (shrink)

The thesis is a metatheoretical analysis of the concept of ‘speaker’s intention’ as it is used in traditional linguistic-philosophical and in cognitive pragmatics. The analysis centers around works of Austin, Searle, Grice, and Relevance Theory. The main aim is to argue for the following thesis: (T1) if pragmatics is targeting on how speaker’s intentions contribute to linguistic choices in communicative language use, then focusing solely on causally efficient mental states and analyzing them at the utterance level necessarily leads to unsatisfactory (...) consequences in theorizing. -/- The secondary aim of the dissertation is to argue that: (T2) the traditional account of intentionality is tenable only if we take utterances as data. Surely, there are causally efficient intentions which lead the speaker to formulate her utterances as part of conversational turns. But speakers also have intentions which shape the conversation itself (motives, goals, plans). However: (T3) if we take discourses or conversations as divergent sources of data (besides of utterances), we run into an explanatory gap that cannot be filled mechanically: the structured-proposionalist theory is simply insufficient to grasp discourse-level intentions. To argue for (T3), I analyse speaker’s intentions in a fictive conversation (The smuggler sketch from Monty Python’s Flying Circus) through the “prisms” of Austin’s speech act theory, Searle’s (at least) two theories of intentions, Grice’s M-intention, and the ostensive-inferential view on communication. (shrink)

In the era of big data and distributed ecosystems, understanding the origin, flow, and transformation of data across complex infrastructures is critical for ensuring transparency, accountability, and informed decision-making. As data-driven enterprises increasingly rely on hybrid cloud architectures, data lakes, and real-time pipelines, the complexity of tracking data movement and transformations grows exponentially. Traditional data lineage solutions, often based on static metadata extraction or rule-based approaches, are insufficient in dynamically evolving environments and fail to provide granular, context-aware insights. This research (...) introduces an AI-augmented, cognitive graph-based framework for autonomous data lineage, designed to enhance data traceability in large-scale and heterogeneous data ecosystems. The framework leverages cutting-edge machine learning (ML), natural language processing (NLP), and graph-based reasoning techniques to enable intelligent discovery, semantic interpretation, and continuous monitoring of data assets throughout their lifecycle. The core of the solution lies in the construction of a dynamic cognitive graph that represents relationships between datasets, processes, systems, and users, enriched with contextual annotations and temporal dimensions. Our architecture incorporates self-learning mechanisms, enabling adaptive lineage discovery and automated anomaly detection. By applying reinforcement learning and stream analytics, the framework not only maps data flows in real time but also evolves with system changes, schema variations, and business logic updates. It provides both forward and backward traceability, supports impact analysis, and enhances compliance auditing capabilities. Furthermore, our system is capable of processing both structured and unstructured metadata, employing advanced NLP models to extract implicit lineage information from data dictionaries, SQL queries, and documentation. The result is a holistic, intelligent, and scalable data lineage solution that reduces manual intervention, mitigates operational risks, and supports regulatory compliance frameworks such as GDPR, HIPAA, and SOX. Experimental evaluations conducted in hybrid cloud environments using tools such as Apache Kafka, Neo4j, Spark, and Python-based ML libraries demonstrate a significant improvement in lineage coverage, anomaly detection accuracy, and system scalability. Compared to conventional lineage tools, our AI-augmented framework delivers a 30% increase in traceability precision and a 40% reduction in manual effort required for lineage tracking and governance. This research lays the foundation for a new paradigm in data governance, where AI not only enhances observability but enables autonomous cognition within data infrastructure. The framework is poised to play a critical role in enabling data democratization, operational agility, and enterprise-wide data literacy. (shrink)

In the way that First Born and the grizzly bear woman once shared a home, or the young girl who happily married the python man, perhaps conservation and preservation is about nurturing and sustaining relationships–relationships of reciprocity, respect, and shared existence with all living beings. Indigenous knowledge reminds us that humans are not separate from nature but integral to its fabric. As we are facing environmental crises like climate change and biodiversity loss, Indigenous knowledge and traditional practices offer a (...) pathway toward a more sustainable and harmonious future founded on respect, reciprocity, and responsibility. (shrink)

This paper explores the mathematical details behind the geometric transformation of folding a circle into a cone. It includes detailed theo- rems, proofs, and Python implementations for visualization, providing a thorough analysis of the transformation process.

This book serves as an introduction to mathematical logic for STEM (Science, Technology, Engineering, and Mathematics) students, written for undergraduates (in particular, 1st and 2nd year undergraduates). A focus on this book is on logical thinking, not simply rote memorization, with a focus on examples and analogies relevant to students aimed at becoming technical leaders and problem solvers. This book includes propositional logic, set theory, functions and relations, and more, with coding examples provided in the Python programming language.

This study focuses on developing a comprehensive sentiment analysis framework aimed at understanding sentiments expressed in social media posts, enhancing online reputation management for brands. Given the overwhelming volume of user-generated content across platforms, we instituted a methodical approach leveraging advanced machine learning techniques. Specifically, we used Python libraries such as TensorFlow for deep learning functionalities and PyTorch for natural language processing tasks. Our models classify sentiments into three categories: positive, negative, and neutral, while simultaneously analyzing trending patterns and (...) user opinions. The results demonstrate a significant classification accuracy, providing evidence for the framework’s efficacy in real-time social media monitoring. Ultimately, our findings emphasize the strategic role of sentiment analysis in improving brand reputation, enabling better consumer engagement, and informing marketing strategies. (shrink)

With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can beutilized for earlier (...) evaluating. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like ClusterCentroids, RepeatedENN, AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold and NearMiss. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree classifier tends to retain 98% before and after feature scaling for the underrsampling with EditedNearestNeighbours, RepeatedENN and AllKNN methods. (shrink)

Study shows that mask-wearing is a critical factor in stopping the COVID-19 transmission. By the time of this article, most states have mandated face masking in public space. Therefore, real-time face mask detection becomes an essential application to prevent the spread of the pandemic. This study will present a face mask detection system that can detect and monitor mask-wearing from camera feeds and alert when there is a violation. The face mask detection algorithm uses a haar cascade classifier to find (...) facial features from the camera feed and then utilizes it to detect the mask-wearing status. With the increasing number of cases all over the world, a system to replace humans to check masks on the faces of people is greatly needed. This system satisfies that need. This system can be employed in public places like transport stations and malls. It will be of great help in companies and huge establishments where there will be a lot of workers. Alongside this, we have used basic concepts of transfer learning in neural networks to finally output the presence or absence of a face mask in an image or a video stream. Experimental results show that our model performs well on the test data with 100 percent and 99 percent precision and recall, respectively. We are making a savvy framework that will identify whether the specific user has worn the mask or not and further tell the accuracy level of the detection. Our framework will be python and AI-based which will be safe and quick for implementation. This system will be of great help there because it is easy to obtain and store the data of the employees working in that Company and will very easily find the people who are not wearing the mask and a will be sent to that respective person to take Precautions not wearing a mask. Potential delays are analyzed, and efforts are made to reduce them to achieve real-time detection. The experiment result shows that the presented system achieves a real-time 45fps 720p Video output, with a face mask detection response of 0.15s and the accuracy of detection 99%. (shrink)

The purpose of this research is to develop an automated attendance management system using Python with a graphical user interface (GUI) and OpenCV for face recognition. The system provides a contactless, efficient, and reliable solution to tracking attendance in institutions such as schools, universities, or workplaces. This paper outlines the system architecture, development process, implementation challenges, and the performance evaluation of the system.

Revista IT & C este o publicație trimestrială din domeniile tehnologiei informației și comunicații, și domenii conexe de studiu și practică. -/- Cuprins: -/- EDITORIAL Cu sau fără Internet?, de Nicolae Sfetcu -/- TEHNOLOGIA INFORMAȚIEI Tehnologia blockchain, de Nicolae Sfetcu -/- TELECOMUNICAȚII Rețelele de comunicații 5G, de Nicolae Sfetcu -/- INTERNET Megadatele (Big Data) pe Internet, de Nicolae Sfetcu -/- SOFTWARE Excel: Comenzi rapide și formatare de bază, de Nicolae Sfetcu -/- PROGRAMARE Tipuri de date atomice în Python, de (...) Stephen Davies -/- DEZVOLTARE WEB Variabile în dezvoltarea WordPress, de Nicolae Sfetcu -/- SECURITATE CIBERNETICĂ Securitatea Big Data: Amenințări, de Nicolae Sfetcu -/- AFACERI ONLINE Marketing de conţinut, de Nicolae Sfetcu -/- MEDIA Cercetare și aplicații în rețelele sociale, de Bogdan Batrinca și Philip C Treleaven -/- ISSN 2821– 8469 ISSN – L 2821 – 8469, DOI: 10.58679/IT35986. (shrink)

Most forms of human papillomavirus can create alterations on a woman's cervix that can lead to cervical cancer in the long run, while others can produce genital or epidermal tumors. Cervical cancer is a leading cause of morbidity and mortality among women in low- and middle-income countries. The prediction of cervical cancer still remains an open challenge as there are several risk factors affecting the cervix of the women. By considering the above, the cervical cancer risk factor dataset from KAGGLE (...) data warehouse is executed for predicting the cervical cancer risk classes. The cervical cancer data set is normalised with incomplete data and Pattern Calibration. Secondly, the interpretive data analysis is carried out, and the target feature's dispersion of the cervical cancer risk is visualised. Thirdly, several classifiers are fitted to the unprocessed data set, and the performance is measured with pre and post feature scaling. Fourth, oversampling methodologies are applied to the pre - processed data set. Fifth, the oversampled dataset by differment methods are applied to all the classifiers and the performance is compared with pre and post feature scaling. Sixth, Precision, recall, Fscore, accuracy, and running time are some of the metrics used in performance analysis. The code is written in Python and executed with Anaconda Navigator on the Spyder framework. The findings of the experiments reveal that the Random forest classifier tends to sustain 96% accuracy pre and post scaling for unporocessed dataset. Similarly the same classifier tends to sustain 98% accuracy for all the oversampling techniques. (shrink)

The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. The methodology involves data collection through user inputs, preprocessing of data for analysis, and the creation of a robust system architecture comprising frontend interfaces, backend servers, and database management. The implementation of the application involves a comprehensive technology stack, including Python for AI algorithms, TensorFlow for ML models, React.js for front end development, Flask for (...) backend services, and PostgreSQL for database management. (shrink)

Due to risks like tampering, corruption, and illegal access, the rapid rise in cloud storage usage makes data integrity a top priority. As a way to verify the correctness of data stored in the cloud, we develop a "Data Integrity Verification Scheme" in this project which involves the use of third-party auditing (TPA). The system produces hash values for files both during upload and retrieval using cryptographic hashing methods, assuring consistency between both. Without needing a link to the real file (...) contents, the third-party auditor independently confirms the accuracy of the data that was stored. The Google Cloud SDK and 2 Python-based encryption libraries, hashlib and pycryptodome, are utilized in the project. Our approach is meant to be safe and efficient, providing real-time verification of integrity for data stored in cloud. . (shrink)

If you have enjoyed any of the 7 (seven) other books I have published over 20 years, including literally thousands of pages of mathematical and topological concepts, Python programs and conceptually expanding papers, please consider buying this book for $20.00 on google play books. -/- Introduction: -/- Though the following pages provide extensive exposition and dedicated descriptions of the phenomenological velocity formulas, theory and mystery, I thought it appropriate to write this introduction as a partial explanation for what phenomenal (...) velocity is, and describe, briefly its theory and applications. Phenomenological Velocity is a method for solving for something that ought cancel out with itself, but there are specific implicit forms for this thing that, “ought cancel out with itself,” namely the Lorentz coefficient ought cancel out with itself when applied to the height of a cone derived from the difference between the circumferences of two circles applied to the Pythagorean Theorem, or, more generally, the height implied by application of the Pythagorean theorem to the difference between two arc lengths’ equaling a third arc length. These difference equations are essential to conceptualizing differentiation, and in these further chapters, I demonstrate that the phenomenological velocity is, indeed the conditional derivative in the chapter, “Conditional Integral of Phenomenological Velocity.” The phenomenological velocity algebraic solution to the velocity within the Lorentz coefficient when applied to the height function in such a way that it ought cancel out with itself is both constructive mathematics and it employs the concept of, “bracketing,” - first introduced by Edmund Husserl in his writings on the phenomenological reduction. Phenomenological Velocity’s algebraic solution from the difference between two arc lengths applied to the Pythagorean Theorem to solve for a theoretical height (which is a projected distance in space), employs bracketing, because we, “set aside,” the existence of an undefined solution, namely due to the presence of necessitated complex analytical forms by the architecture of the equation, or the “mathetecture,” of the algebraic form. -/- With respect to theology, the phenomenological velocity is somehow symbolic of the creation itself; symbolic of creation due to the fact that we find the canceling out of the Lorentz coefficient as, “impotent,” non-existent or non-effecting to the mathetecture of the height function. However, via the modus-ponens work around to phenomenological velocity, which in itself does not require the complex field, but embeds implied complex field solutions to the equation while maintaining logical consistency, we find existence from non-existence. This is directly linguistically applicable to the concept of the big-bang, the resurrection of Yeshua the Messiah, and opens analogies for us to draw relationships between the, “fall,” of Adam and Eve as the generation of error, or the introduction of paradox, as we see the phallus representing paradox topologically. -/- The phenomenological velocity is a gestalt concept, relevant to cosmology, because we find that it is the perfect language-form for discussing dark matter. It does, however, require the reader to re-conceive or re-frame rather, some of the fundamental aspects of assumed physical reality like time, experience, solidity of dark matter, etc. We find the hidden dimension of phenomenological velocity to have been an overlooked aspect of mathematical physics by the researchers of Bell’s theorem and undoubtedly a host of other theorems. Thus, raising awareness about the real existence and necessitated reality of phenomenological velocity is in no way an endeavor deserving further procrastination by the scientific community, for doing so would be intellectually dishonest and further the propagation of incomplete or misleading theories on reality. -/- This work details how the Lorentz coefficient, when applied to the height of a cone in such a way as to cancel out with itself, permits the velocity variable to have a solution to it anyway, even though it ought cancel out with itself. This mathe-tecture, so to speak has consequences for complex analysis, and pave the way for, "transcendental relativity," building an adaptive framework for consciousness and physical reality. (shrink)

The project focuses on the development of a tool for identifying and extracting Forward Error Correction (FEC) schemes from unknown demodulated signals. FEC is a vital communication technique that ensures error-free data transmission without the need for retransmission, particularly in satellite communications, digital broadcasting, and deepspace applications. The proposed solution involves using Python to preprocess signals, detect FEC schemes, and then extract the specific coding parameters. Different FEC schemes such as BCH, Convolutional Codes, Turbo Codes, and LDPC codes are (...) explored for their unique characteristics and error correction capabilities. The tool aims to automate the detection and extraction of FEC schemes, thus improving the accuracy and efficiency of signal processing in communication systems. (shrink)

Password authentication is a crucial aspect of computer security and privacy. In today's digital world, users have to remember a plethora of passwords for different online accounts, which can lead to password fatigue and security risks, such as password reuse and weak passwords. To address these issues, researchers have proposed various alternative authentication methods, including graphical password authentication. In this research paper, we propose a graphical password authentication system that uses Structural Similarity Index (SSIM) to compare the user's drawn pattern (...) with the previously stored pattern. The system was implemented using the Python programming language and was designed to be user-friendly, allowing users to draw their patterns on the screen using a mouse or touch screen. The results of our experiments showed that the system was able to accurately identify the user's drawn pattern and grant access with high accuracy. Participants also reported that they found the system to be user-friendly and easy to use. The proposed system provides users with a more natural and intuitive way to remember their passwords and reduces the likelihood of password fatigue, password reuse, and weak passwords. (shrink)

This project presents an innovative approach to the efficient enumeration of hidden service URLs (.onion) on the TOR network using a custom-built graphical user interface (GUI) application. The tool leverages web scraping techniques, targeting the Ahmia search engine, to retrieve and analyze active hidden server URLs. Through the integration of Python’s Tkinter library for GUI development and the use of requests and regular expressions for content mining, the application simplifies the process of discovering and visualizing .onion URLs. The tool (...) allows users to input search queries, clean them for web compatibility, and display the results in real time. Additionally, the application provides graphical insights into the search results by generating visual data representations using matplotlib. This paper discusses the architecture, development process, and functionality of the tool, providing a new method for researchers to explore the TOR network. (shrink)

The rapid advancement of artificial intelligence (AI) technologies has revolutionized various industries, including the realm of education and career guidance. This project endeavors to harness the power of AI to develop a sophisticated career guidance application that offers personalized and effective recommendations to students and job seekers. The primary objective of this project is to address the limitations of traditional career guidance methods, which often lack customization and fail to adapt to individual preferences, skills, and aspirations. Through the integration of (...) AI algorithms, NLP techniques, and ML models, our application aims to provide accurate interest assessments, skill matching, and tailored educational pathways. The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. The methodology involves data collection through user inputs, preprocessing of data for analysis, and the creation of a robust system architecture comprising frontend interfaces, backend servers, and database management. The implementation of the application involves a comprehensive technology stack, including Python for AI algorithms, TensorFlow for ML models, React.js for frontend development, Flask for backend services, and PostgreSQL for database management . (shrink)

Revista IT & C este o publicație trimestrială din domeniile tehnologiei informației și comunicații, și domenii conexe de studiu și practică. -/- Cuprins: -/- EDITORIAL Cu sau fără Internet?, de Nicolae Sfetcu Supremația tehnologiilor IT&C, de Nicolae Sfetcu -/- TEHNOLOGIA INFORMAȚIEI Tehnologia blockchain, de Nicolae Sfetcu Ciclul de viață al inteligenței artificiale, de Nicolae Sfetcu -/- TELECOMUNICAȚII Rețelele de comunicații 5G, de Nicolae Sfetcu Proiectarea și arhitectura rețelelor de comunicații 5G, de Nicolae Sfetcu -/- INTERNET Megadatele (Big Data) pe Internet, (...) de Nicolae Sfetcu Cum funcționează Internetul, de Michael Mendez -/- SOFTWARE Deschideți / Creați o imagine în Adobe Photoshop, de Nicolae Sfetcu Excel: Comenzi rapide și formatare de bază, de Nicolae Sfetcu -/- PROGRAMARE Utilizarea limbajului de programare R în știința datelor cu RStudio, de Rafael A. Irizarry Tipuri de date atomice în Python, de Stephen Davies -/- DEZVOLTARE WEB Servere web, de Michael Mendez Variabile în dezvoltarea WordPress, de Nicolae Sfetcu -/- SECURITATE CIBERNETICĂ Securitatea informațiilor în lucrul cu megadate pe Internet, de Nicolae Sfetcu Securitatea Big Data: Amenințări, de Nicolae Sfetcu -/- AFACERI ONLINE Proiectarea și modelarea tehnologiei blockchain – Bitcoin, de Nicolae Sfetcu Marketing de conţinut, de Nicolae Sfetcu -/- MEDIA Analitica rețelelor sociale, de Nicolae Sfetcu Cercetare și aplicații în rețelele sociale, de Bogdan Batrinca și Philip C Treleaven -/- ISSN 2971 – 9097 ISSN-L 2821 – 8469, DOI: 10.58679/IT89910. (shrink)

I claim that the significance of comic works to influence our attitudes is limited by the conditions under which we find things funny. I argue that we can only find something funny if we regard it as norm-violating in a way that doesn’t make certain cognitive or pragmatic demands upon us. It is compatible with these conditions that humour reinforces our attitude that something is norm-violating. However, it is not compatible with these conditions that, on the basis of finding it (...) funny, we come to reject some existing attitude. Such a rejection would require that we recognize our attitude as norm-violating in a way that has pragmatic force. Thus if a humorous work reveals the absurdity of something, we can either find it funny and not have our attitudes significantly influenced, or else be significantly influenced but not find it funny. (shrink)

Chaos topology delves into bizarre symbol configurations of the world of quasi-quantification and pseudo-quantifiability. With quantum complexes, we are eventually able to find game play that blends wormholes with quantum computers and transcendental numbers. A conclusion that will leave you breathless, Chaos (Emmerson, 2023),.