Russophilism and Misunderstanding Ukraine (Review: Olga Onuch and Henry Hale. The Zelensky Effect. London: Hurst and Co., 2023; Dominique Arel and Jesse Driscoll. Ukraine’s Unnamed War. Before the Russian Invasion of 2022. Cambridge: Cambridge University Press, 2023).

This participatory study recounts conversational practices occurring between three teachers, a head of school, and a researcher during a month-long curriculum design workshop mediated by the MagicSchool AI technology to create social studies, language arts, science, and mathematics lessons for a virtual special education program. A social paradigm of AI-mediated educational practices is presented, wherein teachers interact with AI tools by embodying co-agency and a spirit of inquiry. Collective practices are interpreted using Gordon Pask’s conversation theory framework, showcasing how to (...) design systems of human-AI feedback loops devoted towards curriculum design. Meeting transcriptions, AI-prompt data, AI-mediated lesson plans, teacher feedback, and final products are shared using narrative inquiry to illustrate how collaborative co-agency culminated in lesson plans that co-observers felt confident in deploying. (shrink)

The sixteenth-century Cossacks became the favourite topic of Ukrainian authors of the nineteenth century who dealt with national and individual identity issues. Nikolai Gogol, the celebrated Russian author who had Ukrainian origin and was born in a Cossack village, wrote the epic romance of Taras Bulba, which narrated the story of Cossacks and their struggle for preserving their independence. While the work has been previously studied under the light of postcolonial theoretical framework, using the concepts developed by Homi Bhabha to (...) scrutinize the situation and the destiny of the two main hybrid characters, Taras Bulba’s sons, takes a step further and reveals the deeply ingrained anxiety and ambivalence in the Cossack mentality. The article focuses on the two main hybrid characters who choose divergent paths upon encountering the conflict brought to them by the imperial power. (shrink)

Publication of Taras Lyuty’s book on Nietzsche is a salient event in the philosophical life of Ukraine in 2016. Given the fact that the book covers a great number of different issues, the reviewer decides to focus on those of them which correspond to his own academic interests, related primarily to the history of psychoanalysis and biographical approach within the historiography of philosophy. As a review shows, Lyuty masterfully avoids such a trap as a biographical reductivism. He neither reduces his (...) hero’s teaching to his life, nor isolates the former from the latter. The reviewer pays a special attention to Lyuty’s delineation of intellectual links between the great philosopher and some of his predecessors (such as Ralf Waldo Emerson) and followers (some pioneers of depth psychology). In the end, the reviewer demonstrates that a very deep and systematic analysis of Nietzsche’s biography and philosophical heritage carried out by Lyuty leaves a room for further productive reflections and interpretations. (shrink)

EN: Selection of Marczyński's interviews on philosophy and literature which were recorded in early 2007 for the purpose of his radio broadcast "Hortus (In)Conclusus." Includes interviews with: Marek Bieńczyk, Józef Bremer, Ihor Byczko, Andrij Dachnij, Anna Dziedzic, Mateusz Falkowski, Tadeusz Gadacz, Michał Głowiński, Dorota Hall, Serhij Jospenko, Wachtang Kebuładze, Zbigniew Kloch, Andrzej Kołakowski, Wasyl Lisowyj, Ołeksandr Majewskyj, Anton Marczynski, Julia Marczyńska, Wadym Menżulin, Zbigniew Mikołejko, Monika Milewska, Andrij Okara, Ihor Paśko, Adam Pomorski, Myrosła Popowycz, Jerzy Prokopiuk, Iryna Puchta, Barbara Skarga, (...) Dmytro Stepowyk, Władysław Stróżewski, Myrosław Trofymuk, Tomas Venclova, Jan Woleński, Taras Wozniak, Arkadiusz Żychliński. (shrink)

Volumul de articole, studii, cuvântări şi Pastorale publicat la Editura Mitropolia Olteniei din Craiova în anul 1986 trebuie văzut şi analizat, pentru o bună şi corectă înţelegere, în lumina vremurilor în care a apărut. În acelaşi timp, ideile pe care IPS Nestor Vornicescu, Arhiepiscopul Craiovei şi Mitropolitul Olteniei, le dezvoltă în scrierile sale sunt actuale şi după aproape 30 de ani de la strângerea lor între paginile aceleaşi cărţi, după ce văzuseră lumina tiparului cu ani în urmă în diferite reviste (...) din ţară şi străinătate, semn că autorul dovedeşte o putere impresionantă de a rămâne viu prin scrierile sale într-o epocă în care totul era dictat şi atent controlat şi cenzurat de la centru. În zilele noastre, într-o perioadă în care cei ce abordează teme ca „patriotism”, „românism”, „idealul strămoşilor”, „istoria naţională”, „pacea pe pământ”, „munca”, „dreptatea” ş.a. sunt consideraţi a fi spirite retrograde şi contaminate de perioada apusă şi de tristă amintire, cuvintele IPS Nestor Vornicescu referitoare la aceste idei rămân de o actualitate ce îi lasă pe mulţi fără replică. În ciuda faptului că anumite cuvinte şi expresii au fost acaparate de doctrina comunistă, schimbându-li-se sensul, ele reprezintă constante şi permanenţe atât ale spiritului nostru naţional, cât şi virtuţi creştine despre care s-a vorbit şi trebuie să se vorbească şi să fie respectate pentru a putea să ne orientăm într-o Europă din care ele nu au dispărut nicio clipă din discursul oficial sau particular şi în care România îşi caută coordonatele naţionale pentru a se adapta după integrarea atât de mult dorită de toţi. „Volumul Pace pe pământ îl adevereşte pe Mitropolitul şi Academicianul Nestor Vornicescu ca ierarh şi teolog pe linia învăţăturii şi a tradiţiei Ortodoxiei româneşti, iubitor şi luptător pentru ţară şi popor, pentru pace şi înţelegere între oameni şi neamuri, pentru apărarea păcii şi a vieţii. Concluzia categorică este că pacea – salvgardarea, apărarea, întărirea, impunerea şi permanentizarea ei urbi et orbi – constituie imperativul vital al zilelor noastre, cum arată autorul pe bună dreptate”.2. (shrink)

Kyiv-Mohyla Seminar on the History of Philosophy was established by the Kyiv-Mohyla Academy’s Department of Philosophy and Religious Studies in 2003. In this yearly seminar, the Department’s members as well as the historians of philosophy from other academic institutions regularly take part. Since 2003, 16 meetings of the seminar took place. They were focused on such topics as “Historiography of Philosophy in Ukraine: Current State and Perspectives”, “Actual Problems of the Source Studies in the Historiography of Philosophy”, “The Problem of (...) Text Interpretation in the Historiography of Philosophy”, “Dmytro Chyzhevskyi as a Historian of Philosophy”, “Historiography of Philosophy in Ukraine: Current State and Perspectives”, “The Problem of Method in the Historiography of Philosophy”, “Oleksii Losiev: Personality and Heritage ”, “Methodology of the Historiography of Philosophy: Actual Strategies”, “Wilhelm Windelband as a Philosopher and Historian of Philosophy ”, “Hegel’s Heritage in the Mirror of Interpretations”, “The Studies on the History of Philosophy: New Generation”, “Kant’s Criticism from the perspective of Wolf’s dogmatism”, “The Reception of Indian Philosophy in Ukraine: 1840s–1930s”, “Did Kant Answer the Question on What a Man Is?”. The proceedings of the early three meetings were published in a special volume. The current issue of “NaUKMA Research Papers in Philosophy and Religious Studies” contains the proceedings of the sixteenth meeting of Kyiv-Mohyla Seminar on the History of Philosophy, which took place at the National University of Kyiv-Mohyla Academy on February 1, 2017. The main speaker was Dr. Taras Lyuty, while the co-speakers included Dr. Mykhailo Minakov and Dr. Vakhtang Kebuladze. The meeting was conducted by Prof. Vadym Menzhulin. The audio recording of the meeting was deciphered by a PhD-student Taras Fostiak. (shrink)

Despre afinitățile elective dintre Mircea Eliade și diferiți oameni de cultură români și străini s-au scris multe pagini de-a lungul timpului. Cei interesați de opera savantului nu pot trece cu vederea legăturile spirituale și culturale cu Nae Ionescu, Mihail Sebastian, Emil Cioran, Constantin Noica, Goethe, C.G. Jung, Marcel Proust, Raffaele Pettazzoni etc. Și numărul acestora poate crește cu multe alte nume din țară și din străinătate, dovedind faptul că Mircea Eliade a fost și rămâne o personalitate care a marcat istoria (...) și cultura națională și universală și ale cărui idei încă fecundează spiritele preocupate de viața religioasă, de literatură sau de științele secolului în care trăim. Pornind de la aceste date, în cartea de față încercăm să creionăm felul în care ideile lui Eliade pot fi așezate alături de cele ale unor gânditori din arii diferite de cercetare, având în centru teme ce se regăsesc atât în lucrările savantului de origine română, cât și operele acestor personalități. În acest sens, ne-am oprit la câteva nume importante: Rudolf Otto, Sfântul Dionisie Areopagitul (alături de Meister Eckhart și Nicolaus Cusanus), Pr. Alexander Schmemann, Thomas J.J. Altizer, Sfântul Grigorie Palama (și isihasmul), I.P. Culianu, Marin Sorescu, Constantin Brâncuși, Nicolae Steinhardt, Pr. Dumitru Stăniloae. (shrink)

Le but de ce recueil est d’offrir des commentaires accessibles et introductifs aux textes classiques qu’ils accompagnent, en ouvrant des perspectives de discussion sur le thème du capitalisme. C’est en ce sens qu’Emmanuel Chaput lance le débat en commentant le texte de Pierre-Joseph Proudhon, « Qu’est-ce que la propriété ? ». Les textes de Karl Marx ne sont bien sûr pas laissés pour compte : Samuel-Élie Lesage s’engage fermement dans cette voie en discutant L’idéologie allemande de Karl Marx, Christiane Bailey (...) nous offre d’approcher différemment l’œuvre marxienne en abordant son traitement de la question animale dans des extraits du Manifeste du parti communiste et du Travail salarié et capital, et Mathieu Joffre-Lainé nous présente une analyse fine des questions proprement économiques du Capital. Abordant les alternatives au capitalisme dans la pensée de Léon Bourgeois, Éliot Litalien commente La Solidarité et Simon-Pierre Chevarie-Cossette s’attaque à l’analyse de l’Essai d’une philosophie de la solidarité. Enfin, en liant capitalisme, patriarcat et pouvoir politique, Tara Chanady propose une lecture du texte Du mariage et de l’amour de Emma Goldman. La conclusion de Marie-Eve Jalbert se situe dans une perspective contemporaine et critique en décortiquant la critique du socialisme avancée par Friedrich A. Hayek. (shrink)

În toată perioada celui de Al Doilea Război Mondial, serviciile secrete române au colectat informații privind localizarea unităților germane în țară. Ofițerii din grupul conspirativ pentru întoarcerea armelor împotriva Germaniei sporesc numărul unităților românești prezente în capitală, pentru a putea face față germanilor. O misiune interaliată clandestină a fost parașutată în București și găzduită în secret de generalul Constantin Sănătescu. Urmare a succesului ofensivei germane pe aliniamentul Iași-Chișinău, grupul conspirativ a decis accelerarea pregătirilor. Pe 20 august, Mihai I și grupul (...) de militari din conspirație au stabilit data acțiunii pentru 26 august. În noaptea de 21 august, în secret, membrii opoziției și-au dat acordul pentru această dată. Aflând însă că mareșalul Antonescu intenționa să se întoarcă pe front în 24 august, Regele Mihai s-a văzut obligat să modifice data pentru 23 august 1944. -/- DOI: 10.13140/RG.2.2.22256.12808. (shrink)

Asemenea altor procese istorice, procesul de destrămare a regimurilor comuniste din Europa Centrală și de Est nu poate fi periodizat cu ușurință și nu poate fi corelat cu o explicație cauzală irefutabilă. Prăbușirea acestor regimuri s-a petrecut accelerat în cursul anului 1989, însă procesul de decompensare a început mult mai devreme. Când anume? – Este foarte greu de spus. Fără îndoială, au existat cauze obiective care au dus la rezultatele spectaculoase consemnate de istorie, însă a existat și un joc al (...) întâmplărilor fericite, precum și un impact aproape imposibil de măsurat al unor factori subiectivi. Destrămarea regimurilor comuniste din Europa Centrală și de Est nu a părut inevitabilă pentru actorii scenei politice din anul 1989, după cum nu ni se pare nici nouă că ar fi fost vorba de un proces inexorabil, la trei decenii de la evenimentele respective. Dat fiind caracterul puțin previzibil al evenimentelor care au marcat destrămarea regimurilor comuniste din Blocul Estic, nu este de mirare că ziariștii de la „Scînteia“ au aplicat până la capăt stratageme propagandistice menite să apere în sfera mediatică establishment-ul comunist: (a) supradimensionarea taberei proprii, respectiv deprecierea adversarului; (b) prezentarea lacunară sau abstractizantă a faptelor; (c) decontextualizarea; (d) mistificarea. În ciuda „virtuozității“ de care au dat dovadă ziariștii, aceste stratageme propagandiste nu au dat rezultatele scontate, în primul rând din cauza faptului că regimul ceaușist nu controla perfect mediul informațional din țară. Cetățenii români puteau corobora informațiile oficiale din „Scînteia“ cu informațiile obținute de la Europa Liberă, Vocea Americii, posturile radio și TV din țările vecine. Cea mai întristătoare concluzie este însă aceea că articolele din „Scînteia“ au creat pentru România regimului Ceaușescu imaginea unei fortărețe a ortodoxiei socialiste. Până în ultima zi a regimului comunist din România (22 decembrie 1989), publicația „Scînteia“ s-a dovedit a fi organul de presă obedient al Partidului Comunist Român, partid aflat în cea mai dogmatică și inflexibilă perioadă de viață. Nu este de mirare, în aceste condiții, că sfârșitul regimului Ceaușescu a însemnat sfârșitul Partidului Comunist Român și sfârșitul ziarului „Scînteia“. (shrink)

Mărturii importante despre momentul 23 august 1944 deţinem şi ca urmare a existenţei, în Arhivele Naţionale ale României – Fondul „Manuscrise”, a Memoriului de activitate al lui Traian Borcescu, şeful Secţiei a II-a Contrainformaţii din cadrul Serviciului Special de Informaţii (SSI). Ofiţerul, din postura sa de conducător al structurii SSI-ului cu atribuţii în ceea ce priveşte cunoaşterea şi combaterea fenomenelor care puteau atinge ordinea şi stabilitatea internă a ţării în timp de război, este unul din participanţii direcţi la acţiunea de (...) pregătire a actului, demonstrând, încă o dată, că această îndrăzneaţă acţiune de alăturare a României la coaliţia Naţiunilor Unite nu s-a făcut ad-hoc, ci a necesitat un plan minuţios elaborat la care SSI-ul a contribuit prin reprezentanţii săi care şi-au făcut pe deplin datoria faţa de ţară, ca şi în alte momente sau evenimente de mare importanţă pentru România. (shrink)

Many people appear to attach great value to sad music. But why? One way to gain insight into this question is to turn away from music and look instead at why people value sad conversations. In the case of conversations, the answer seems to be that expressing sadness creates a sense of genuine connection. We propose that sad music can also have this type of value. Listening to a sad song can give one a sense of genuine connection. We then (...) explore the nature of this value in two experimental studies. The results suggest a striking relationship between music and conversation. People see something distinctively musical in works that express precisely those emotions that they think most create connection within conversation. (shrink)

O sintagmă revine recurent, cel puţin în prima parte a cărţii, „conștiinţă feministă”. Legat de aceasta, constatările Lilianei Popescu, conferenţiară universitară și prorectoră la Școala Naţională de Studii Politice și Administrative (SNSPA), mi-au confirmat câteva intuiţii despre felul în care s-a produs transformarea politică a societăţii românești în ultimele trei decenii. „Coagularea conștiinţei feministe în ţara noastră după anul 1990 – susţine autoarea – s-a făcut mai ales prin adoptarea tradiţiei feminismului occidental. În același timp, trebuie remarcat faptul că, înainte (...) de anul 1990, în societatea socialistă existau elemente implicite și explicite de afirmare a egalităţii de gen: educaţia era deschisă atât tinerilor, cât și tinerelor; oportunităţile de muncă erau accesibile atât bărbaţilor, cât și femeilor; statul socialist oferea facilităţi tinerelor mame, concedii maternale, creșe, grădiniţe, iar la întoarcerea din concediul de creștere a copilului, serviciul era asigurat. Multe dintre societăţile occidentale au luptat din greu pentru obţinerea acestor drepturi. (…) Consider că în conștiinţa feministă de după 1990 s-a pus un accent supradimensionat pe principiile și pe practicile feministe venite dinspre Vest și s-a subestimat propria noastră moștenire din perioada socialistă, care era valoroasă și merita preţuită. Este suficient să ne gândim la scăderea dramatică a cifrei de reprezentare a femeilor în parlamentele formate începând cu 1990. Practic, reprezentarea femeilor a scăzut de aproape 10 ori – de la peste 30% la 4%” (p. 52-53). (shrink)

People tend to show greater liking for expressions of sadness when these expressions are described as art. Why does this effect arise? One obvious hypothesis would be that describing something as art makes people more likely to regard it as fictional, and people prefer expressions of sadness that are not real. We contrast this obvious hypothesis with a hypothesis derived from the philosophical literature. On this alternative hypothesis, describing something as art makes people more inclined to appropriate it, i.e., to (...) see it as an expression of their own sadness. Study 1 found that describing the exact same sad text as art (e.g., a monologue) as opposed to not-art (e.g., a diary entry) led to increased liking for the work. Study 2 showed that this effect is not mediated by fictionality. Study 3 showed that the effect is mediated by appropriation. Study 4 looked at the impact of a manipulation of fictionality. Describing a work as fictional did lead to increased liking, but this effect was completely mediated by appropriation. These results provide at least some initial support for the appropriation hypothesis. (shrink)

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has brought to the (...) field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the challenges businesses (...) face when adopting AI, such as data privacy concerns, integration difficulties, and the need for skilled professionals. The paper concludes by offering recommendations for successfully implementing AI solutions to maximize benefits while mitigating potential challenges. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, highlighting how (...) machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is rapidly transforming organizational dynamics and decision-making processes. This paper explores the ways in which AI enhances leadership effectiveness by providing data- driven insights, optimizing decision-making, and automating routine tasks. Additionally, it examines the challenges leaders face when adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to offer a (...) comprehensive overview of the opportunities AI presents for effective leadership, as well as the strategies required to address its associated challenges. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive feat. (...) Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Abstract: The sinking of the RMS Titanic in 1912 remains a poignant historical event that continues to captivate our collective imagination. In this research paper, we delve into the realm of data-driven analysis by applying Artificial Neural Networks (ANNs) to predict the chances of survival for passengers aboard the Titanic. Our study leverages a comprehensive dataset encompassing passenger information, demographics, and cabin class, providing a unique opportunity to explore the complex interplay of factors influencing survival outcomes. Our ANN-based predictive model (...) achieved an accuracy rate of 78%, shedding light on the underlying patterns within the data. However, this paper underscores the multifaceted nature of survival prediction and acknowledges several challenges. These include missing data imputation, feature engineering, and the inherent noise within historical datasets. We further discuss the importance of model evaluation metrics, showcasing not only accuracy but also precision, recall, and F1-score as essential indicators of predictive performance. The research paper meticulously outlines the architecture of the ANN model, emphasizing the key hyperparameters, activation functions, and regularization techniques employed in model development. Additionally, we address ethical considerations related to data handling and potential biases within the dataset. While our achieved accuracy is a notable achievement, this study emphasizes the importance of interpreting the results with due caution. It underscores the need for a holistic perspective that considers the practical implications of false positives and false negatives, especially in the context of a disaster scenario. In conclusion, this research contributes to our understanding of survival prediction in historical events like the Titanic disaster using modern machine learning techniques. It calls for further exploration and refinement of predictive models, with a focus on enhancing the interpretability and generalizability of such models to real-world applications. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...) layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Abstract: Context: grapevine leaves are an important agricultural product that is used in many Middle Eastern dishes. The species from which the grapevine leaf originates can differ in terms of both taste and price. Method: In this study, we build a deep learning model to tackle the problem of grape leaf classification. 500 images were used (100 for each species) that were then increased to 10,000 using data augmentation methods. Convolutional Neural Network (CNN) algorithms were applied to build this model (...) specifically using the pre-trained model on top of the VGG16 architecture. Then, dense layers were added to classify the output of the Convolutional layers and classify outputs to the five classes (species) the leaf belonged to. Results: It was found that feature extraction without fine-tuning the convolutional layers yielded poor results, about 86% accuracy, while training the whole network along with some data preprocessing gave the best results, about 99.45% accuracy on the testing dataset. Conclusions: The proposed CNN model is an effective one for the problem of classification of grape leaf species. (shrink)

Стаття продовжує цикл опитувань, метою яких було з’ясування факторів впливу на вибір мови в білінгвальному середовищі. В аналізованому опитуванні, проведеному у Вінниці та Вінницькій області, охоплено 560 старшокласників віком 14–17 років. Результати анкетування дали змогу зробити висновки про психологічні чинники, що впливають на респондента, який перебуває в двомовному середовищі.

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water quality (...) monitoring and decision-making for stakeholders and policymakers. (shrink)

Queer Death Studies (QDS) refers to an emerging transdisciplinary field of research that critically and (self) reflexively investigates and challenges conventional normativities, assumptions, expectations, and regimes of truths that are brought to life and made evident by death, dying, and mourning. Since its establishment as a research field in the 1970s, Death Studies has drawn attention to the questions of death, dying, and mourning as complex and multifaceted phenomena that require inter- or multi-disciplinary approaches and perspectives. Yet, the engagements with (...) death, dying and mourning, constitutive of conventional Death Studies’ investigations, tend to remain insufficient and reductive. They are often governed by the normative notions of: the subject; bonds between humans, as well as between humans and (their) animals; family relations and communities; rituals; and finally, experiences of grief, mourning, and bereavement. Moreover, these engagements are frequently embedded in constraining beliefs in life/death divides, constructed along the lines of conventional religious and/or scientific mind/body dualisms, characteristic of the Western cultural imaginaries. Against this background, QDS offers a site for ‘queering’ traditional ways of approaching death both as a subject of study and philosophical reflection, and as a phenomenon to articulate in artistic work or practices of mourning. Here, the notion of ‘queer’ conveys many meanings. It refers to researching and narrating death, dying, and mourning in the context of queer bonds and communities, where the subjects involved/studied/interviewed and the relations they are involved in are recognised as ‘queer’. Simultaneously, the term ‘queer’ can also function as an adverb and a verb, referring thus to the processes of going beyond and unsettling (subverting, exceeding) binaries and given norms, normativities, and constraining conventions. In other words, ‘queer’ becomes both a process and a methodology that is applicable and exceeds the focus on gender and sexuality as its exclusive concerns. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, achieving (...) an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. (...) A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction is (...) based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (shrink)

Abstract: Climate change presents a critical challenge that demands advanced analytical tools to predict and mitigate its impacts. This paper explores the role of artificial intelligence (AI) in enhancing climate modeling, emphasizing how AI-driven methods are revolutionizing our understanding and response to climate change. By integrating machine learning algorithms with diverse data sources such as satellite imagery, historical climate records, and real-time sensor data, AI improves the accuracy, efficiency, and granularity of climate predictions. The paper reviews key AI techniques, including (...) neural networks and ensemble models, and their applications in simulating extreme weather events and forecasting long-term climate trends. Additionally, it examines the impact of AI-enhanced climate models on policy-making and strategic planning. Despite significant advancements, challenges such as data quality, computational demands, and model interpretability remain. This study underscores the potential of AI to transform climate modeling and highlights areas for future research and development to harness these technologies for a sustainable future. (shrink)

Mango is an economically significant fruit crop cultivated in various tropical and subtropical regions around the world. However, the productivity and quality of mangoes can be severely impacted by a range of pests. This research paper introduces an innovative approach to identify mango pests using an expert system. The expert system integrates knowledge from entomology and plants to provide accurate identification of common mango pests. The paper outlines the development and implementation of the expert system using Clips shell, which utilizes (...) rule-based techniques to analyze symptoms associated with mango pests. By inputting observed symptoms, such as leaf spots, fruit damage, leaf mild and stem cankers the system can accurately identify the specific pest species affecting the mango tree. For each pest, the paper discusses their specific symptoms and Favorable conditions that affect the mango plant. By understanding the identification and symptoms of these pests, mango growers and agricultural professionals can implement appropriate pest management strategies to mitigate the economic losses caused by these insects. The research presented in this paper aims to contribute to the body of knowledge on mango pest management, facilitating sustainable mango production and ensuring the availability of high-quality mangoes in global markets. (shrink)

As sustainable tourism gains increasing attention, understanding the factors that influence travelers' motivation to engage with sustainable travel agents becomes crucial. This study investigates the moderating effect of social media usage on the relationship between the perceived value of websites and motivational factors for sustainable travel agents. The study proposes that social media usage acts as a moderator in shaping the relationship between the perceived value of websites and motivational factors. This study has utilized smart tourism. Therefore, independent variable motivation (...) in terms of informativeness gratification and gratification acted as moderating variables. This study has utilized an online questionnaire adapted from a standardized questionnaire based on the literature review. Moreover, a total of 146 managers were surveyed in which they were required to respond to 55 items that elicited the 7 variables social media use, Propensity, perceived website value, motivating factors, entertainment gratification, informativeness gratification, sustainable smart tourism. The data were analyzed by SPSS and PLS-SEM. The findings indicated that relevance and importance of social media use and sustainable smart tourism. The result revealed that 7 hypotheses had a significant relationship. 3 hypotheses did not show a moderating effect. Overall, this study extends the understanding of sustainable smart tourism. These findings will benefit travel agents’ managers in Malaysia to better understand the role of social media use and utilize it towards developing sustainable sustainable smart tourism. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to enhance (...) model generalizability. (shrink)

Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These findings are pivotal (...) for enhancing public health initiatives aimed at mitigating tobacco use, assessing associated health risks, and mitigating smoking-related diseases. (shrink)

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our neural network, (...) featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

Papaya is a tropical fruit with a green cover, yellow pulp, and a taste between mango and cantaloupe, having commercial importance because of its high nutritive and medicinal value. The process of sorting papaya fruit based on maturely is one of the processes that greatly determine the mature of papaya fruit that will be sold to consumers. The manual grading of papaya fruit based on human visual perception is time-consuming and destructive. The objective of this paper is to the status (...) classification of papaya fruits if it's mature or partially matured or unmatured. A deep learning technique that was extensively applied to image recognition was used. The trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. Classification model of VGG16 achieved a 100% accuracy and 112 seconds of training time. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)

Abstract: As artificial intelligence (AI) technologies rapidly evolve and permeate various aspects of society, the need for effective regulation and governance has become increasingly critical. This paper explores the current landscape of AI regulation, examining existing frameworks and their efficacy in addressing the unique challenges posed by AI. Key issues such as ensuring compliance, mitigating biases, and maintaining transparency are analyzed. The paper also delves into ethical considerations surrounding AI governance, emphasizing the importance of fairness and accountability. Through case studies (...) of different regulatory approaches and their outcomes, the paper highlights both successes and areas for improvement. Finally, it offers insights into future directions for AI regulation, advocating for innovative policies and enhanced international cooperation to ensure that AI technologies are developed and deployed responsibly. This comprehensive analysis aims to contribute to the ongoing discourse on creating robust regulatory mechanisms that can keep pace with the rapid advancement of AI. (shrink)

Abstract: As artificial intelligence (AI) technologies become increasingly integrated into various sectors, ethical considerations surrounding their development and deployment have become paramount. This paper explores the multifaceted ethical landscape of AI, focusing on key challenges such as bias, transparency, privacy, and accountability. It examines how these issues manifest in AI systems and their impact on society. The paper also evaluates current approaches and solutions aimed at mitigating these ethical concerns, including regulatory frameworks, ethical guidelines, and best practices for AI design. (...) By providing a comprehensive analysis of these challenges and solutions, this paper aims to contribute to the ongoing discourse on responsible AI development and implementation, advocating for a balance between technological innovation and ethical integrity. (shrink)

The emphasis on evidence based medicine (EBM) has placed increased focus on finding timely answers to clinical questions in presence of patients. Using a combination of natural language processing for the generation of clinical excerpts and information theoretic distance based clustering, we evaluated multiple approaches for the efficient presentation of context-sensitive EBM excerpts.

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our investigation (...) identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

The study’s goal was to provide an educational intervention in Algebra through GeoGebra that would boost students’ confidence, improve their learning, and correct their most minor mastered skills, allowing them to improve their Algebra performance. The research design was quasi-experimental, with 40 nonrandomly chosen participants comprising the GeoGebra and control groups. Mean and standard deviation was employed to describe the algebra performance and confidence of the respondents. At the same time, independent and dependent t-tests were used to determine the students’ (...) significant difference in algebra performance and confidence in the pre-and post-test between the control and GeoGebra groups. GeoGebra effectively improved algebra confidence, enhanced learning, and remedied the students’ least mastered skills. GeoGebra is recommended as an instructional material in teaching and learning Algebra and can be extended to other mathematics content areas. (shrink)

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing attacks (...) are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research institutions, (...) and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...) the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a heldout test set. (shrink)

This study aims to identify the strengthening of the global entrepreneurial orientation practice for employees at the University of Palestine, where the researchers used the descriptive and analytical approach, through a questionnaire distributed to a sample of employees at the University of Palestine, where the size of the study population is (234) employees and the sample size is (117) Employees, of whom (90) employees responded. The study found a set of results, the most important of which are: that there is (...) a good degree of global entrepreneurial orientation for employees, as the percentage reached (70.25%), and the results also showed that there are no statistically significant differences in the smart university transformation and their impact on the global entrepreneurial trend according to personal and organizational data. The study presented a set of recommendations, the most important of which are: The need to work to enhance the global entrepreneurial orientation of the university's employees. (shrink)

In recent years, there has been a heated debate about how to interpret findings that seem to show that humans rapidly and automatically calculate the visual perspectives of others. In the current study, we investigated the question of whether automatic interference effects found in the dot-perspective task (Samson, Apperly, Braithwaite, Andrews, & Bodley Scott, 2010) are the product of domain-specific perspective-taking processes or of domain-general “submentalizing” processes (Heyes, 2014). Previous attempts to address this question have done so by implementing inanimate (...) controls, such as arrows, as stimuli. The rationale for this is that submentalizing processes that respond to directionality should be engaged by such stimuli, whereas domain-specific perspective-taking mechanisms, if they exist, should not. These previous attempts have been limited, however, by the implied intentionality of the stimuli they have used (e.g. arrows), which may have invited participants to imbue them with perspectival agency. Drawing inspiration from “novel entity” paradigms from infant gaze-following research, we designed a version of the dot-perspective task that allowed us to precisely control whether a central stimulus was viewed as animate or inanimate. Across four experiments, we found no evidence that automatic “perspective-taking” effects in the dot-perspective task are modulated by beliefs about the animacy of the central stimulus. Our results also suggest that these effects may be due to the task-switching elements of the dot-perspective paradigm, rather than automatic directional orienting. Together, these results indicate that neither the perspective-taking nor the standard submentalizing interpretations of the dot-perspective task are fully correct. (shrink)