Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a technique used (...) to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification. (shrink)

The van Wijngaarden grammars are two-level grammars that present many interesting properties. In the present article I elaborate on six of these properties, to wit, (i) their being constituted by two grammars, (ii) their ability to generate (possibly infinitely many) strict languages and their own metalanguage, (iii) their context-sensitivity, (iv) their high descriptive power, (v) their productivity, or the ability to generate an infinite number of production rules, and (vi) their equivalence with the unrestricted, or Type-0, Chomsky grammars.

This article investigates the hitherto under-examined relations between affirmative action, paternalism and respect. We provide three main arguments. First, we argue that affirmative action initiatives are typically paternalistic and thus disrespectful towards those intended beneficiaries who oppose the initiatives in question. Second, we argue that not introducing affirmative action can also be disrespectful towards these potential beneficiaries because such inaction involves a failure to adequately recognize their moral worth. Third, we argue that the paternalistic disrespect involved in affirmative action is (...) alleviated when the potential beneficiaries’ preferences against such initiatives are adaptive. We conclude that although there is a relevant sense in which paternalistic affirmative action is disrespectful, it may well be more disrespectful not to pursue such policies. (shrink)

For millennia, knowledge has eluded a precise definition. The industrialization of knowledge (IoK) and the associated proliferation of the so-called knowledge communities in the last few decades caused this state of affairs to deteriorate, namely by creating a trio composed of data, knowledge, and information (DIK) that is not unlike the aporia of the trinity in philosophy. This calls for a general theory of knowledge (ToK) that can work as a foundation for a science of knowledge (SoK) and additionally distinguishes (...) knowledge from both data and information. In this paper, I attempt to sketch this generality via the establishing of both knowledge structures and knowledge systems that can then be adopted/adapted by the diverse communities for the respective knowledge technologies and practices. This is achieved by means of a formal–indeed, mathematical–approach to epistemological matters a.k.a. formal epistemology. The corresponding application focus is on knowledge systems implementable as computer programs. (shrink)

I propose and articulate a novel theory of desire, called the Revised Reward Theory. As the name suggests, the theory is based—and expands—on Arpaly and Schroeder’s (2014) Reward Theory of Desire. The initial Reward Theory identifies desires with states of the reward learning system such that for an organism to desire some P is for its reward system to treat P as a reward upon receipt. The Revised Reward Theory identifies desires with a different state of the same system, such (...) that for an organism to desire some P is for its reward system to expect or predict that P will be rewarding (roughly) prior to receipt. The difference amounts to equating desires with what we ultimately find rewarding or satisfying versus those that underlie our motivations to obtain that which we take ourselves to desire. I argue that the structure of the reward system is incompatible with the original Reward Theory but compatible with the Revised Reward Theory. I demonstrate that this difference has important philosophical implications. I focus on moral responsibility and demonstrate Arpaly and Schroeder’s argument, that addiction can mitigate moral responsibility, turns on this precise difference. Arpaly, Schroeder, and I all ascribe to a meta-theory called ‘natural kindism’ which identifies mental kinds with neurocognitive kinds. This discussion, in addition to defending a theory of desire, is intended to act as a proof of concept for natural kindism as offering a powerful framework for relating empirical results to philosophical issues. (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)

The gulf between philosophical and formal ontology can and should be bridged. In this Editorial, I elaborate briefly on this subject.

Numerous societal issues, from climate change to pandemics, require public engagement with scientific research. Such engagement reveals challenges that can arise when experts communicate with laypeople. One of the most common frameworks for framing these communicative interactions is the deficit model of science communication, which holds that laypeople lack scientific knowledge and/or positive attitudes towards science, and that imparting knowledge will fill knowledge gaps, lead to desirable attitude/behavior changes, and increase trust in science. §1 introduces the deficit model in more (...) detail and shows that adhering to this model often fails to achieve its aims, which motivates the main question of this chapter: how can Ludwik Fleck’s theory of thinking collectives address the persistent problem of deficit approaches in science communication? I suggest that it can do so by exposing the deficit model’s implicit assumption of an expert-lay divide. Accordingly, §2 lays out Fleck’s theory and §3 contrasts it with contemporary debates about science communication. Following this descriptive work, §4 draws on Fleck’s ideas to make four concrete suggestions for further questioning the expert-lay divide. (shrink)

An analysis of--yet another--case of academic failure in multi- and interdisciplinarity. An editorial of the Journal of Knowledge Structures & Systems.

Criminal law practices in the US, including policing and incarceration, have drawn heavy criticism for their disproportionate impact on black people, particularly black men. At the same time, some feminist scholars and activists advocate for increases in criminal law responses to sexual assault, including expanding criminal statutes to cover more instances of sexual assault and increasing sentencing guidelines. These reforms are often justified by claims that criminal law should express more feminist values and reject sexist social schemas. This paper makes (...) the case that criminal legal interventions aimed at changing sexist social schemas, such as ‘rape culture,’ are unlikely to be successful because criminal law is effective at maintaining existing social norms, but bad at introducing novel ones. Similarly, in the US, criminality has been ideologically linked to blackness, so attempts to use the stigma of criminality to combat sexual violence are likely to reinforce anti-black racism. The very meaning of a criminal law depends on related social schemas, and the racist social schemas associated with criminality undermine the ability of criminal legal responses to present anti-sexist values. At best, criminal legal reforms aimed at expressing the equal dignity of women will be ambiguous because their meaning is negotiated by existing sexist and racist social schemas. Thus, penal expressivism cannot offer a justification for using criminal law to combat sexual assault. (shrink)

2nd edition. Many-valued logics are those logics that have more than the two classical truth values, to wit, true and false; in fact, they can have from three to infinitely many truth values. This property, together with truth-functionality, provides a powerful formalism to reason in settings where classical logic—as well as other non-classical logics—is of no avail. Indeed, originally motivated by philosophical concerns, these logics soon proved relevant for a plethora of applications ranging from switching theory to cognitive modeling, and (...) they are today in more demand than ever, due to the realization that inconsistency and vagueness in knowledge bases and information processes are not only inevitable and acceptable, but also perhaps welcome. The main modern applications of (any) logic are to be found in the digital computer, and we thus require the practical knowledge how to computerize—which also means automate—decisions (i.e. reasoning) in many-valued logics. This, in turn, necessitates a mathematical foundation for these logics. This book provides both these mathematical foundation and practical knowledge in a rigorous, yet accessible, text, while at the same time situating these logics in the context of the satisfiability problem (SAT) and automated deduction. The main text is complemented with a large selection of exercises, a plus for the reader wishing to not only learn about, but also do something with, many-valued logics. (shrink)

People often discriminate based on negative or positive stereotypes about others. Important examples of this are highlighted by the theory of ambivalent sexism. This theory distinguishes sexist stereotypes that are negative (hostile sexism) from those that are positive (benevolent sexism). While both forms of sexism are considered wrong towards women, hostile sexism seems intuitively worse than benevolent sexism. In this article, we ask whether the difference between discriminating based on positive vs. negative stereotypes in itself makes a morally relevant difference. (...) We suggest that it does not. By examining a number of prominent accounts of what makes discrimination wrong, we defend the Moral Irrelevance View according to which stereotype valence is irrelevant to the moral evaluation of discrimination, all else equal. (shrink)

This paper develops co-ordinated multiple-domain supervenience relations to model determination and dependence relations between complex entities and their constituents by appealing to R-related pairs and by making use of associated isomorphisms. Supervenience relations are devised for order-sensitive and repetition-sensitive mereologies, for mereological systems that make room for many-many composition relations, as well as for hierarchical mereologies that incorporate compositional and hylomorphic structure. Finally, mappings are provided for theories that consider wholes to be prior to their parts.

Academic excellence and intelligence are commonly lauded as commendable attributes synonymous with success. However, a disconcerting trend has surfaced within educational institutions, challenging the prevailing narrative of scholastic accomplishment—smart shaming. This research delves into the increasing concern of smart shaming within educational settings, particularly at Immaculate Conception College of Balayan, Inc., questioning the predominant emphasis on academic excellence and intelligence. A qualitative case study design, along with judgmental sampling, was employed to examine fifteen (15) student leaders who had experienced smart (...) shaming on campus. Thematic analysis revealed that sarcastic remarks including "edi wow," "Okay ikaw na matalino," "bidabida," and "sipsip" were a prevalent manifestation of smart shaming, adversely affecting emotional well-being, academic and leadership prowess, mental health, and self-perception. Coping mechanisms, predominantly centered around ignoring and focusing on goals, were identified. The findings underscore the imperative for educators to remain vigilant regarding instances of smart shaming in classrooms, promoting open communication to ensure student well-being. Additionally, guidance counselors play a pivotal role in aiding students in effectively navigating the repercussions of smart shaming. This study advocates for proactive programs and interventions to address and alleviate the deleterious effects of smart shaming on students. (shrink)

Expressiveness and decidability are two core aspects of programming languages that should be thoroughly known by those who use them; this includes knowledge of their metalanguages a.k.a. formal grammars. The van Wijngaarden grammars (WGs) are capable of generating all the languages in the Chomsky hierarchy and beyond; this makes them a relevant tool in the design of (more) expressive programming languages. But this expressiveness comes at a very high cost: The syntax of WGs is extremely complex and the decision problem (...) for the generated languages is generally unsolvable. With this in mind, I provide here a short primer of the syntax of WGs, which includes syntactic restrictions that guarantee decidability for the corresponding generated languages. (shrink)

Reasoning over our knowledge bases and theories often requires non-deductive inferences, especially – but by no means only – when commonsense reasoning is the case, i.e. when practical agency is called for. This kind of reasoning can be adequately formalized via the notion of supraclassical consequence, a non-deductive consequence tightly associated with default and non-monotonic reasoning and featuring centrally in abductive, inductive, and probabilistic logical systems. In this paper, we analyze core concepts and problems of these systems in the light (...) of supraclassical consequence. (shrink)

This paper provides an account of Kant's categories of freedom, explaining how they fit together and what role they are supposed to play. My interpretation places particular emphasis on the structural features that the table of the categories of freedom shares with the table of judgements and the table of categories laid out by Kant in the Critique of Pure Reason. In this way we can identify two interpretative constraints, namely (i) that the categories falling under each heading must form (...) a synthetic unity whereby the third one derives from the combination of the other two. and (ii) that the first two categories falling under each heading must be morally undetermined and sensibly conditioned, while the third category is sensibly unconditioned and determined only by the moral law. (shrink)

Lifelong learning is becoming part of the philosophy of professional education. Continuing medical education is the responsibility of all personnel who are responsible for the delivery of components of the healthcare delivery system. Continuing education is becoming increasingly obvious for medical universities, hospitals, and health care providers. Pharmacists who practice in a community pharmacy and hospital, and who are participating in residency recognize that the traditional role of the pharmacist is changing. Over the last decades, a host of new services (...) have been identified as a function of the pharmacist [1]. A review of these services revealed a personal commitment to continuing education. It is the absolute need to develop a level of competence that will improve patient care. In Libya, pharmacists working in the community practice setting are pharmacists with a diploma of pharmacy or with a Bachelor of Pharmaceutical Science, Master of Science, and Doctor of Pharmacy (Pharm D). The main objective is that the graduates possess the knowledge and skills needed to optimally mange the pharmaceutical services [1, 2]. Patient's expectations from pharmacists are that the medicine should be effective, safe and affordable. (shrink)

One must accept the complexity of modern society as a multifaceted influence that will direct the future of all social organizations. Technology has brought the people of the world into almost instant communication. Biomedical science has conquered most infectious, acute diseases and has provided several medicines for the successful maintenance therapy of chronic diseases. The lifestyle of some people as smoking and drug addiction remains a strong factor in their achievement of the health goals of the good life. Universally, economic (...) inflation and international business have created an economic interdependence among nations and dealing with the economic problems of one country must take into account this interdependence. When one views the future within this technologic-social-economic framework, he has difficulty sorting out certain directing influences that may shape the future of pharmacy practice in Libya. (shrink)

The chapter argues that radical contextuality, a hallmark theme of “postmodern” thought, is also a key element of Heidegger’s thinking. Aristotelian metaphysics, as the question of being qua being, looks for a universal principle common to every particular instance of “to be.” By contrast, the postmetaphysical approach gradually developed by Heidegger basically addresses being as the irreducible context-sensitivity and singularity of a meaningful situation, understood as a unique focal point of a dynamic and complex meaning-context. The fundamental ontology of Being (...) and Time (1927) articulates the temporally contextual structure of the human being as Dasein, culminating in the concept of the multidimensional singular instant (Augenblick). After the failure of his attempt to articulate the temporally contextual singularity of being in terms of its correlation with Dasein, Heidegger attempts an inverted approach: Contributions to Philosophy (1936–38) considers Dasein as the spatiotemporally situated place of the singular event (Ereignis) of being. The chapter finally suggests an interpretation of the fourfold (Geviert) of sky, earth, gods, and mortals, allusively introduced in the 1949 Bremen lectures, as Heidegger’s most elaborate account of the multidimensional structure that makes possible the contextual singularization of meaningfulness. (shrink)

Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description logic (...) as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C(a) for some concept C” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.” . (shrink)

The paper also discusses the technical architecture, including the network protocols, data management strategies, and user interface design considerations necessary to implement such a system. Additionally, it addresses the challenges related to data security, privacy, and system interoperability. Finally, the paper outlines potential future enhancements, such as the incorporation of AI-driven predictive analytics and advanced AR features, to further elevate the smart home experience.

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging (...) advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that provide (...) personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. (shrink)

All research is immersed in the competition for knowledge, but this is not always governed by fairness. In this opinion article, I elaborate on indicators of unfairness to be found in both evaluation guides and evaluation panels, and I spontaneously offer a number of rules of thumb meant to keep it at bay. Although they are explicitly offered to the Portuguese Foundation for Science and Technology (FCT) and in particular to the evaluation panel for Philosophy, Ethics and Religion of FCT's (...) fifth edition of their Individual Call to Scientific Employment Stimulus, here used as concrete illustrations of unfairness, my rules of thumb are guaranteed to promote fairness in the competition for knowledge in general. (shrink)

The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.

The relations between ontology and information are many and fundamental, and they help us to understand the present gulf between (formal) ontology and (philosophical) Ontology: We can speak of respectively ontology-driven information and information-driven ontology as the focus on being informed vs. informed being. The question of whether these two (can) coincide is relevant to both fields, and in this article I elaborate on what needs to be addressed first of all to provide us with an answer: The form. This (...) core ontological concept rooting in Aristotelian metaphysics was central to philosophical ontology, in particular in Latin Scholasticism, when it was clearly put into relation with information as that which defines an entity. In this context, Dietrich of Freiberg synthesized this long debate in a way that matters not only to the philosophical effort of producing information-driven ontologies but also to the engineering constructs of ontology-driven information systems. (shrink)

Inflammation is said to be a healthy component of the body's immune system's reaction. Inflammation is characterised by four key symptoms: pain, redness, heat or warmth and swelling. As secondary metabolites, plants may produce a wide range of phytochemical compounds, which possess anti-inflammatory characteristics. Herbal remedies are important therapies for a wide range of ailments all over the world. There are around 7,500 species of medicinal plants, including representatives from over 17,000 flowering plant species. Even though synthetic chemistry has developed (...) its expectations, the use of natural ingredients in the manufacture of drugs used in contemporary medicine is unparalleled. By interfering with the biology of inflammation, anti-inflammatory medications may assist to minimising tissue damage and increase patient comfort. Because of the bulky figure of species reachable for study, the effective development of novel naturally taking place anti-inflammatory drugs is mostly dependent on a multidisciplinary approach to discovering new chemicals. Despite the statistic that many review papers have been produced in this field, the conventional of them simply examined the issue from an area perspective. Several non-steroidal anti-inflammatory drugs have been shown to reduce inflammation and pain by decreasing the isoform of the cyclooxygenase enzyme's digestion of arachidonic acid, hence lowering prostaglandin production. Non-steroidal anti-inflammatory drugs have a host of harmful effects. There are, however, medicinal herbs with anti-inflammatory pharmacological properties that have few or no negative effects. (shrink)

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by modern (...) applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

Postpartum depression is a major maternal health problem after childbirth. It can start at any time within the first year after delivery and continue for several years. It is characterized by an inability to experience pleasure, anxiety symptoms, panic attacks, spontaneous crying and depressed mood. Some women with postpartum depression even have thoughts of harming their child and self-harm. This study aims to find out the status of postpartum depression and the associated factors among postnatal mothers at the first, fourth (...) and sixth month. This study is a prospective descriptive study, carried out in three major health facilities in Misurata. This study consists of a three-part questionnaire. Part 1 covers the demographic characteristics of the participants, while Part 2 explores the associated risk factors according to the variables used. Part 3 consists of the psychological evaluation of the participants. A hundred mothers (ages between 15 and 43 years) were enrolled in this study. The findings revealed that the prevalence of depression is 60.0% and 22.0% are suffering from borderline depression. Development of depression was strongly correlated with sleeping disturbances, the state of the neonate after birth, excess consumption of stimulants, less weight acceptance by mothers during pregnancy and infant illness. There was a weak relation between depression and delivery state, maternal admission to the hospital during the current pregnancy, and infant gender dissatisfaction. No correlation was found with the development of depression regarding miscarriage, maternal medical problems after delivery, previous child sex, maternal age, education, parity or neonate weight. Thus, this study shows that the chance of having a miscarriage and an unhealthy neonate increases with advanced maternal age. It also shows that caesarean section is associated with bad neonatal outcomes, more weight gain and having gestational diabetes or preeclampsia during pregnancy. Maternal sleeping problems are more pronounced in the first month, attributed to the baby's care, compared with the fourth and sixth months. The study indicates that depression is a common state of psychiatric disorders among Libyan women and should have appropriate attention by physicians and gynecologists. (shrink)

Prescribing drugs for elderly patients is not an easy task since elderly patients frequently have comorbid conditions. In Libya, there are no guidelines for the management of medications used in elderly patients and no specialized geriatric health institutions. This study aims to assess the pattern of medication use among hospitalized elderly patients in Sebha Medical Centre and the drug-related problems associated with these patterns. This report is a descriptive and retrospective cross-sectional study conducted at Sebha Medical Center in 2021. Potential (...) drug-related problems were assessed based on the classification of Hepler and Strand. In this study, out of 195 participants, most patients have been given antibiotics (92.0%). The majority of patients have been prescribed anti-gastric agents, vitamins and anti-thrombotic agents which accounted for 75.0%, 62.0% and 55.0%, respectively. To a lesser extent, anti-hypertensive agents, analgesics and antihyperlipidemic were reported by 45.0%, 43.0% and 38.0%, respectively. Almost all the patients have at least one event of drug-related problem and more than three-quarters of the patients had more than one event of drug-related problems. The highest rate was untreated indications which were reported for 77.0% then followed by drug use without indication in nearly half of the events 48.0%. To fewer extent rates were reported: “in improper drug selection”, “drug interaction” and “adverse effects” which were reported for 25.0%, 23.0% and 18.0% of the total events, respectively. Almost all elderly patients have been prescribed antibiotics, analgesics and vitamins. Drug-related problems are predominantly prevalent in the clinical practice at the center. This study highlights the importance of activation of clinical pharmacist interventions at hospitals which can contribute to reducing the chance of risk of drug-related problems events among hospitalized elderly patients and ensuring rational drug prescribing in the geriatric clinical settings. (shrink)

Postpartum depression is a major maternal health problem after childbirth. It can start at any time within the first year after delivery and continue for several years. It is characterized by an inability to experience pleasure, anxiety symptoms, panic attacks, spontaneous crying and depressed mood. Some women with postpartum depression even have thoughts of harming their child and self-harm. This study aims to find out the status of postpartum depression and the associated factors among postnatal mothers at the first, fourth (...) and sixth month. This study is a prospective descriptive study, carried out in three major health facilities in Misurata. This study consists of a three-part questionnaire. Part 1 covers the demographic characteristics of the participants, while Part 2 explores the associated risk factors according to the variables used. Part 3 consists of the psychological evaluation of the participants. A hundred mothers (ages between 15 and 43 years) were enrolled in this study. The findings revealed that the prevalence of depression is 60.0% and 22.0% are suffering from borderline depression. Development of depression was strongly correlated with sleeping disturbances, the state of the neonate after birth, excess consumption of stimulants, less weight acceptance by mothers during pregnancy and infant illness. There was a weak relation between depression and delivery state, maternal admission to the hospital during the current pregnancy, and infant gender dissatisfaction. No correlation was found with the development of depression regarding miscarriage, maternal medical problems after delivery, previous child sex, maternal age, education, parity or neonate weight. Thus, this study shows that the chance of having a miscarriage and an unhealthy neonate increases with advanced maternal age. It also shows that caesarean section is associated with bad neonatal outcomes, more weight gain and having gestational diabetes or preeclampsia during pregnancy. Maternal sleeping problems are more pronounced in the first month, attributed to the baby's care, compared with the fourth and sixth months. The study indicates that depression is a common state of psychiatric disorders among Libyan women and should have appropriate attention by physicians and gynecologists. (shrink)

Puhuttaessa Ukrainan sotaan kärjistyneen Venäjän geopoliittisen ja ideologisen ajattelun filosofis-teoreettisista taustavoimista nousee toistuvasti esiin ”putinismin pääideologiksi” ja ”maailman vaarallisimmaksi filosofiksi” maalaillun Aleksandr Duginin nimi. Kuka Dugin on, millaista on hänen vaarallinen ajattelunsa ja mikä on sen yhteys suurvallan aggressioon ja hyökkäyssotaan? Seuraavassa luodaan tiivis yleiskatsaus Duginin ajattelun kahteen keskeisimpään ideologiseen elementtiin: geopoliittiseen Euraasia-ideologiaan ja Duginin radikaalikonservatiiviseen ”neljänteen poliittiseen teoriaan”. Vaikka Duginin suora vaikutusvalta on rajallinen, hänen ajattelunsa heijastelee Venäjän poliittisen eliitin ajatusmaailmaa laajemminkin.

Translated into Turkish by Mustafa Yalçınkaya.

In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make proactive decisions in hiring, (...) skill development, performance management, and retention. (shrink)

Distance education has quickly been integrated into the education system anywhere, and the pandemic has intensified the need for it. The Philippines is just one of the countries that grappled with the effects of the unseen enemy; therefore, leaning mainly to home-based learning just cope to the need for learning and continuity. This study aimed to elicit the satisfaction level of Filipino college students both enrolled in private and public higher education in the country. To proceed with the study, the (...) researchers adapted an 18-item questionnaire to find out the satisfaction level of the students in the home-based learning modality implemented in the entire country during the school year 2022 – 2023. Using the satisfaction scale adapted from Nabayra and Tambong (2023), results showed that Filipino students had an overall slight satisfaction with the implemented home-based learning modality. Moreover, the demographics, namely gender, year level, type of institution, and type of home-based learning modality, are not directly associated with students' level of satisfaction, which can only contribute less than 1% of the variance to their satisfaction. (shrink)

Kirjoitus tarkastelee Martin Heideggerin myöhäisajattelussa esiin nousevaa olemisen ainutkertaisuuden (Einzigkeit, Einmaligkeit) teemaa ja sen edelleenkehittelyä Jean-Luc Nancyn ajattelussa. Teeman osoitetaan kytkeytyvän Heideggerin välienselvittelyyn filosofian esisokraattisen alun, erityisesti Parmenideen ajattelun kanssa. Parmenides ajattelee olemista kaikkia yksittäisiä ilmentymiään, "kuolevaisten" äärellisiä "näkemyksiä" (doksai) yhdistävänä absoluuttisen homogeenisena ja itseidenttisenä ilmeisyytenä (alētheia), todellisuuden puhtaana läsnäolona ajattelulle. Tätä vasten Heidegger ajattelee olemisen nimenomaan yksittäisten ilmentymiensä kontekstuaalisena ainutkertaisuutena, mielekkyystilanteiden ainutkertaistavana kontekstualisoitumisena. Nancy jatkaa ajatusta kuvailemalla olemista "ainutkertaiseksi-monikolliseksi" (singulier pluriel), mutta täydentää Heideggerin ajatteluun tunnetusti jäänyttä aukkoa soveltamalla ajatusta (...) yhteisön ajattelemiseen ainutkertaisuuksien yhdessäolemisena ja keskinäisyytenä. Näin avautuva "ainutkertaisuuden politiikan" teema onkin viime vuosikymmeninä noussut mannermaisen poliittisen ajattelun keskiöön. (shrink)

Abstract: The rapid advancement of artificial intelligence (AI) has catalyzed significant developments in autonomous systems, which are increasingly shaping diverse sectors including transportation, robotics, and industrial automation. This paper explores the evolution of AI technologies that underpin these autonomous systems, focusing on their capabilities, applications, and the challenges they present. Key areas of discussion include the technological innovations driving autonomy, such as machine learning algorithms and sensor integration, and the practical implementations observed in autonomous vehicles, drones, and robotic systems. Additionally, (...) the paper addresses critical challenges including safety, ethical concerns, and regulatory issues that influence the deployment and acceptance of autonomous technologies. By examining current trends and future prospects, this research aims to provide a comprehensive overview of how AI is transforming the landscape of autonomous systems and to identify key areas for future research and development. (shrink)

Artikkeli käy läpi Quentin Meillassoux’n Äärellisyyden jälkeen -teoksessa esittelemiä spekulatiivisen materialismin lähtökohtia: ajatuksen Kantin jälkeistä filosofiasta hallinneesta korrelationismista ja sen ”heikosta” ja ”vahvasta” muodosta sekä Meillassoux’n perusargumentin, jolla hän pyrkii osoittamaan vahvan korrelationismin pyrkimyksen absoluuttisista viitepiisteistä luopumiseen sisäisesti ristiriitaiseksi. Tarkastelun pääpaino on Meillassoux’n väitteessä, että ajattelun riisuminen kaikista absoluuttisista näkökohdista johtaa vahvan korrelationismin omaksumaan ”fideistisen”, uskon ja järjen erillisyyttä ja keskinäistä riippumattomuutta korostavan suhtautumisen uskonnolliseen uskoon. Tällainen fideismi voi Meillassoux’n mukaan suojella tai jopa palvella ”nykypäivän fanatismia”. Artikkeli esittää joukon kriittisiä (...) huomioita Meillassoux’n fideismin ja uskon, uskonnollisten absoluuttien sekä fanatismin käsitteistä. Hänen klassiseen valistusmoderniin nojaava fideismikritiikkinsä osoittautuu toistaiseksi puutteellisesti perustelluksi ja ongelmallisesti muotoilluksi. (shrink)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates. The shift to digital learning (...) platforms has empowered students with access to a broader array of learning resources, interactive content, and personalized learning paths. However, millions of students, particularly those from rural areas or lower-income families, struggle to access these resources due to a lack of digital infrastructure, reliable internet connectivity, and affordability of devices. Furthermore, the role of teachers has evolved, requiring new pedagogical approaches and technical skills to effectively harness these digital tools. (shrink)

Results indicate substantial improvements in efficiency, accuracy, and personalized care, but also highlight the challenges of data privacy, ethical considerations, and system scalability. By providing a structured analysis, this research contributes insights into optimizing generative AI deployments for both banking and healthcare, ensuring a balance between innovation and risk management. The study concludes with recommendations for future research directions, including advanced model training, ethical guidelines, and enhanced privacy measures. These insights aim to inform practitioners on the benefits of generative AI, (...) ensuring sustainable integration into banking and healthcare ecosystems. (shrink)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates.

Optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to determine the optimal aggregation and transmission schedules, taking into account factors such as network topology, node energy levels, and data urgency. The proposed approach is validated through extensive simulations, demonstrating significant improvements in energy consumption, packet delivery ratio, and overall network performance. The results suggest that the optimized aggregated packet transmission method can effectively extend the lifespan of duty-cycled WSNs while ensuring reliable data communication. Future (...) work will explore the integration of machine learning techniques for adaptive scheduling and the application of the proposed method to heterogeneous WSNs. (shrink)

Green buildings are an integral part of the solution to the environmental challenges facing the planet. Today we use the equivalent of 1.5 Earths to meet the resource needs of everyday life and absorb the resulting wastes. This measure of our planet’s carrying capacity means that it takes Earth 18 months to regenerate what is used in only 12 months. If current trends continue, estimates suggest, by the year 2030 we will need the equivalent of two planets. Turning resources into (...) waste faster than they can be regenerated puts the planet into ecological overshoot, a clearly unsustainable condition that we all must address. The forces driving this situation are numerous. Human population has increased exponentially in the past 60 years, from about 2.5 billion in 1950 to more than 7 billion todays. Our linear use of resources, treating outputs as waste, is responsible for the toxins that are accumulating in the atmosphere, in water, and on the ground. This pattern of extraction, use, and disposal has hastened depletion of finite supplies of non-renewable energy, water, and materials and is accelerating the pace of our greatest problem—climate change. (shrink)

The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the GSM network system. The system uses various sensors to detect fire, smoke, and gas, then transmits the message using GSM module. After the message, send by the module the help arrives in 15 minutes. The (...) system is associated with water pump and a sprinkler. The Water system is associated with a water tank and a sprinkler. The water tank uses a Ultrasonic sensor to monitor the level of water level in the tank for refilling the water tank. Once fire is detected, it automatically sends the emergency message and sprinkles the water. (shrink)

The rapid adoption of telemedicine has transformed healthcare delivery, especially during the COVID-19 pandemic. This digital shift has enabled medical professionals to offer consultations and manage patients remotely, ensuring continuity of care while reducing exposure risks. However, the integration of telemedicine has presented both opportunities and challenges for doctors, particularly in terms of their work-life balance. This paper explores the digital adaptation of doctors in public and private hospitals concerning telemedicine practices and its impact on their work-life harmony. The study (...) highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient load. (shrink)

In this Ph.D. dissertation, completed at the Sorbonne, it is shown that the whole of medieval philosophy was not reduced to a realist stance: in the 13th-14th centuries, an idealist stance emerged and was developed into a full-fledged epistemological idealism, personified in the philosophers Eckhart von Hochheim and Dietrich von Freiberg. This dissertation deviates from most works in the history of philosophy by proposing to see this as a taxonomy.

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized through (...) techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)