The relationship between experience and thought is one of the distinctive problems in contemporary philosophy and has significant implications for both philosophy of mind and epistemology. John McDowell in his Magnum Opus Mind and World has argued in favour of a rational and conceptual relationship between experience and thought. In our understanding of the relationship between experience and thought, in his opinion, we fall into an “intolerable oscillation” between Myth of the Given and Coherentism. One of these pitfalls, he specifically (...) targets, is Davidson’s coherentism according to which there cannot be rational relationship between experience and thought. The point Davidson makes is that our perception of the world cannot give justification to our beliefs about the world. Only a belief, in his opinion, can justify another belief and this is considered to be one of the most controversial claims in contemporary philosophy. Both Davidson and McDowell would agree that the root of the problem pertaining to the relationship between experience and thought lies in the relationship between reason and nature (rationality and natural world). In this paper, my aim is to critically evaluate the debate between Davidson and McDowell about the relationship between experience and thought in connection with their views on the relationship between reason and nature. I will argue that a rational relation between experience and thought is necessary for our thought to have a genuine content from the external world. (shrink)

Consumers often buy products not because of their attributes per se but rather because of the ultimate benefits that these attributes provide, in turn leading to the satisfaction of ultimate values. And Apple is a type of company which understand their customers & future aspects of innovation in a way that no other companies do. That’s the reason that apple is at No. 1 place in Top 50 Most innovative companies. This article endeavors to recognize the consumer perception towards the (...) Apple products and what is Apple’s ideology. In this article the researcher has listed down 12 variables which affect the consumer behavior most while purchasing Apple products. (shrink)

The proposed system combines predictive analytics and historical data to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools (...) can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

Accelerated computing leverages specialized hardware and software techniques to optimize the performance of computationally intensive tasks, offering significant speed-ups in scientific, engineering, and data-driven fields. This paper presents a comprehensive study examining the role of accelerated computing in enhancing processing capabilities and reducing execution times in diverse applications. Using a custom-designed experimental framework, we evaluated different methodologies for parallelization, GPU acceleration, and CPU-GPU coordination. The aim was to assess how various factors, such as data size, computational complexity, and task concurrency, (...) impact processing efficiency. (shrink)

The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics. This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims or (...) assisting in medical treatments. By providing a reliable method for facial reconstruction, this technology can potentially save lives and improve outcomes for individuals involved in accidents. (shrink)

The study outlines the technical challenges encountered during migration, including data compatibility issues, schema conversion, and security compliance, as well as strategies to mitigate these obstacles. Testing and validation techniques are applied throughout the migration, highlighting essential checkpoints to confirm data accuracy and optimal performance in the Snowflake environment. Postmigration performance metrics are evaluated to illustrate improvements in query execution, scalability, and overall system efficiency compared to the legacy system. The results underscore the advantages of Snowflake’s architecture in handling high-volume, (...) concurrent queries without compromising speed or accuracy. (shrink)

The results show that the hybrid CNN-LSTM model outperforms the individual models in terms of prediction accuracy and robustness, suggesting that combining convolutional layers with recurrent units is beneficial for capturing both spatial and temporal patterns in air quality data. This study demonstrates the potential of deep learning methods to offer real-time, accurate air quality forecasting systems, which can aid policymakers and urban planners in managing air pollution more effectively.

Philosophers throughout the ages have struggled to explain the way or ways by which we can acquire knowledge about the external world. With an aim to meet the skeptical challenges regarding the possibility of knowledge, various accounts of knowledge have been developed across philosophical traditions. The worry to meet skeptical challenges is implicitly or explicitly present in almost every philosophical account of knowledge. Many philosophers, while explaining about the nature and the possibility of knowledge, have talked about placing it in (...) the space of reasons or space of justifications. So, I think one of the ways in which we can respond to skeptical challenges is by developing a proper understanding of the space of reasons and justifications where we place our knowledge. When we talk about the space of reasons, it is also important to highlight, in this context, its relationship with the natural world. I would like to emphasize in this regard that there has been a normative turn specifically in the works of John McDowell and Robert Brandom after the naturalistic turn in epistemology. But one can ask- why is there a need of a normative turn after a seemingly successful naturalistic turn in epistemology? I call them normative epistemologists those who have argued that knowledge should be understood by placing it properly in the space of reasons which is necessarily a normative space. I think John McDowell, Robert Brandom and their philosophical heroes Immanuel Kant and G. W. F. Hegel fall into this category of epistemologists. Normative epistemologists have always argued that a philosophical account of knowledge in order to meet the skeptical challenges has to place our knowledge satisfactorily in the space of reasons and various ways of placing knowledge in the space of reasons have been developed in this regard. The significant questions that have been asked in this context are- what could be the best plausible way to place knowledge in the space of reasons? Is placing knowledge in the space of reasons enough to avoid the skeptical challenges regarding the possibility of knowledge? According to some philosophers, the skeptical problems arise because of a certain misunderstanding of space of reasons i.e. the interiorization of the space of reasons. On the interiorized conception of the space of reasons, there is need of extra elements beyond the space of reasons which are required for our knowledge but are not part of the logical space. In this paper, my aim, following Kant and McDowell, is to propose a critique of interiorized conception of space of reasons and show how this conception leads to various problems regarding the possibility of knowledge. In this context, I will specifically discuss argument from illusion as a skeptical challenge for the possibility of knowledge and McDowell’s response to it. In the second part of my paper, my aim is to discuss the debate between McDowell and Brandom on the nature and extent of the space of reasons. (shrink)

Cloud computing has transformed data management and storage by providing unmatched scalability, flexibility, and cost-effectiveness. However, rising cloud storage use has raised data security and privacy issues. As sensitive data being outsourced to third-party cloud providers, security is crucial. Traditional encryption methods secure data but make data recovery difficult. Specifically, efficiently searching encrypted data without compromising security is difficult.

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We (...) examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements and strengthening ABKS against emerging security vulnerabilities are future research priorities. (shrink)

My aim in this paper is to critically evaluate the debate surrounding the distinction between phenomenal and representational character of conscious experience which is one of the important debates in contemporary philosophy of mind and consciousness studies. The main objective of this paper is to seek an answer to the question- whether the content of conscious experience is phenomenal or intentional, or both? In the introduction, I will introduce the phenomenal and representational as two significant properties of consciousness. In the (...) first part, I will articulate the debate regarding the distinction between phenomenal and representational character of consciousness. Here I will argue that the phenomenal and representational properties of experience are not mutually exclusive of each other as many philosophers have taken them to be and one can be understood in relation to the other. In relation to the above distinction, many theories of consciousness have been developed emphasizing on the one or the other property of consciousness. My second objective in this paper is to argue against the strong or reductive representationalism. In relation to this, I will discuss Michael Tye’s position as a representative of strong representationalism. In the third part, I will argue against it drawing sources from the works of various non-reductive representationalists particularly from Tim Crane’s version of representationalism. In the final section, I will try to defend a position which may be called phenomenological representationalism where I will try to reconcile the divide between phenomenology and intentionality. In this context, I will argue, on the one hand, against the Cartesian notion of subjectivity which leads to pure phenomenal consciousness and, on the other hand, against the physical explanation of consciousness which leads to strong representationalism. (shrink)

The rapid advancement of artificial intelligence (AI) technologies has revolutionized various industries, including the realm of education and career guidance. This project endeavors to harness the power of AI to develop a sophisticated career guidance application that offers personalized and effective recommendations to students and job seekers. The primary objective of this project is to address the limitations of traditional career guidance methods, which often lack customization and fail to adapt to individual preferences, skills, and aspirations. Through the integration of (...) AI algorithms, NLP techniques, and ML models, our application aims to provide accurate interest assessments, skill matching, and tailored educational pathways. The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. T. (shrink)

My aim in this paper is to critically evaluate the debate between John McDowell and Michael Friedman on the nature of understanding and relativism. McDowell in his magnum opus Mind and World, has argued in favour of the view that the way we are open to reality is constituted by concepts. According to him, our openness to reality is placed in the space of reasons which is nothing but space of concepts. Friedman in his critical and detail review of McDowell’s (...) book has taken McDowell’s project in his book in particular and in his philosophy in general as the project of not constructing philosophical theories but of exorcising the philosophical traditions. It is indeed so as McDowell himself has acknowledged because he closely follows Wittgensteinian quietism in his approach to various philosophical problems. While explaining how our knowledge and perception of the world among other things are placed in the space of reasons, towards the end of his book, McDowell relates space of reasons to language and tradition. Space of reasons, in his opinion, can be understood, in terms of initiating ourselves into language and inheriting the tradition. He thus says, “…the sort of language into which human beings are first initiated, serves as a repository of tradition, a store of historically accumulated wisdom about what is reason for what.” And this is Gadamer in significant sense. (shrink)

By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve (...) response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

This study explores the role of government policies, public-private partnerships, and emerging technologies in bridging the digital divide, ensuring inclusive and equitable access to quality education for all students. By analyzing case studies and statistical data, this research identifies key areas where the EdTech revolution can be leveraged to close gaps in the education system while fostering innovation in pedagogical practices. Finally, the paper presents recommendations for ensuring that the EdTech revolution contributes to systemic equity, rather than exacerbating existing disparities.

The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be (...) made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

A wide variety of two-wheelers of all category light- weighted, medium – weighted and heavy weighted vehicles have been introduced in the market. The objective of the study is to know the preference and satisfaction of women consumers over two-wheelers and the various aspects, which determines the purchase or buying behavior and to know the expectations of women consumers over two-wheelers. The sample size of the study was conducted in Tiruchirappalli city with 75 respondents through Non-Probability Random Sampling Method. The (...) tools and techniques used were simple percentage, chi-square and ANOVA. (shrink)

The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection system's decision-making process. The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content (...) based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. (shrink)

Furthermore, the paper delves into the role of hospital management and policy in easing the digital transition and fostering a more harmonious work-life balance. By analyzing technological tools and frameworks in telemedicine, the research identifies areas where improvements can be made, offering recommendations for enhancing doctors' digital efficiency while promoting better work-life harmony. This study contributes to understanding how technology can be harnessed to benefit healthcare professionals, particularly in managing the dual demands of professional duties and personal well-being.

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This (...) study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Underwater Wireless Sensor Networks (UWSNs) are pivotal for various applications, including oceanographic data collection, environmental monitoring, and naval operations. However, the harsh underwater environment poses challenges in designing efficient routing protocols, especially concerning energy consumption and data transmission reliability. This paper proposes an optimized depth-based routing protocol for energy-aware data transmission in UWSNs, focusing on minimizing energy usage while ensuring robust data delivery. The protocol dynamically adjusts transmission power based on node depth and residual energy, reducing communication overhead and prolonging (...) network lifetime. (shrink)

The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices (...) for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

Grounding chains are usually taken to be top-ist, grounded in the largest entity (the cosmos), or bottom-ist, grounded in the smallest entity(ies) (mereological atoms). Between these two, middle-ism, the view that grounding chains terminate in middle-sized entities (like iPhones, toasters, amoebae), is almost never considered. Sarah Bernstein in her paper Could a middle level be the most fundamental? argues for the plausibility for middle-ism. In this paper I argue against Bernstein in that middle-ism is a view much more problematic than (...) top-ism or bottom-ism. This is shown in three ways. The paper first shows how the middle level itself is problematic. The second problem is that middle-ism leads to a contradiction with respect to the notion of relative fundamentality, and third, middle-ism creates fatal problems for the parthood relation. Through these three issues, the paper shows how middle-ism is problematic in lieu of having fatal problems which refrain it from serving as the theory to explain fundamentalia and subsequently grounding structures. (shrink)

Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration, agents can use either the classical Continuous Prisoner's Dilemma or a new type of interaction called moral interaction, where agents donate or steal from other agents. We introduce moral interactions to (...) model ethical decision making. We also propose a novel agent type, called virtue agent, parametrised by the agent's level of ethics. Virtue agents' decisions are based on moral evaluations of past interactions. Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them to those of virtue agents. (shrink)

The integration of robotics and stock management systems has revolutionized warehouse operations, offering greater efficiency, accuracy, and flexibility. This paper presents an optimal design for integrating robotic systems with stock management for pick-and-place operations in warehousing environments. The primary objective of this research is to enhance the precision and speed of stock handling while minimizing human intervention and error. Our design incorporates state-of-the-art sensors, real-time tracking systems, and autonomous robots programmed with advanced algorithms for object identification, gripping, and movement.

The Internet of Things (IoT) wireless LAN in healthcare has moved away from traditional methods that include hospital visits and continuous monitoring. The Internet of Things allows the use of certain means, including the detection, processing and transmission of physical and biomedical parameters. With powerful algorithms and intelligent systems, it will be available to provide unprecedented levels of critical data for real-time life that are collected and analyzed to guide people in research, management and emergency care. This chapter provides a (...) quick overview of IoT features and how they relate to wireless discovery and technology to deploy the medical applications you need. In the world, the revolution in any industry is to connect your products and devices to the Internet and make them independent and remotely connected, so that anyone can use and view them from anywhere and anytime. The Internet of Things provides us with a home automation system that uses smart devices to overcome this obstacle, allowing us to easily manage our appliances. A smart city is a vision to integrate a variety of information and communication solutions for residents with essential services, such as smart parking on all streets. The main motivation for using the Internet for parking objects is simply collecting data to get free parking. The IoT-based RTSSPS architecture is divided into three parts: a WSN-based smart street parking module, an IoT-based smart street parking module, and an IoT-based smart street parking module. IoT-based cloud with street parking algorithm, rating and future directions. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated by deep (...) belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm. (shrink)

This volum includes 37 papers of mathematics or applied mathematics written by the author alone or in collaboration with the following co-authors: Cătălin Barbu, Mihály Bencze, Octavian Cira, Marian Niţu, Ion Pătraşcu, Mircea E. Şelariu, Rajan Alex, Xingsen Li, Tudor Păroiu, Luige Vlădăreanu, Victor Vlădăreanu, Ştefan Vlăduţescu, Yingjie Tian, Mohd Anasri, Lucian Căpitanu, Valeri Kroumov, Kimihiro Okuyama, Gabriela Tonţ, A. A. Adewara, Manoj K. Chaudhary, Mukesh Kumar, Sachin Malik, Alka Mittal, Neetish Sharma, Rakesh K. Shukla, Ashish K. Singh, Jayant (...) Singh, Rajesh Singh, V.V. Singh, Hansraj Yadav, Amit Bhaghel, Dipti Chauhan, V. Christianto, Priti Singh, and Dmitri Rabounski. (shrink)

Among the terrestrial species, the majority 332 species is represented by small Volant and non Volant species belonging to the orders Vandeleuria is a small genus of rodent also known long-tailed climbing mouse. Vandeleuria Nilagirica is a Subspecies of Vandelueuria Olaracia species is divided by geographical variations and morphological characterized described namely dumeticola Hodgson, Nilagirica Jerodon, Spandicea Ryley, Rubida Thomas, Modesta Thomas, and Nolthenii Phillips as its Sub Species and the synonymized wrought on Ryley with the nominate sub species and (...) Provensis Hodgson, badius Blyth and marica Thomas with dumesticola Hodgson (V.V Agarwal and Chakraborty 1980). Vandeleuria nilagirica is longer tails, arboreal,It is very shy Nocturnal and omnivore, feeding is usually the spruce leaves and small flower sand buds of small plants that includes small insects also. IUCN status of this species is Endangered. This species is Endamic to the tropical forest along the Northern Western Ghats, Nilgiri Hills (Musses & Carleton 2005). Species altitudinal range is 900 to 2100 M. Theses are found in mountainous areas where there is no hassle. It is Listed as a under scheduled ‘V’ of the Indian wild life Act. As compared to other zones, the frequency of occurrence of F. pennanti and B. bandicota was found to be very low at all the localities in southeastern Rajasthan. Some of the hills have been afforested and the plantations have a very low herbaceous ground cover. This may be one of the reasons of low occurrence of the long-tailed tree mouse, Vandeleuriaoleracea. Suncus stoliczkanus, the Anderson’s shrew was found to be absent in this zone (Ishwar prakash & Himmat Singh 2001). In Thar desert Maximum number of Vandeleuria oleracea 66.6% Species Was observed ( Prakash I,PratapSingh 2005) .Human Disturbance, floods, habitat loss & degradation found to be major threat to these species. This small mammal is plays in important role in the Environment. (shrink)