The Grievance Management System is a web-based platform designed to enhance the efficiency and transparency of handling public grievances. By introducing role-based access for users, moderators, and government officials, the system ensures that grievances are systematically reviewed, prioritized, and resolved. Users can submit grievances, track their status, and receive notifications regarding updates. Moderators are tasked with verifying the validity of each grievance and assigning it a priority level before passing it on to government officials for action. Government officials, in turn, (...) have the ability to filter grievances by priority and submission date, helping them manage and resolve issues more effectively. The system integrates technologies such as Flask for backend logic, MySQL for database management, and natural language processing (NLP) to classify grievances into relevant departments. It also prevents rejected grievances from being re- submitted and delivers real-time notifications to users, ensuring an efficient and transparent grievance handling process. This project aims to improve the accountability and responsiveness of government departments in addressing public complaints. (shrink)

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

The main objective of this project is to improve an existing real-time vehicle tracking system by improving its control circuit with GPS and a microcontroller. It uses GPS technology to track the vehicle location; the position data of the vehicle is updated in a real-time manner so that monitoring is done effectively. also features remind that can be set to alert when entering or leaving a chosen virtual perimeter of the car. The purpose of the project is to offer the (...) consumer an accurate and inexpensive solution of vehicle tracking that allows to determine location, perform geofencing, and be used for security or planning, both for commercial fleets and individuals. (shrink)

. In this article, we propose an innovative method that utilizes AI for the real-time detection of humans and automatically triggers an alarm using the latest object detection methods based on algorithms such as YOLO (You Only Look Once). The key goal of the system is to facilitate rescue operations, enhance security surveillance, and minimize the turnaround time for any emergency actions. This allows, for example, the placement of such a system in a live video stream, and the person will (...) be located in another area of the feed and an alarm triggered. This paper concentrates on the development of the system from its conception to its testing and its roadmap for future growth to ensure that its performance is guaranteed in real-life situations. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on (...) developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

This paper examines the challenges and lessons learned for Property and Casualty (P&C) insurers as they look to balance technological innovation against regulatory compliance, specifically regarding privacy laws. These technologies have been adopted rapidly, from Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT), to transform how claims are processed, resulting in efficiencies, improved fraud detection and increased customer-centeredness. While these advancements have brought much to the table, they hold great fear regarding the data privacy, security, and compliance involved. (...) Insurers need to incorporate privacy-centric frameworks during the development and deployment of new technology to help them navigate these challenges. Our key strategies are adopting the Privacy by Design approach, ensuring compliance-focused innovation via regulatory sandboxes, and constantly monitoring the evolution of privacy regulations. Mitigating privacy risks is possible only when you have effective governance, such as having robust data governance policies and third-party risk management. Additionally, leadership is committed to helping create an environment that is privacy-centric, and providing employee training and transparency in terms of communication with the customer is equally important to sustaining trust. The paper uses a case study of Shift Technology’s AI-based fraud detection system to show how innovation can meet privacy and compliance goals. Finally, the final section concludes with a discussion of emerging privacy risks (cybersecurity threats and algorithmic bias), along with identifying the crucial role of a strong organizational culture in addressing these threats. At the end of the day, this paper emphasizes the importance of adapting privacy-preserving technologies, working with regulators, and monitoring privacy practices to ensure continuous compliance and create innovation in claim processing. (shrink)

Property and Casualty (P&C) insurers are adopting multi-cloud environments as a strategic imperative because of their increasing data volumes and complexities in regulatory compliance, customer expectations and technological advancements. This paper discusses how Snowflake’s cloud-agnostic, unified platform enables insurers to create resilient, efficient, and compliant multi cloud data strategies. Using Snowflake’s elastic scalability, real-time analytics, secure data sharing and seamless cloud interoperability, insurers can optimize claims processing, augment fraud detection, and support customer engagement. The study offers core design principles for (...) multi cloud data architectures and sheds light on problems in data fragmentation, latency, and security risks. It points to how Snowflake enables data resilience, including data replication, encryption, GDPR, and CCPA compliance. Details are provided. Practical implementation strategies of phased data migration ETL pipelines and integration with existing enterprise system to ensure a smooth deployment. Snowflake is seen as a clear win through a cost-benefit analysis with metrics like RTO and RPO that define how well Snowflake performs disaster recovery and business continuity. Additionally, the paper also looks at the emergence of tech like AI, blockchain and edge computing which could additionally bolster multi-cloud strategies even further. Finally, Snowflake enables the journey for P&C insurers through cloud ecosystem pitfalls, enabling operational resilience, regulatory compliance, along competitive advantage in a data-driven industry. (shrink)

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

P&C insurers have an important role in addressing financial risk management needs but now struggle to respond to the new forms of risk. Historical analysis and actuarial calculations, which form the backbone of classical approaches to risk measurement and management, are not well suited to such new kinds of risks as climate change, cyber risks, and business cycle risks. These conventional approaches are also a static method for selling, which has limited potential in changing quickly with new market and consumer (...) changes. However, the development of AI, big data, or advanced predictive analytics has provided a new paradigm to such challenges to cope with and analyze big data dynamically to derive Applying a combination of best Machine Learning (ML), natural language processing (NLP) and other AI techniques, it will become possible to predict risks exposures, as well as optimize underwriting and claims processing. In this paper, the author investigates the opportunities that AI carries within the sphere of reformation of risk evaluation approaches in P&C insurance. In particular, it discusses the ever-emerging field with AI models integrated into operations, underwriting, fraud detection, claims modelling, and loss computation. The applied research uses a comprehensive bibliographical review to present past and present practices and assess modern AI implementations. This paper uses research approaches and case studies to prove that AI can solve some problems and bring cost-saving solutions. Moreover, it emphasizes the ability of predictive analytics to enhance customer satisfaction at the policy level as well as optimize operations. However, the paper also explores the dirty side of implementation, including the stipulations surrounding compliance and data privacy and thus provides an objective analysis of the future of AI use in the P&C Insurance Industry. (shrink)

The Upanishads reveal that in the beginning, nothing existed: “This was but non-existence in the beginning. That became existence. That became ready to be manifest”. (Chandogya Upanishad 3.15.1) The creation began from this state of non-existence or nonduality, a state comparable to (0). One can add any number of zeros to (0), but there will be nothing except a big (0) because (0) is a neutral number. If we take (0) as Nirguna Brahman (God without any form and attributes), then (...) from where and how did the universe come into existence? -/- The neutral power (0) cannot produce anything without having an element of duality in it. Although Nirguna Brahman is neutral, it has a positive, negative, and neutral pole, constituting its Prakriti or nature. Prakriti has three latent Gunas (modes or qualities): Satva, Rajas, and Tamas. They are related to Gyana Shakti (the power of knowledge) Sankalpa Shakti (the power of ideation), and Kriya Shakti (the power of action). Science says that Atom is the basic element from which the universe evolved. The Atom has three nuclei- electron, pluton, and neutron. The Satva, Rajas, and Tamas in Indian spirituality are nothing but the mystical names for the nuclei of an atom. -/- According to Bhagavata Purana, Prakriti is also constituted of the elements of Time, Karma (action/destiny) and Swabhava (innate nature). Time disturbs the equilibrium of Gunas. From the aspect of Karma is produced an entity called Mahat, in the form of intelligence. Mahat is dominated by Satva and Rajas. From Mahat manifests the next evolute dominated by Tamas with three predominant qualities – Dravya (substance), Kriya (action), as well as intelligence. It forms to become the Ego principle (Ahamkara). -/- Ahamkara has three modes – Satvic, Rajasic and Tamasic. Satvic is Jnana oriented, Rajasic action-oriented and Tamasic Dravya (substance) oriented. From the Tamasic Ahankara, five gross elements are produced (ether, air, fire, water, and earth - Akash, Vayu, Agni, Jal and Prithvi). From the Satvic Ahamkara, guardian deities (the sun and moon, deities of sense organs, and organs of action) and from Rajasic Ahamkara, ten senses (Indriyas), five senses of perception, five organs of action, the faculties of intellect and Prana (life breath) are produced. -/- Bhagavata says that since these energies, elements, and faculties remained disassociated, they were combined to form a Cosmic Egg. The egg floats in the primal waters for a thousand years. Then God enters this Cosmic Egg and manifests himself as the Cosmic Purusha. He is the first nucleus, the God particle equivalent to the number (1) which is the embodiment of everything in the universe. The concept of creation and dissolution in Hinduism can be compared to the waves in an ocean that appear and disappear incessantly. The Manvantaras are such successive episodes of creation emerging from the Cosmic Person, the Manu who is embodied God-Consciousness, God himself. These episodes of creation are measured in Hinduism in terms of Manvantaras, the epochs of Manus. -/- Manu is the ‘First-Born’ (1) of God, the Cosmic Purusha from whom the world has originated. This Cosmic Person (Purusha) has been described as having fourteen biospheres (heavens) that are inhabited by various life forms in the order of evolution of consciousness. If we begin from the human biosphere (Bhuloka), there are ten astral biospheres that a man must transcend to attain liberation or Mukti from the cycle of births and deaths. -/- The number (1) produces the primary numbers up to (9) through the transmutation of the creational energies and qualities. The process can be related to the descent and cyclical evolution of (1) through ten spiritual stages, finally merging with the nondual (0). The number (10) marks the merger of (1) with (0). The Hindu worldview is that all life forms in an episode of creation must evolve to become one with the non-dual state (0) to attain liberation from the cycle of births and deaths in a cyclical process. -/- . (shrink)

What happens to the inner light of consciousness with the death of the individual body and brain? Reductive materialism assumes it simply fades to black. Others think of consciousness as indicating a continuation of self, a transformation, an awakening or even alternatives based on the quality of life experience. In this issue, speculation drawn from theoretic research are presented. -/- Table of Contents Epigraph: From “The Immortal”, Jorge Luis Borges iii Editor’s Introduction: I Killed a Squirrel the Other Day, Gregory (...) M. Nixon iv-xi Research Essays The Tilde Fallacy and Reincarnation: Variations on a "Skeptical" Argument Teed Rockwell 862-881 Death, Consciousness, and Phenomenology, Steve Bindeman 882-899 The Idealist View of Consciousness After Death, Bernardo Kastrup 900-909 Consciousness, a Cosmic Phenomenon—A Hypothesis, Eva Déli 910-930 The Theory of a Natural Afterlife: A Newfound, Real Possibility for What Awaits Us at Death, Bryon K. Ehlmann 931-950 Near-Death Cases Desegregating Non-Locality/Disembodiment via Quantum Mediated Consciousness: An Extended Version of the Cell-Soul Pathway, Contzen Pereira & J Shashi Kiran Reddy 951-968 On the Possible Existence of Quantum Consciousness After Brain Death, Massimo Pregnolato & Alfredo Pereira Jr. 969-991 Science and Postmortem Survival, Edward F. Kelly 992-1011 Explorations ISS Theory: Cosmic Consciousness, Self, and Life Beyond Death in a Hyperdimensional Physics, Chris H. Hardy 1012-1035 Does the Consciousness End, Remain Awake, or Transform After Death? Radivoj Stankovich (with Micho Durdevich) 1036-1050 Big Bang Spirituality, Life, and Death, Ken Bausch 1051-1063 Death, Consciousness and the Quantum Paradigm, Ronald Peter Glasberg 1064-1077 Living With Limits: The Continuum of Consciousness, Donald Brackett 1078-1098 Mysticism, Consciousness, Death, Mike Sosteric 1099-1118 What Dies? Eternalism and the Afterlife in William James, Jonathan Bricklin 1119-1140 Theories of Consciousness and Death: Does Consciousness End, Continue, Awaken, or Transform When the Body Dies? Roger Cook 1141-1153 It’s the Other Way Around: Matter is a Form of Consciousness and Death is the End of the Illusion of Life in the World, James P. Kowall & Pradeep B. Deshpande 1154-1208 Statements A Feminine Vision for the World Consciousness, & a New Outrageous Ontology, Lorna Green 1209-1217 The Mask of Eternity: The Quest for Immortality and the Afterlife, Iona Miller 1218-1228 Are We Really “such stuff as dreams are made on”? Chris Nunn 1229-1225 Is the Afterlife a Non-Question? (Let's Hope Not), Deepak Chopra 1226-1230 Life After Death? An Improbable Essay, Stuart Kauffman 1231-1236. (shrink)

Bu çalışmada Metin Erksan’ın 1965 yapımı Sevmek Zamanı filminin “bir resme âşık olma” hikâyesi gibi görünen anlatısının aslında dönemin varoluşçu felsefesinin Türkiye’deki özgün bir yansıması olduğu ve bunun da filmdeki kadın karakter ile kurulduğu gösterilecektir. Yazıda filmde beliren temsil, korku, özgürlük ve iletişim temaları öncelikle Platon’un mağara alegorisi ve Heidegger’in varoluşçu felsefesinin Dasein (orada-varlık) kavramına başvurularak ele alınacaktır. Filmde âşık olduğunu söylemekten çekinen Halil’in Meral’in temsiline kaçışı resmin bakışındaki iyilik ve sevgi ile açıklanır. Doğada insanların uzağında yaşayan ve çalışan Halil (...) arkadaşı Mustafa’nın kötülük uyarısı ile Meral’den kaçmaktan vazgeçip onunla konuşmaya karar verir. Halil’in kaçışındaki korkunun kaynağı Halil’in varoluşunun Meral tarafından tanınıp onaylanmaması ihtimali olsa da Meral’in Halil’in varoluşuna özgürce ve ihtimamla yaklaşarak Halil’in korkusunu daha derin bir varoluşsal kaygı ve özgürlükle karşı karşıya getirir. Yazı Meral’in Halil ile aynı moda girebilip onu anlayabilmesi ve bu güce sahip olmasının ikisi arasında iletişimi mümkün kıldığını iddia etmektedir. Yazıda en son filmin toplumsal gerçekçi yanında kadın sorununun nasıl şekillendiği ortaya konulacaktır. Meral Halil’i tanısa, kendisi Halil tarafından tanınsa bile kendi sınıfından gelen bir tanıma reddi ile karşılaşmaktadır. Bu tanımamanın ölüm kadar sert bir şiddete ulaşmasının nedeni ise ekonomik sınıfı değil cinsiyetidir. (shrink)

A discussion of the intellectual impact which Modernity has had on Islamic religious consciousness.

To develop a Unified Portal to collaborate across educational stakeholders to formulate and maintain curriculum standards for Technical Education in India. In that AICTE plays a pivotal role in shaping the landscape of technical education in India. We are going to design a portal to streamline the curriculum design process for educators. This portal incorporates various curriculum design tools to facilitate a more efficient and effective curriculum creation experience.

The object of this paper is to explore the possibility defending women's rights (or, more broadly, expressing women's concerns) within a framework of Islamic concepts and ideas. This is to be accomplished by introducing a number of methodological principles that can, and (for feminists) should govern the practice of "religious interpretation" (ijtihad) which Muslims have used throughout the centuries to adapt Qur'anic and Islamic teachings to changing realities and circumstances.

Maritime transport plays a pivotal role in global trade, yet it faces challenges due to corrosion, which deteriorates metallic surfaces of vessels, leading to potential safety hazards and financial burdens. Traditional corrosion detection methods such as visual inspections are inefficient, time-consuming, and often subjective. This paper proposes a deep learning-based solution utilizing Convolutional Neural Networks (CNNs) to detect and assess corrosion on marine vessel surfaces. Our proposed solution not only automates the detection process but also enhances accuracy, ensuring early identification (...) and effective management of corrosion. Through rigorous experimentation, the model demonstrated high accuracy, significantly improving the corrosion detection process for the maritime industry. (shrink)

Data normalization is a crucial step in database management systems (DBMS), ensuring consistency, minimizing redundancy, and enhancing query performance. Traditional methods of normalization in supermarket sales databases often demand significant manual effort and domain expertise, making the process time-consuming and prone to errors. This paper introduces an innovative machine learning (ML)-based framework to automate data normalization in supermarket sales databases. The proposed approach utilizes both supervised and unsupervised ML techniques to identify functional dependencies, detect anomalies, and suggest optimal schema transformations. (...) Experiments on supermarket sales datasets show substantial improvements in accuracy, scalability, and processing time compared to traditional approaches. The results emphasize the potential of incorporating ML into database management practices to boost operational efficiency and support better decision-making. (shrink)

In the era of exponential data growth, the efficient migration of data in automotive manufacturing systems is a critical challenge for enterprises. Traditional approaches are often time-intensive and error-prone. This paper proposes an intelligent data transition framework leveraging machine learning algorithms to automate, optimize, and ensure the reliability of data migration processes in automotive manufacturing databases. By integrating supervised learning and reinforcement learning techniques, the framework identifies optimal migration paths, predicts potential bottlenecks, and ensures minimal downtime. Experimental results demonstrate significant (...) improvements in data transfer efficiency and accuracy compared to traditional methods. (shrink)

This paper presents a machine learning-based framework for real-time short-term snowfall forecasting by integrating atmospheric and topographic data. The model uses real-time meteorological data such as temperature, humidity, and pressure, along with terrain data like elevation and land cover, to predict snowfall occurrence within a 12-hour forecast window. Random Forest (RF) and Support Vector Machine (SVM) models are employed to process these multi-source inputs, demonstrating a significant improvement in prediction accuracy over traditional methods. Experimental results show that the RF model (...) achieved a root mean square error (RMSE) of 3.2 cm, while traditional regression models achieved an RMSE of 5.1 cm. The proposed framework provides a more reliable and accurate tool for real-time snowfall prediction, especially in mountainous regions, and can be deployed for operational weather forecasting applications. (shrink)

Demand forecasting in grocery retail encounters considerable difficulties due to fluctuating consumer behavior, as well as external factors such as weather conditions and local events. This research presents an innovative framework that utilizes generative artificial intelligence (AI) to improve forecasting accuracy by incorporating various contextual elements. By employing Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), we integrate weather data, local events, and consumer behavior to better predict grocery sales. The proposed approach aims to optimize inventory management, minimizing stockouts and (...) reducing waste. Experimental results indicate that this context-aware method significantly outperforms traditional forecasting models, providing a powerful solution for improving supply chain operations in grocery retail. (shrink)

Customer feedback and reviews are rich sources of information that reflect the sentiments and experiences of consumers, especially in the financial sector. Mining customer sentiments from these textual data sources provides valuable insights for improving services, identifying emerging issues, and predicting customer satisfaction. This paper proposes a novel approach to mining customer sentiments from financial feedback and reviews, leveraging advanced natural language processing (NLP) techniques, sentiment analysis algorithms, and machine learning models. We discuss methods for preprocessing financial text data, feature (...) extraction, sentiment classification, and visualization of sentiment trends. Additionally, we explore the application of sentiment analysis in understanding customer behaviour, improving financial product offerings, and enhancing customer support strategies. Experimental results demonstrate the effectiveness of the proposed methods in accurately predicting customer sentiments and generating actionable insights. This paper also suggests directions for future research, focusing on real-time sentiment monitoring and integration with personalized financial services. (shrink)

Today e-commerce has had tremendous growth in the past years primarily due to changes in technology and customer’s buying behavior. One of the big shifts in the process has been the use of ML in advertising which has the capability to transform the marketing domain together with consumer interactions. This paper discusses the viability of using machine learning for designing realistic models of advertising to increase effectiveness of target and personalized advertising, as well as conversion rates in e-commerce. Several techniques (...) are explored in the study, such as supervised and unsupervised learning, recommender systems, and content optimization with natural language processing. By using case studies and experimental models, we discuss how and to what extent ML is beneficial in e-commerce advertising transformation. The results presented in this paper indicate that using machine learning in advertising has a potential of dramatically improving the customer experience while simultaneously increasing brand recognition and sales. (shrink)

CVDs continue to be the number one cause of death, therefore early detection and treatment is crucial. Currently, the application of Artificial Intelligence (AI) in the cardiac image analysis has become popular due to increased accuracy, productivity, and modelling. In this paper, the use of the AI system to study the echocardiogram, CT, MRI, and other images of the heart and blood vessels to view the risk factors for cardiovascular diseases is discussed. We focus on the current methods of artificial (...) intelligence, how it is implemented in clinical practice, problems, and prospects for AI development. (shrink)

Cyber security incidents pose significant threats to organizations, potentially leading to data breaches, financial losses, and reputational damage. Effective incident response is crucial for mitigating these impacts and restoring normal operations. This research article explores comprehensive strategies and best practices for responding to cyber security incidents. By reviewing existing literature, analyzing related work, and identifying research gaps, the study aims to develop a robust framework for incident response. The methodology encompasses data collection, tool utilization, and algorithm implementation to establish an (...) efficient response mechanism. Results demonstrate the framework's effectiveness in enhancing incident detection, containment, and recovery processes. The study concludes with recommendations for improving incident response strategies and outlines future research directions. (shrink)

The health industry is experiencing change, the newest forerunner of which is being propelled by spatial computing and generative AI. Spatial computing simply refers to the ability to interface with physical space through computation and digital devices; on the other hand, generative AI means using advanced machine learning to generate new output. This paper examines the roles and the combined possibilities of these two technologies with the view of transforming health care and diagnostics in the field of patient care. Precision (...) medicine unveils new opportunities of integrating precise spatial computing components and generative artificial intelligence to provide a personalized treatment for patients and data visualization for surgeons. The paper examines major use cases, concerns, and future of this convergence specifically in the fields of medicine. (shrink)

Generative AI has proven itself as an efficient innovation in many fields including writing and even analyzing data. For spatial computing, it provides a potential solution for solving such issues related to data manipulation and analysis within the spatial computing domain. This paper aims to discuss the probabilities of applying generative AI to graph-based spatial computing; to describe new approaches in detail; to shed light on their use cases; and to demonstrate the value that they add. This technique thus incorporates (...) graph theory, generative models to model spatial relations, generate new spatial forms and improve on spatial decision-making processes. The paper surveys such methods, describes typical applications, and outlines further development of the subject. (shrink)

Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) Confirm the diagnosis of (...) a brain tumor, 2) Consider the possibility of a brain tumor that has metastasized, and 3) Consider the possibility of a brain tumor. Our expert system offers a user-friendly interface, enabling users to select symptoms and receive a diagnosis based on the provided information. This paper discusses the expert system's development, implementation, and evaluation, highlighting its potential to facilitate brain tumor diagnosis and decision-making in clinical settings. Additionally, we provide a literature review that contextualizes our expert system within the broader landscape of rule-based expert systems for brain tumor diagnosis, examining their effectiveness, limitations, and challenges. (shrink)

This paper is a study of Conspiracy Theory, the theory according to which the causes which explain the occurrence of many events and phenomena are not the officially advertised causes which the public media present us with; rather, the events and phenomena in question should be viewed as the work of agents and agencies that operate in secret in the service of projects that may or not be publicly known. The paper discusses the relation between theory and evidence and compares (...) conspiracy theories to others, especially scientific theories. Two conclusions emerge. Firstly, a theory should not be rejected merely on the ground that it is a conspiracy theory. Secondly, most conspiracy theories do not deserve to be taken seriously because of the consequences which follow from their method of dealing with counter evidence. -/- يناقش هذا البحث نظرية المؤامرة من جوانبها المنطقية والمنهجية والابستمولوجية. ينتهي البحث الى هاتين النتيجتين.أولا، لا ينبغي رفض نظرية ما لمجرد كونها نظرية مؤامرة، فليس هناك من حيث المبدأ ما يمنع ان تكون نظرية ما من نظريات المؤامرة صادقة. ثانيا، ومع ذلك هناك بعض الاعتبارات التي تدعونا للتشكك في مصداقية الاغلبية الساحقة من نظريات المؤامرة المتداولة. تتعلق هذه الاعتبارات بالتبعات التي تترتب على طريقة نظريات المؤامرة في التعامل مع البينات المضادة. (shrink)

This chapter explains how two seemingly unrelated theories in the fields of morality and emotion conspire to make the notion of forgiveness seem (doubly) impossible. The discussion of the paradoxical nature of forgiveness is followed by a proposal about the relation between affect and cognition which reconciles conflicting claims and vindicates the coherence of the notion of forgiveness.

La robotique, comme plus largement le numérique, débouche sur de multiples usages aux déploiements parfois aussi massifs qu’inattendus, tel l’essor actuel des drones civils. Dans ce contexte évolutif, il serait vain d’énoncer de nouvelles normes éthiques qui pourraient vite s’avérer inadéquates. Mieux vaut équiper le monde scienti que pour que la dimension éthique devienne indissociable de l’activité de recherche, dans les communautés et les esprits. Le présent avis émet à cet effet quelques préconisations à l’attention des établissements et un ensemble (...) de préconisations ancrées dans la recherche à l’attention des scientifiques. (shrink)

Incompatibilists and compatibilists (mostly) agree that there is a strong intuition that a manipulated agent, i.e., an agent who is the victim of methods such as indoctrination or brainwashing, is unfree. They differ however on why exactly this intuition arises. Incompatibilists claim our intuitions in these cases are sensitive to the manipulated agent’s lack of ultimate control over her actions, while many compatibilists argue that our intuitions respond to damage inflicted by manipulation on the agent’s psychological and volitional capacities. Much (...) hangs on this issue because manipulation-based arguments are among the most important for defending incompatibilist views of free will. In this paper, I investigate this issue from a experimental perspective, using a set of statistical methods well suited for identifying the features of hypothetical cases people’s intuitions are responding to. Results strongly support the compatibilist view—subjects’ tendency to judge that a manipulated agent is unfree was found to depend on their judgments that the agent suffers impairments to certain psychological/volitional capacities that compatibilists say are the basis for free will. I discuss the significance of these results for the use of manipulation cases in the philosophical debate about free will. (shrink)

This study examines the impact of Islamic Work Ethic on organizational citizenship behaviors and knowledge-sharing behaviors among university employees in Pakistan. A total of 215 respondents from public sector educational institutions participated in this research. The findings suggest that IWE has a positive effect on OCBs. In other words, individuals with high IWE demonstrate more citizenship behaviors than those with low IWE. The findings also suggest a positive effect of IWE on KSBs. Individuals with high IWE exhibit more KSBs than (...) those with low IWE. The paper also discusses the theoretical and practical implications of these findings. (shrink)

The results revealed that students who remained in China experienced challenges which included anxiety, closure of campuses, lockdown, their parents’ concern regarding health issues, and not being able to meet with friends. On the other hand, students who had left China during the pandemic were confined to their home countries. This group of students experienced more severe problems than the students who remained in China. Since the transition to home countries was “unplanned,” they were not ready to readjust to their (...) native culture and were vulnerable to severe reverse culture shock. Upon returning to their home countries, international students faced a number of challenges, including readjustment to their home countries and changes in their lives in host and home countries. In addition, they lost social and academic resources, such as the disruption of study environment, losing important group memberships, financial constraints, visa expiry, graduation delay, and academic suspension. (shrink)

The object of this paper is to elaborate an understanding of Islamic law and legal theory in terms of the conceptual framework provided by Legal Positivism. The study is not based on denying or contesting the claim of Islamic law to being of divine origin; rather, it is based on the historical reality of Islamic law as part of a (once) living legal tradition, with structure, method, and theory, regardless of claims of origin. It will be suggested that Ash‘arism may (...) be taken as providing an Islamic version of Legal Positivism, and that the objections which Mu‘tazilism raised in the face of Ash‘arism were not different in substance from some of the objections that have been directed at contemporary Legal Positivism. It will also be suggested that the Ash‘arites and the Mu‘tazilites were not opposed to each other in the way they are commonly supposed to have been. Rather, they were preoccupied with different notions of what it is to be obliged (to have an obligation) to do something. (shrink)

There are many types of political culture as well as many elements to be found in each type of political culture. The present study will be limited in two ways. Firstly, we shall not deal with all the elements of political culture. We shall focus on what has been called the "Affective Attitudes" element, which we take to include feelings and emotional proclivities, which to us, are inseparable from values and evaluations. Secondly, we shall not focus on all types of (...) political cultures, but rather on democratic political culture. We plan to study one particular conceptual relation between (democratic) political culture and (democratic) political system. This is the relation of congruence. This relation is less controversial than causal relation which some theorists propose as holding between political culture and political system. However, it does suggest concepts that can be used in formulating causal hypotheses that can be empirically tested. مناقشة لطبيعة الثقافة السياسية التي تتوائم مع تتوائم مع النظام السياسي الديمقراطي. سوف يجري التركيز على التوجهات الانفعالية (المشاعر العواطف والقيم) التي تشكل جزءا هاما من الثقافة السياسية -/- . (shrink)

Cloudbursts pose a significant threat in India, especially during the South-West Monsoon season that commences in June. India's diverse climate regions, including the northern Himalayan region, Indo-Gangetic Plain, southern peninsula, and coastal areas, experience sporadic cloudbursts, with only 31 recorded instances, mainly in Himachal Pradesh, Uttarakhand, and Jammu and Kashmir. To address the lack of comprehensive Indian cloudburst data, we've curated a dataset, incorporating meteorological factors for cloudburst prediction. This dataset encompasses variables such as Temperature, Wind Gust, Wind Gust Speed, (...) Humidity, Monsoon patterns, Air Pressure, and Cloud Density. Our goal is to improve preparedness and mitigation strategies, safeguarding lives, and property in cloudburst prone areas. Employing optimized machine learning algorithms, our model analyzes these parameters alongside prevailing weather conditions, facilitating cloudburst event prediction. We evaluate the prediction performance of machine learning algorithms, including KNN. The KNN algorithm outperformed others with an accuracy of 86.18%. Moreover, we provide graphical insights into the correlation between humidity and cloudburst occurrence, emphasizing the importance of weather variables in prediction models. This research contributes to cloudburst forecasting, even with limited Indian data, and highlights the potential of utilizing diverse machine learning techniques for improved accuracy. (shrink)

محاضرة القيت في المركز العربي للدراسات وابحاث السياسات (الدوحة/قطر) في العام 2018. تعرض المحاضرة لمفاهيم مختلفة حول عبور التخصصات وتحاول ان تبين من منها يمكن ان يؤخذ بجدية ومن منها لا يصمد امام الامتحان النقدي. ينتهي المحاضر الى القول بان هناك عبور للتخصصات بمعني او معاني تتسم بالبراءة ويمكن التعايش معها، كما ان هناك مفاهيم أخرى ليست واضحة المعنى، واخرى معادية لفكرة وجود تخصصات في المقام الأول. يحاجج المحاضر ان ليس هناك مبرر قوي للتنكر للتخصصات ولا لمحو الحدود بينها، وان (...) القول بخلاف ذلك ينم عن رؤية سطحية للنظرية العلمية وشروط امكان التوصل اليها. (shrink)

his paper presents a Multilingual Translation Engine designed to automate public announcements in multiple languages. It leverages Google Translate for real-time translation and gTTS (Google Text-to-Speech) for speech synthesis. The system supports over 90 languages, enhancing accessibility in transportation hubs. The engine's architecture is built using Flask for the backend, integrated with APIs to handle translation and audio generation. Additionally, the system supports custom profiles for recurring announcements. Future enhancements could include speech recognition and advanced scheduling.

This chapter (5) focuses on the concept of the forgiving God in Islamic religion and theology and claims that Islamic thinking about divine forgiveness accommodates two different views that emphasize two different attributes of God: justice and mercy. The first view is associated with a rationalist school of theology known as Mu'tazilism, while the second is associated with a fideistic school known as Ash'arism. The author argues that the first view, which is based on a strict calculus of desert, leaves (...) little or no room for mercy, and that the second view is more true to our ordinary notion of forgiveness. (shrink)

The object of this paper is to present and discuss the way democracy is conceived of by some prominent Islamic thinkers. Their position is that democracy, rightly understood, is simply a method of dispensing, sharing, and managing political authority, and as such does not imply secularism or other values and practices that are associated with liberalism. This paper is conceived of within a broader project to theorize the relations (actual and possible) between Islam, democracy, and modernity.

This paper aims at clarifying the role which the concept of 'divine sovereignty ' plays in the discussions which are taking place among Islamic thinkers (and others) concerning the possibility of democracy in an Islamic context. It argues that 'sovereignty ' has at least two meanings, one 'f'actual', the other 'normative'. The paper also argues that the second sense of 'sovereignty ' allows us to construe ta!k o{ 'divine sovereignty' as an attempt by Islamic thinkers to go beyond the merely (...) procedural views of democracy which focus on the familiar procedures o( power contestation, election and accountability. (shrink)

The application leverages gamification and personalized recommendations to encourage ecofriendly behaviors, track environmental impact, and reward users for their commitment to protecting the planet. By integrating features such as daily challenges, educational content, and a community leaderboard, the app seeks to empower individuals to make positive changes in their daily lives while building a community of like-minded environmental enthusiasts.

My aim is to argue that forgiveness may be conceived by analogy to healing. The analogy is not self-evident, but a number of subsidiary analogies will be seen to point in its direction, or so I will argue. In the course of the discussion we shall see how injustice (and wrong-doing) may be compared to physical injury (both change the state of the sufferer to the worse), and how the resentment caused by suffering injustice may be compared to the physical (...) pain caused by injury (both are aversive, action-motivating states). The analogies will be illustrated and supported by concrete examples, ultimately suggesting that, in an important sense, when we forgive, we actually heal. (shrink)

This paper is a discussion of the ways in which the notion of public reason has come to manifest itself in recent Islamic writings. The discussion is part of an effort to discover a common language in terms of which Islamic and liberal/secular discourses about democracy and public debate can be understood. The difficult question we are left with is whether it is permissible to speak of “public reason” sans phrase, or whether the notion must always be qualified by reference (...) to culture and religion. (shrink)

Diese Einleitung liefert eine Skizze der Genese des Bandes, eine Einführung in die im Sammelband diskutierten Themenkomplexe und Fragestellungen sowie Zusammenfassungen aller in ihm enthaltenen Beiträge.

Customer feedback and reviews are rich sources of information that reflect the sentiments and experiences of consumers, especially in the financial sector. Mining customer sentiments from these textual data sources provides valuable insights for improving services, identifying emerging issues, and predicting customer satisfaction. This paper proposes a novel approach to mining customer sentiments from financial feedback and reviews, leveraging advanced natural language processing (NLP) techniques, sentiment analysis algorithms, and machine learning models. We discuss methods for preprocessing financial text data, feature (...) extraction, sentiment classification, and visualization of sentiment trends. Additionally, we explore the application of sentiment analysis in understanding customer behaviour, improving financial product offerings, and enhancing customer support strategies. Experimental results demonstrate the effectiveness of the proposed methods in accurately predicting customer sentiments and generating actionable insights. This paper also suggests directions for future research, focusing on real-time sentiment monitoring and integration with personalized financial services. (shrink)

Imaginative resistance refers to a phenomenon in which people resist engaging in particular prompted imaginative activities. Philosophers have primarily theorized about this phenomenon from the armchair. In this paper, we demonstrate the utility of empirical methods for investigating imaginative resistance. We present two studies that help to establish the psychological reality of imaginative resistance, and to uncover one factor that is significant for explaining this phenomenon but low in psychological salience: genre. Furthermore, our studies have the methodological upshot of showing (...) how empirical tools can complement the predominant armchair approach to philosophical aesthetics. (shrink)

The Home Depot data breach, which occurred in 2014, marked a significant turning point in the cybersecurity landscape, drawing attention to the increasing prevalence of ransomware and cyberattacks targeting large-scale retail organizations. This breach exposed the personal and financial information of over 56 million customers and highlighted the vulnerabilities in the systems used by major corporations. The attack was attributed to a sophisticated ransomware operation that gained access through a third-party vendor’s compromised credentials, ultimately enabling cybercriminals to infiltrate Home Depot’s (...) network. This paper explores the Home Depot breach, focusing on its relation to the rise of ransomware as a common cyber threat. It delves into the tactics, techniques, and procedures (TTPs) employed by the attackers, the vulnerabilities exploited, and the broader implications for the retail industry and cybersecurity at large. Additionally, the study examines how ransomware has evolved from a relatively niche threat to a significant weapon in the cybercriminal arsenal, affecting organizations across all sectors. By analyzing the breach and its aftermath, this paper offers insights into best practices for cybersecurity resilience and incident response strategies that can help mitigate future threats of similar magnitude. (shrink)

Generative AI is reshaping sectors like banking and healthcare by enabling innovative applications such as personalized service offerings, predictive analytics, and automated content generation. In banking, generative AI drives customer engagement through tailored financial advice, fraud detection, and streamlined customer service. Meanwhile, in healthcare, it enhances medical imaging analysis, drug discovery, and patient diagnostics, significantly impacting patient care and operational efficiency. This paper presents an experimental study examining the implementation and effectiveness of generative AI in these sectors.