Wildlife conservation and habitat monitoring are essential for maintaining biodiversity and ecological balance. Traditional conservation methods face challenges such as limited manpower, inaccessible terrain, and the need for continuous surveillance. Artificial Intelligence (AI) has emerged as a transformative tool, enabling more efficient, scalable, and real-time approaches to wildlife protection. This paper explores the use of AI technologies such as computer vision, machine learning, and bioacoustic analysis in monitoring animal populations, detecting poaching activity, and assessing habitat health. A structured framework is (...) proposed for integrating AI into conservation practices, highlighting benefits and future potential. (shrink)

:It is known that email is one of the most suitable and efficient form of communication amongst the companies and organizations for internal use and also for communication with their clients or customers. Most of us often overlook how unsafe communication through email actually is – in reality email message can be as easily read by any outsider as a postal card. Hence, communication through email is not self-evident. So, emails should be secured through various techniques such as encryption of (...) messages that can ensure its confidentiality. Many have a vague notion that emails travel from one system to other system only. In fact, the route traversed by the email to reach its destination is considerably more complicated. In simple terms, email works so that end users deploy mail servers that can store, send and transfer messages with different client programs. Mail servers in turn transmits messages through another mail servers and different proxy servers to recipient’s mail server. Receivers read the messages from their mail server with the client program in use. Consequently, the email moves through various servers and not directly from computer to computer. Thus, the intent of “The CryptoMailer” is to shield email messages sent over unreliable or untrusted networks, so that snoopers can't gain access to the confidential messages or alter their contents. This may strive to be invisible to senders and highly usable to recipients. Therefore, when we encrypt all email messages as a standard practice, hackers wishing to access our personal information have a more worthwhile task in front of them. Decryption of email messages one-by-one in search of only few messages containing sensitive information may be a daunting and tedious task that even the foremost dedicated hackers may feel is not worth the effort. (shrink)

(1) This paper is about how to build an account of the normativity of logic around the claim that logic is constitutive of thinking. I take the claim that logic is constitutive of thinking to mean that representational activity must tend to conform to logic to count as thinking. (2) I develop a natural line of thought about how to develop the constitutive position into an account of logical normativity by drawing on constitutivism in metaethics. (3) I argue that, while (...) this line of thought provides some insights, it is importantly incomplete, as it is unable to explain why we should think. I consider two attempts at rescuing the line of thought. The first, unsuccessful response is that it is self-defeating to ask why we ought to think. The second response is that we need to think. But this response secures normativity only if thinking has some connection to human flourishing. (4) I argue that thinking is necessary for human flourishing. Logic is normative because it is constitutive of this good. (5) I show that the resulting account deals nicely with problems that vex other accounts of logical normativity. (shrink)

Many philosophers suppose that sometimes we think we are saying or thinking something meaningful when in fact we’re not saying or thinking anything at all: we are producing nonsense. But what is nonsense? An account of nonsense must, I argue, meet two constraints. The first constraint requires that nonsense can be rationally engaged with, not just mentioned. In particular, we can reason with nonsense and use it within that-clauses. An account which fails to meet this constraint cannot explain why nonsense (...) appears meaningful. The second constraint requires that nonsense does not express thoughts. An account which fails to meet this constraint undercuts the critical force of the concept of nonsense. I offer an account which meets both constraints. The central idea is that to be under the illusion that some nonsense makes sense is to enter a pretence that the nonsense is meaningful. (shrink)

I provide a critical commentary regarding the attitude of the logician and the philosopher towards the physicist and physics. The commentary is intended to showcase how a general change in attitude towards making scientific inquiries can be beneficial for science as a whole. However, such a change can come at the cost of looking beyond the categories of the disciplines of logic, philosophy and physics. It is through self-inquiry that such a change is possible, along with the realization of the (...) essence of the middle that is otherwise excluded by choice. The logician, who generally holds a reverential attitude towards the physicist, can then actively contribute to the betterment of physics by improving the language through which the physicist expresses his experience. The philosopher, who otherwise chooses to follow the advancement of physics and gets stuck in the trap of sophistication of language, can then be of guidance to the physicist on intellectual grounds by having the physicist’s experience himself. In course of this commentary, I provide a glimpse of how a truthful conversion of verbal statements to physico-mathematical expressions unravels the hitherto unrealized connection between Heisenberg uncertainty relation and Cauchy’s definition of derivative that is used in physics. The commentary can be an essential reading if the reader is willing to look beyond the categories of logic, philosophy and physics by being ‘nobody’. (shrink)

This paper is about why voluntary associations, such as churches, unions and political parties, are subject to natural justice requirements in common law: in other words, why they are required to treat their members fairly. These requirements are typically imposed (under the name of procedural fairness) by public law on exercises of state authority, but voluntary associations do not exercise state authority. Voluntary associations are set up in private law, as structures of property and contract, but property and contract law (...) do not ordinarily require rights to be exercised in accordance with natural justice. What is needed is an account which locates voluntary associations within private law while adverting to what they have in common with the state: that they make and apply rules to facilitate human interaction. I propose such an account, drawing on an idea from Lon Fuller. Natural justice requirements on voluntary associations are grounded in the rule of law. (shrink)

Physical dimensions like “mass”, “length”, “charge”, represented by the symbols [M], [L], [Q], are not numbers, but used as numbers to perform dimensional analysis in particular, and to write the equations of physics in general, by the physicist. The law of excluded middle falls short of explaining the contradictory meanings of the same symbols. The statements like “m tends to 0”, “r tends to 0”, “q tends to 0”, used by the physicist, are inconsistent on dimensional grounds because “m”, “r”, (...) “q” represent quantities with physical dimensions of [M], [L], [Q] respectively and “0” represents just a number—devoid of physical dimension. Consequently, due to the involvement of the statement “q tends to 0'', where q is the test charge” in the definition of electric field leads to either circular reasoning or a contradiction regarding the experimental verification of the smallest charge in the Millikan–Fletcher oil drop experiment. Considering such issues as problematic, by choice, I make an inquiry regarding the basic language in terms of which physics is written, with an aim of exploring how truthfully the verbal statements can be converted to the corresponding physico-mathematical expressions, where “physico-mathematical” signifies the involvement of physical dimensions. Such investigation necessitates an explanation by demonstration of “self inquiry”, “middle way”, “dependent origination”, “emptiness/relational existence”, which are certain terms that signify the basic tenets of Buddhism. In light of such demonstration I explain my view of “definition”; the relations among quantity, physical dimension and number; meaninglessness of “zero quantity” and the associated logico-linguistic fallacy; difference between unit and unity. Considering the importance of the notion of electric field in physics, I present a critical analysis of the definitions of electric field due to Maxwell and Jackson, along with the physico-mathematical conversions of the verbal statements. The analysis of Jackson’s definition points towards an expression of the electric field as an infinite series due to the associated “limiting process” of the test charge. However, it brings out the necessity of a postulate regarding the existence of charges, which nevertheless follows from the definition of quantity. Consequently, I explain the notion of undecidable charges that act as the middle way to resolve the contradiction regarding the Millikan–Fletcher oil drop experiment. In passing, I provide a logico-linguistic analysis, in physico-mathematical terms, of two verbal statements of Maxwell in relation to his definition of electric field, which suggests Maxwell’s conception of dependent origination of distance and charge ) and that of emptiness in the context of relative vacuum. This work is an appeal for the dissociation of the categorical disciplines of logic and physics and on the large, a fruitful merger of Eastern philosophy and Western science. Nevertheless, it remains open to how the reader relates to this work, which is the essence of emptiness. (shrink)

This technical article explores the evolution and current state of Natural Language Processing (NLP), focusing on its fundamental components, sentiment analysis capabilities, language generation techniques, and implementation considerations. The article examines the transformation of NLP through transformer-based architectures, discussing advancements in text preprocessing, tokenization methods, and named entity recognition. It analyzes the progression of sentiment analysis from basic lexicon-based approaches to sophisticated neural architectures, highlighting improvements in contextual understanding and emotional context detection. The article also investigates modern language generation systems, (...) their architectural innovations, and practical applications. Additionally, it addresses critical implementation considerations, including computational requirements, data quality concerns, and ethical implications, providing insights into the deployment challenges and solutions in real-world NLP applications. (shrink)

This work focuses on enhancing security for transmitted e-documents, such as e-stamp papers, within wireless environments through dynamic authentication using variable circular concealment of multiple invisible signature images. This method is driven by encrypted secret keys and session-based random numbers, alongside a region-specific bit casting strategy and dynamic bit sequencing for multiple signatures to bolster security and robustness. Multi-copy signature marking on non-overlapping document areas, coupled with varied bit encoding strategies for each modified pixel byte coefficient in cover image sub-blocks, (...) further enhances protection and resilience against attacks. The reverse transformation of bit-coded pixel byte components in spatial domain ranges is preserved, allowing threshold-based bit creation to perform effectively even under attack scenarios. With secret information and random numbers transmitted to the receiver, the securely embedded signature bits can be reliably identified in their original sequence from the forward-transformed sub-block pixel elements. Experimental results validate that this approach significantly improves authentication and confidentiality, surpassing prior methods in terms of security and reliability. (shrink)

This paper is about when associations, and in particular associations that are part of the state, should be treated as legal persons. I distinguish two forms of association – those that render coherent the agency of their members and those that are group agents – and argue that only the latter should be treated as persons. Following this, I discuss the conditions under which associations that are part of the state can legitimately be group agents.

Location analytics is key to get into the current market. It deals with movements data of customer which tells about the customer’s lifestyle, their behavior through understanding the visiting pattern. A geo-fence is a virtual boundary for a real-world geographic area. A geo-fence can be generated as per user’s need by considering different radius, or a geo-fence can be a predefined set of boundaries. Geo-fencing is use of geo-fence. Geo fencing is a feature that uses the global positioning system (GPS) (...) to define geographical boundaries. It has widely increasing scope. Very few applications are dealing with geo-notifications that are intended to inform the mobile/stationary user proactively about location specific information. The technology behind proactive location-based services is called Geo-fencing. (shrink)

This comprehensive article explores the transformative journey of cloud migration in the modern IT landscape, examining its driving forces, benefits, challenges, and critical success factors. The article delves into how organizations across sectors increasingly adopt cloud technologies to enhance operational efficiency, reduce costs, and gain competitive advantages. It highlights the impact on healthcare organizations and discusses the strategic considerations necessary for successful migration. The article examines various aspects of cloud adoption, including financial implications, security concerns, technical challenges, and the importance (...) of proper planning and execution. Through detailed analysis of market trends and industry research, this article provides insights into the current state of cloud computing adoption and its future trajectory while offering guidance for organizations contemplating or undertaking cloud migration initiatives. (shrink)

This comprehensive article explores the transformative role of Customer Relationship Management (CRM) systems in modern healthcare delivery. The article examines how CRM platforms revolutionize patient care through enhanced engagement, streamlined operations, and data-driven decision-making capabilities. It investigates the significant impact of healthcare CRM on personalized care delivery, patient experience, and operational efficiency while addressing the technical and organizational implementation challenges. The article demonstrates how CRM systems facilitate better care coordination, improve patient outcomes, and contribute to financial sustainability in healthcare organizations. (...) Special attention is given to the unique features of healthcare CRM systems that differentiate them from traditional CRM platforms, highlighting their crucial role in advancing patient-centered care models and supporting healthcare providers in delivering more effective, efficient, personalized medical services. (shrink)

The management of waste has become an increasingly critical issue as urbanization and industrial activities surge globally. Traditional waste management practices are struggling to keep pace with the rising quantities and complexities of waste. This paper delves into the integration of intelligent technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and data analytics, into waste management systems. By examining current practices, methodologies, experiments, and results, this study aims to highlight the transformative potential of intelligent technologies in optimizing (...) waste management processes, enhancing efficiency, and reducing environmental impact. (shrink)

Large language models (LLMs) lead a new era of computational innovation brought forth by generative artificial intelligence (AI). Designed around transformer architectures and trained on large-scale data, these models shine in producing both creative and functional code. This work examines the emergence of LLMs with an emphasis on their two uses in content generation and software development. Key results show great mastery in daily activities, balanced by restrictions in logic, security, and uniqueness. We forecast future developments, therefore concluding with ramifications (...) for industry, education, and society. Particularly with the progress of Large Language Models (LLMs), Generative Artificial Intelligence (AI) has seen explosive expansion recently. From sophisticated software code to plain language writing, these models have shown amazing capacity in content creation. Focusing on the performance, problems, and consequences of LLMs in code and content creation, this work investigates the advent of generative artificial intelligence. We assess these models' accuracy, efficiency, and inventiveness while also attending to ethical issues and social effects. We also go over the direction LLMs will take and their possible uses in several sectors. (shrink)

The intersection of intelligent computing and linguistics has emerged as a vibrant field of study, offering innovative solutions and applications that transform how we understand and interact with language. This paper explores the diverse applications of intelligent computing in linguistics, encompassing natural language processing (NLP), computational linguistics, language modeling, speech recognition, and more. It delves into the underlying technologies, methodologies, and the impact of these advancements on various linguistic subfields. Through an extensive review of recent literature, case studies, and practical (...) examples, this paper aims to provide a comprehensive overview of the current state and future prospects of intelligent computing in linguistics. (shrink)

The integration of Artificial Intelligence in healthcare represents a transformative advancement in modern medicine, particularly in diagnostic applications and personalized treatment approaches. This comprehensive article examines the current state and future prospects of AI in healthcare, focusing on medical diagnosis, data analytics, and precision medicine. The article encompasses various applications, from imaging diagnostics and pattern recognition to personalized treatment planning, highlighting significant improvements in diagnostic accuracy, workflow efficiency, and patient outcomes. The article evaluates AI's performance across multiple medical specialties, including (...) radiology, ophthalmology, and genomic medicine, while also addressing the challenges and considerations in implementing these technologies within healthcare systems. The findings demonstrate AI's potential to enhance healthcare delivery through improved diagnostic capabilities, streamlined workflows, and more personalized treatment approaches, while acknowledging the need for careful consideration of regulatory compliance and clinical integration challenges. (shrink)

The rapid advancement of intelligent computing has revolutionized the field of economic forecasting, providing unprecedented capabilities for developing, implementing, and analyzing advanced prediction models. This paper explores the comprehensive process of harnessing intelligent computing for economic forecasting, emphasizing the critical stages of model development, integration, and evaluation. Initially, it discusses data collection and preprocessing techniques essential for building robust models, followed by the selection of suitable statistical, machine learning, and deep learning algorithms. The paper then outlines the practical aspects of (...) model implementation, including real-time data integration, deployment strategies, and scalability considerations. Further, it delves into the continuous monitoring and maintenance required to ensure model accuracy and reliability over time. The analysis section evaluates model performance using various accuracy metrics, scenario analyses, and visualization techniques to provide actionable insights. Through case studies across the financial sector, government policy, and business planning, the paper illustrates the transformative impact of intelligent computing on economic forecasting. The findings underscore the potential of advanced prediction models to enhance decision-making, anticipate economic trends, and respond proactively to dynamic economic environments. (shrink)

This comprehensive exploration of data engineering examines the rapid transformation of the field driven by emerging trends and cutting-edge technologies. The article discusses the exponential growth of data generation, projected to reach 175 zettabytes by 2025, and its implications for data management practices. It delves into key trends such as adopting cloud-native technologies, the rise of DataOps, advancements in real-time data processing, and the impact of artificial intelligence and automation on data engineering workflows. The article also investigates the growing importance (...) of serverless computing, hybrid, and multi-cloud environments and the critical role of data privacy and compliance in shaping modern data architectures. The paper aims to provide organizations with insights to leverage data as a strategic asset in the increasingly competitive digital economy by analyzing these developments. (shrink)

Software Defined Networking (SDN) is a novel network paradigm that intelligently manages the network's resources. The global view of a network is achieved through the separation of the data plane and network control. The abstraction of the network to its application and services is facilitated by the logically centralized control plane. SDN offers the potential to enhance network performance by utilizing the information exchanged between various layers in the network architecture and feedback control, which is facilitated by a global view (...) of the network. SDN enables the network administrator to configure the network from a single point using software control. The entire network can be configured through programming and is dynamically optimized based on the network status. OpenFlow-based SDN technologies offer improved orchestration of network resources, are adaptable to evolving business requirements, and reduce operational complexities. The Network Function Virtualization (NFV) paradigm virtualizes the networking functions, similar to the hypervisors used for compute functions, and implements the entire suite of networking services as Virtual Machines (VMs). This article provides a comprehensive examination of the influence of Software-Defined Networks on data centers that are enabled by Cloud Computing. This article provides a performance comparison of ONOS and ODL data controllers on a variety of parameters, including throughput, jitter, and loss. (shrink)

I offer a new reconstruction of Hegel’s criticism of Kant’s idealism. Kant held that we impose categorial form on experience, while sensation provides its matter. Hegel argues that the matter we receive cannot guide our imposition of form on it. Contra recent interpretations, Hegel’s argument does not depend on a conceptualist account of perception or a view of the categories as empirically conditioned. His objection is that given Kant’s dualistic metaphysics, the categories cannot have material conditions for correct application. This (...) leads to subjectivism in the content of experience: the subject is given an implausibly strong role in determining what is the case. Hegel’s own absolute idealism solves this problem. (shrink)

This intersection of Web3 technology and the metaverse constitutes a major leap into decentralized digital environments. Web3, which is based on blockchain technology, offers a user-centric architecture that allows users to own and control their digital assets, identities, and interactions. Blockchain ensures transparent, immutable records in the metaverse; smart contracts enable secure, trustless transactions to happen automatically. Non-fungible therapy tokens (NFTs) advance ownership online by showing original virtual items. Metaverse is a notion that envisions a communal, virtual shared environment that (...) exists beyond the physical world. This space is shared by all individuals. It is a digital world in which users can interact with computer-generated surroundings, connect with other users, and participate in a variety of activities by utilizing AR, VR, and other immersive technologies. This article investigates how the integration of Web3 technology supports distributed governance, virtual commerce, and interoperable experiences, thereby ushering a new era of immersive, fair digital places. We also look at potential adoption chances as well as challenges. (shrink)

A legal fiction is a knowingly false assumption that is given effect in a legal proceeding and that participants are not permitted to disprove. I offer a semantic pretence theory that shows how fiction-involving legal reasoning works.

In the past decade, blockchain technology has garnered significant attention from a diverse array of industries. Despite the fact that blockchain technology was initially developed for the Bitcoin P2P cryptocurrency network, there has been a recent surge in interest in its potential integration with other service sectors. In the domain of machine-to-machine commerce, distributed ledger technologies, as seen in blockchain systems, have recently been hailed as potential platforms. A decentralised network architecture, security, immutability, and transparency are among the primary attributes (...) of blockchains. The unalterable nature of blockchain ledgers enables members to rely on one another, thereby increasing performance and participation. Scalability and security are substantial challenges for blockchain technology. This study provides a comprehensive analysis of the emergence of blockchain technology and various methods for addressing the scalability issues associated with distributed ledgers. (shrink)

Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced models such Quantum Support Vector (...) Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges. (shrink)

The rapid evolution of technology, especially in the realm of smart systems, has significantly impacted modern urban development. This paper explores how smart technology is reshaping cities to be more sustainable, efficient, and livable. Smart technologies have the potential to optimize energy use, reduce carbon footprints, enhance mobility, improve urban governance, and foster more resilient infrastructures. This paper investigates the effects of smart cities in the context of sustainability, examining both the environmental and societal benefits and challenges. The paper is (...) structured into sections covering energy management, transportation, waste management, and smart governance, supported by data and case studies from leading smart cities around the world. (shrink)

MLNova is a structured platform designed to bridge the gap between theoretical knowledge and practical application in machine learning. Focused on enhancing student learning, the platform offers a path for beginners to explore key concepts in data preprocessing and model evaluation. Through interactive modules, MLNova delivers prerecorded lessons and real-world projects, allowing learners to experience hands-on engagement. This paper outlines the platform’s design, methodology, and the impact of interactive learning on improving comprehension of machine learning principles. Early feedback indicates a (...) significant improvement in user engagement and learning outcomes. (shrink)

Wine quality prediction is a significant task in the wine industry, as it helps producers and consumers determine the quality of a wine based on its chemical properties. Traditional methods of evaluating wine quality are subjective and time-consuming, relying on human tasters. However, with the advancement of machine learning (ML), it is now possible to predict wine quality in a more objective, scalable, and efficient manner. This paper explores various machine learning algorithms for predicting wine quality, evaluates their performance, and (...) demonstrates how these models can be applied to improve wine classification systems. (shrink)

There are various aspects to Internet of Things security, such as guaranteeing the safety of both the devices and the Internet of Things networks to which they connect. Many other types of equipment, including industrial robots, smart grids, construction automation systems, entertainment gadgets, and many more, are included in this, despite the fact that they were not designed with network security in mind. When it comes to securing systems, networks, and data, IoT device security must be able to resist a (...) wide range of IoT security assaults. One of the most important issues in the field of data security is the creation of intrusion detection systems (IDSs) for the Internet of Things. Client devices (edge devices) in Federated Learning utilize local data to train the machine learning model, and then send the updated model parameters to a cloud server so that they may be aggregated (rather than raw data).This paper proposes a machine learning system that employs federated learning to detect intrusions in the IoT. The FedAVG algorithm is employed to aggregate models. Models are trained locally by nodes. The models are trained and validated using machine learning methods, including Random Forest, ID3, and Support Vector Machine. The NSL KDD data set is employed to undertake experiments. (shrink)

What is the relation of logic to thinking? My dissertation offers a new argument for the claim that logic is constitutive of thinking in the following sense: representational activity counts as thinking only if it manifests sensitivity to logical rules. In short, thinking has to be minimally logical. An account of thinking has to allow for our freedom to question or revise our commitments – even seemingly obvious conceptual connections – without loss of understanding. This freedom, I argue, requires that (...) thinkers have general abilities to respond to support and tension among their thoughts. And these abilities are constituted by following logical rules. So thinkers have to follow logical rules. But there isn’t just one correct logic for thinking. I show that my view is consistent with logical pluralism: there are a range of correct logics, any one of which a thinker might follow. A logic for thinking does, however, have to contain certain minimal principles: Modus Ponens and Non-Contradiction, and perhaps others. We follow logical rules by exercising logical capacities, which display a distinctive first-person/third-person asymmetry: a subject can find the instances of a rule compelling without seeing them as instances of a rule. As a result, there are two limits on illogical thinking. First, thinkers have to tend to find instances of logical rules compelling. Second, thinkers can’t think in obviously illogical ways. So thinking has to be logical – but not perfectly so. When we try to think, but fail, we produce nonsense. But our failures to think are often subjectively indistinguishable from thinking. To explain how this occurs, I offer an account of nonsense. To be under the illusion that some nonsense makes sense is to enter a pretence that the nonsense is meaningful. Our use of nonsense within the pretence relies on the role of logical form in understanding. Finally, while the normativity of logic doesn’t fall directly out of logical constitutivism, it’s possible to build an attractive account of logical normativity which has logical constitutivism as an integral part. I argue that thinking is necessary for human flourishing, and that this is the source of logical normativity. (shrink)

Image synthesis is the process of generating new images from the ground up, frequently utilizing preexisting data or models. By definition, super-resolution methods produce supplementary image content and features that are not present in the original input in order to reconstruct a high-resolution image from a low-resolution source. Surpassing the performance attained with high-resolution images is a challenge when training or analyzing models with low-resolution images. It is not always easy to obtain a higher-resolution image. Recognizing and identifying objects in (...) low-resolution images is a challenging task. As a result, it is imperative to develop a method that simultaneously enhances the resolution of the low-resolution image and enhances its quality. Generative Adversarial Networks (GANs) and other generative models are increasingly acknowledged for their capacity to accurately replicate high-resolution counterparts. This article provides a performance comparison of DCGAN and Wasserstein GAN for image synthesis via image super resolution. The experimental endeavor employs the Set5 image data set. Wasserstein GAN outperforms DCGAN in terms of PSNR, SSIM, and VIF parameters. (shrink)

This project aims to control the speed and direction of a DC motor in four quadrants: clockwise, counterclockwise, forward braking, and reverse braking. An H-bridge motor drive IC is used to switch the motor’s polarity based on user inputs via four control switches. A slide switch enables alternative directional control. A 555 timer generates PWM pulses for speed regulation, while relays manage polarity changes and braking. In regenerative braking, the motor generates reverse Ftorque for instant stopping. The four-quadrant control is (...) achieved by varying the duty cycle of the PWM signal and switching polarity using the H-bridge IC. The paper provides an overview of DC motor operation, explaining key components and working principles. It discusses the importance of four-quadrant operation, including motoring (forward/reverse) and braking (regenerative/plugging). Various control strategies such as armature voltage control and field flux control are examined for their efficiency. Applications in electric vehicles, robotics, and industrial automation are explored, demonstrating the versatile use of DC motors in modern engineering. (shrink)

The quick expansion of the Internet of Things (IoT) has produced exponential data production requiring efficient processing solutions. Because of too high latency, limited bandwidth, and security concerns, real-time applications find conventional cloud-based architectures less suitable. Edge computing addresses these restrictions by processing data nearer the source, thus reducing latency, improving response times, and so raising overall system efficiency. Edge computing—by leveraging localized computation—allows real-time decision-making for significant IoT purposes like smart cities, industrial automation, healthcare, and autonomous autos. Filtering and (...) evaluating data at the edge before passing relevant information to the cloud helps to clear network congestion as well. This study addresses the importance of edge computing in IoT, its primary benefits, challenges, and future prospects in providing intelligent, real-time, scalable IoT systems. (shrink)

This comprehensive article explores the transformative impact of artificial intelligence technologies in the retail industry, focusing on four key areas: personalized shopping recommendations, intelligent inventory management, enhanced customer service, and data analytics. The article examines how AI-driven solutions are revolutionizing customer experiences through advanced machine learning algorithms, recommendation systems, and predictive analytics. It investigates the implementation of IoT and RFID technologies in inventory management, the evolution of conversational AI in customer service, and the growing importance of data- driven decision-making in (...) retail operations. The article draws on market analyses and industry reports to provide insights into adoption rates, market growth, and the future trajectory of AI applications in retail. (shrink)

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

Computer vision, or image recognition, analyses and interprets visual data in real-world scenarios like images and videos. AI and ML research focusses on object, scene, action, and feature identification because of its usefulness in image processing. Neural networks and deep learning have improved image recognition systems significantly in recent years. Early image recognition used template matching to identify objects. A photo is compared to a stored template using similarity measures like correlation to get the best match. There are several constraints, (...) especially with distorted, scaled, or noisy images. Template matching is computationally expensive. CNNs are the most common deep learning architecture for computer vision. CNNs automatically learn visual input hierarchies. They include pooling, convolutional, and fully connected layers. Regional patterns like shapes, textures, and edges in the input image are filtered by convolutional layers. Pooling layers reduce feature map spatial dimensions, making feature extraction consistent and robust. Thick layers, or fully linked layers, classify or regress using learning features. CNN training requires a big labelled dataset. This research article introduces a deep learning model for the classification and recognition of images. The input data for this model is the Images Data Set. The GenNet Algorithm is employed to derive critical features. The accuracy of classification results is enhanced through the use of preprocessing and feature extraction. The classification model is generated using Convolution Neural Network, AlexNet, ResNet, and VGG. (shrink)

In the digital age, chatbots have emerged as essential tools for automating communication and improving user experiences across various sectors. This paper presents a Chatbot Assistant System powered by Natural Language Processing (NLP) to provide intelligent, context-aware, and real-time responses to user queries. The system incorporates NLP techniques, such as text preprocessing, intent recognition, and entity extraction, to facilitate effective interactions. We explore the architecture, working principles, and applications of the system, along with its performance evaluation in different domains.

The public health crisis is started with emergence of new coronavirus on 11 February 2020 which triggered as coronavirus disease-2019 (COVID-19) pandemics. The causative agent in COVID-19 is made up of positively wrapped single-stranded RNA viruses ~ 30 kb in size. The epidemiology, clinical features, pathophysiology, and mode of transmission have been documented well in many studies, with additional clinical trials are running for several antiviral agents. The spreading potential of COVID-19 is faster than its two previous families, the severe (...) acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV). Apart from clinical manifestation, comorbid status is playing key role for prevalence of COVID-19 infection and mortalities. The comorbid effects associated with COVID-19 are diabetes, cardiovascular, digestive, hepatitis-B, cerebrovascular, hypertension, liver injury, coronary heart disease, cancer, rheumatoid arthritis, and neurological impairment. Antimalarial drugs (chloroquine and hydroxychloroquine), remdesivir, Tocilizumab, clopinavir/ritonavir, convalescent plasma therapy, spike protein-angiotensin-converting enzyme 2 (ACE2) inhibitors, human monoclonal antibodies, mRNA-1273, mesenchymal stem cells, Indian and Chinese traditional medicine, small molecules antioxidant, natural products and dietary supplements, high doses of vitamin-E, -C, -D, minerals, flavonoids,and IFN-beta are therapeutic intervention running to develop treatment against COVID-19. Although clinical usage of these therapeutic agents against COVID-19 is well documented, cytokine storms, absence of appropriate animal model have limited its therapeutic use. This review explores the clinical information currently available on COVID-19 on the mechanisms of infection, prevention, management, comorbid status, and current drug treatment options. (shrink)

A : In the rapidly evolving financial markets, accurately predicting stock prices is crucial for investors seeking to optimize their portfolios and mitigate risks. This project leverages machine learning techniques to develop a predictive model for stock price forecasting. We utilize historical stock price data, along with relevant economic indicators and market sentiment, to construct a robust dataset. Key methodologies include time series analysis, regression models, and advanced machine learning algorithms, such as Long Short-Term Memory (LSTM) networks, which excel at (...) capturing temporal dependencies in sequential data. The project involves comprehensive data preprocessing and feature engineering to enhance model performance. Various models are trained and evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to ensure accuracy and reliability. The results demonstrate the potential of machine learning approaches in financial predictions, revealing patterns and insights that can inform investment strategies. This research aims to contribute to the field of predictive analytics in finance, offering a framework for future studies and practical applications in stock market forecasting. cognitive radio(CR) is a transceiver which automatically detects available channels in wireless spectrum and accordingly changes its transmission or reception parameters. In this paper, it proposes an algorithm for the energy-efficient and spectrumaware communications requirements in CR network. It enables each node to determine and regulate its transmission strategy to provide minimum energy consumption without sacrificing end-to-end delay performance and also maximizes overall spectrum utilization. Spectrum sensing is one of the essential parameter to be considered in CR networks. Therefore, the security aspect of spectrum sensing should be addressed well. Using a Trust-Worthy algorithm, it improves the trustworthiness of the Spectrum sensing in CR-Networks. It implemented using Network Simulator-2. (shrink)

This paper represents the classification and analysis of sleep apnea using machine learning techniques. Now a days Sleep apnea is a sleeping disorder affecting more than 20 % of all American adults, associated with intermittent air passageway obstruction during sleep. This results in intermittent hypoxia, sympathetic activation, and an interruption of sleep with various health consequences. The diagnosis of sleep apnea traditionally involves the performance of overnight polysomnography, where oxygen, heart rate, and breathing, among other physiologic variables, are continuously monitored (...) during sleep at a sleep center. However, these sleep studies are expensive and impose access issues, given the number of patients who need to be diagnosed. There is hence utility in having an effective triage system to screen for OSA to utilize polysomnography better. In this study, we plan to explore using several machine learning algorithms to utilize pre-screening symptoms to diagnose obstructive sleep apnea (OSA). In the experimental results, it was found that Decision Tree Classifier (DTC) and Random Forest (RF) provided the highest classification accuracies compared to other algorithms such as Logistic Regression (LR), Support Vector Machines (SVM), Gradient Boosting Classifier (GBC), Gaussian Naive Bayes (GNB), K Neighbors Classifier (KNC). (shrink)

India is the leading producer of fruits worldwide. The major problem after citrus fruits consumption is their peel that are hazardous to our environment and mainly regarded as a solid waste however, they are rich sources of fibres, large amount of Vitamin C, phenolics and flavonoids which are best agents of antioxidant. In this paper, we have discussed about the orange peel waste which has many beneficial roles in our daily life. Citrus peel has the highest number of Phytochemicals such (...) as flavonoids, terpenoids, quinolines and having highest antioxidant activity as compared to the pulp and seed as well as identified from the extraction. The different Phytochemicals test analysis have been discussed in this paper (steroids testing, saponins testing) and different antioxidant test used to check the antioxidant activity of the citrus fruit peel extract are Hydrogen peroxide scavenging test, Nitric oxide scavenging test, DPPH test etc. The main objective of this paper is that the higher antioxidant activity of different mixture of Citrus fruit peel can help to reduce the generation of free radicals and can act as an anti-proliferative agent. The search for novel drugs and alternative medicine especially against cancer has led to increased research in medicinal plants. This review also indicated that Citrus peels contains antioxidant compounds which could be exploited as value added products in the food and cosmetic industry. (shrink)

Formaldehyde is one of the potent toxic industrial chemicals that are very commonly used in various industries, labs, and biological museums, etc. The toxicity of formaldehyde is well-known. It causes respiratory distress if inhaled in higher concentration thus, causing lung damage. It is also found to be mutagenic due to its rapid reactivity being a small molecule having electrophilic carbon. The current review focuses on the characteristic properties of formaldehyde, Sources, and uses of formaldehyde. This review further brings about the (...) toxicity of formaldehyde on different organs and organ systems of the body. Its genotoxicity and carcinogenicity are described. And later the various ways of decontaminating the formaldehyde are discussed to neutralize its harmful impacts. From this review, we have concluded that formaldehyde is a highly toxic chemical that needs proper protective measures while handling and should not be directly exposed. There should be proper decontaminating agents for emergencies, where there is the mass production of formaldehyde. (shrink)

The commercial VR/AR marketplace is gaining ground and is becoming an ever larger and more significant component of the global economy. While much attention has been paid to the commercial promise of VR/AR, comparatively little attention has been given to the ethical issues that VR/AR technologies introduce. We here examine existing codes of ethics proposed by the ACM and IEEE and apply them to the unique ethical facets that VR/AR introduces. We propose a VR/AR code of ethics for developers and (...) apply this code to several commercial applications. (shrink)

We present the scheduling algorithms of Distributed Real-time Systems in this analysis paper. A real-time system is a type of software where, under a given time period, we have to execute the process with a specific result. Whereas, in the general context, there is no fixed deadline. In reality, scheduling implies performing the operation according to its characteristics and scheduling is done on different processors, one is Uni-processor and one is Multiprocessor and can be performed on the distributed machine as (...) well. Make span length, processor utilization, cache performance, power consumption, task balance, and scheduling penalties are the criteria typically used to evaluate the various scheduling algorithms. The scheduling of real-time assignments consists of 2 sub-problems in the distributed and multiprocessor network: CPU role distribution and scheduling tasks on single processors. The allocation of tasks may be static or interactive. For successful task execution, we'll discuss numerous task allocation algorithms. (shrink)

: The COVID-19 epidemic commenced in December 2019 in Wuhan, China. In contrast to the Spanish flu pandemic of 1918, the death rate from COVID-19 is merely 5%. The condition is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 strain. In reaction to this catastrophe, several nations have implemented quarantines to mitigate the worldwide dissemination of the COVID-19 virus. COVID-19 vaccines will not be accessible until December 2020. A dependable identification method is essential for the early detection of COVID-19 (...) to preserve humanity. Recent studies indicate that Chest X-ray (CXR) imaging is the most expedient way for diagnosing and classifying COVID-19, and it is also more reliable than Reverse Transcription Polymerase Chain Reaction (RT-PCR) as it yields critical information regarding the coronavirus. Clinical practitioners may initiate treatment at the preliminary stage based on this CXR diagnosis. This article proposes methods for predicting the spread of the COVID-19 pandemic utilizing GLCM-CNN, a deep learning system. (shrink)

Nanoparticles have been used in numerous fields, various branches of science and engineering. These were used as a modification and to enhance the activity such as dentistry and oral investigation. The current survey uncovers that graphene oxide has been used to set up a variety of functionalized nanoparticles and progressed nanocomposites carriers. Graphene oxide shows potential in a variety of research examinations, for instance, tooth bleaching, antimicrobial activity, tooth erosion, teeth implants, toothaches, drug delivery at a specific site. All these (...) usages of graphene oxide to biomedicine are excellent, and consoling Graphene oxide use in dental science has given amazing outcomes in antimicrobial activity, bone tissue building, regenerative dental, advancing dentistry biological materials, and in the treatment of oral cancer. The biocompatibilities of graphene oxide and its nanomaterial make them approaching units in bone regeneration, osseointegration, and cell multiplication. Furthermore, its antibiofilm and antiadhesion action encouraged researchers to formulate graphene oxide for biofilm. Nanostructures are utilized advancements or exploration of dentistry, and nanocomposite is employed for oral ailment preventive drugs, prostheses, and teeth implantation. Nanomaterials are further utilized as an oral liquid, mouth wash, or medication, forestalling and facilitating some oral malady and keep up oral wellbeing. (shrink)

Meta-learning, or learning to learn, has emerged as a powerful paradigm for creating adaptive models that can quickly adapt to new tasks with minimal data. In the context of personalized healthcare, meta-learning holds the potential to revolutionize precision medicine by enabling models that can personalize treatments based on individual characteristics. These models can leverage prior knowledge across multiple patients or conditions to provide rapid and accurate predictions for new patients, improving the efficiency and effectiveness of healthcare delivery. This paper explores (...) how meta-learning techniques can be applied to personalized healthcare, addressing the challenges of patient variability, data scarcity, and the need for individualized predictions. We also discuss several meta-learning strategies, such as Model-Agnostic Meta-Learning (MAML) and Prototypical Networks, and their integration into healthcare systems. Furthermore, we present case studies in areas like chronic disease management and treatment recommendation, highlighting the promise of meta-learning in precision medicine. (shrink)

In this research, we develop a machine learning model to predict the ratings of Google Play Store applications using a comprehensive dataset. Our approach includes extensive data preprocessing, feature engineering, and model optimization using Random Forest Regressor and ensemble methods. The proposed model demonstrates significant improvements in prediction accuracy, emphasizing the importance of advanced preprocessing techniques and ensemble learning in regression tasks.