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Role of Technology in the Development of Smart Cities
Get PDFThe key aim of this paper is to address the technical effects of smart city growth. While the smart city issue has been discussed in recent literature, it is of interest if macro ICT considerations should also be considered for determining a city\u27s technical advancement. First of all, literature analysis of a smart city is presented to accomplish this purpose. Along with a theoretical structure focused on the information community, an overview of the smart city idea is included. The ICT development of Smart Cities depends on the characteristics and features of the cities, as well as on macro-technological considerations. Cities that use information technology as a means of urban sustainability build smartness to emerge as smart cities as a source of constant growth and transformation within the urban ecosystem, pursuing a managerial and organizational vision of sustainability. The aim of this thesis is to suggest a theoretical overview of the city as a sustainable society that drives urban development and adopts a smart urban growth vision.
 
Artificial Intelligence and Machine Learning in Waste Management and Recycling
Get PDFWaste management is one of the biggest problems facing the world in any developed or developing country. An important aspect of waste management is that the waste bin in the open space is properly filled before the next cleaning process begins. This can eventually lead to various hazards such as dirt and bad odor in the area, which can lead to the spread of various diseases. Population growth has significantly reduced toilets through the waste management system. Laying garbage in public places creates a polluted environment. To eliminate or reduce waste and maintain good hygiene, it requires a waste-based waste management system. The need for proper waste management is not limited to proper collection and disposal of waste. It continues to be a waste disposal and recyclable level. Recycling is considered a major benefit because in addition to waste disposal, our reliance on immature materials is declining. By recycling metal, plastic and glass, the use of decomposing waste can extend beyond compost and manure. Metals can be reused and plastic can be mixed with clay filler, which can lead to soil compaction. After deep cleaning the glass construction material can be broken down and re-melted into new articles. This article is about machine learning and the use of artificial intelligence in the most viable areas and understanding the full need for human communication
Crowdsourced Software Testing: A Timely Opportunity
Get PDFThe concept of crowdsourcing has gained a lot of attention lately. Many companies are making use of this concept for value creation, as well as the performance of varied tasks. Despite its wide application, little is known about crowdsourcing, especially when it comes to crowdsourced software testing. This paper explores the crowdsourced software testing concept from a wider perspective ranging from a cost-benefit analysis, crowdsourcing intermediaries, and the level of expertise in the crowd. Drawing from a varied range of sources, a systematic literature review is done, where the research narrows down to ten most relevant peer-reviewed sources of high impact rating. In a comparative analysis between crowdsourced software testing and in-house testing, it is found that crowd testing has numerous advantages when it comes to efficiency, user heterogeneity, and cost-effectiveness. The study indicates that intermediaries play a key role in managing the connection between the crowd and crowdsourcing companies despite various challenges. A comparison between novice testers and expert testers reveals that both the two have their unique capabilities in their respective domains.
 
Intelligent Indexing and Sorting Management System – Automated Search Indexing and Sorting of Various Topics
Get PDFAn issue that the majority of the databases face is the static and manual character of indexing activities. This traditional method of indexing and sorting different topics is confirmed to shake the dataset performance somewhat, making downtime and a potential effect in the presentation that is normally addressed by manually indexing operations. Numerous data mining methods can accelerate this process by using proper indexing structures. Choosing the appropriate index generally relies upon the kind of operation that the algorithm performs against the dataset. Topic indexing is the operation of recognizing the principal topics covered by a document. These are helpful for some reasons: as subject headings in libraries, as keywords in scholarly articles, and as hashtags on social media platforms. Knowing a document’s topic assists individuals with deciding its importance quickly. In any case, assigning topics manually is a tedious and redundant task. This paper shows the best way to create them automatically in a way that contends with manual indexing done by humans. This paper also talks about the issues and the techniques for identifying applicable data in a huge variety of documents. The contribution of this thesis to this issue is to foster better content analysis techniques that can be utilized to describe document content with automated index terms. Index terms can be used as meta-data that defines documents and is utilized for seeking various topics. The main point of this paper is to show the way toward creating an automatic indexer which analyzes the topic of documents by integrating proof from word frequencies and proof from the linguistic analysis given by a syntactic parser. The indexer weighs the expressions of a document as per their assessed significance for depicting the topic of a given document based on the content analysis
Maximizing the Potential of Artificial Intelligence to Perform Evaluations in Ungauged Washbowls
Get PDFLong short-term memory networks (LSTM) offer precision in the prediction that has never been seen before in ungauged basins. Using k-fold validation, we trained and evaluated several LSTMs in this study on 531 basins from the CAMELS data set. This allowed us to make predictions in basins for which we did not have any training data. The implication is that there is usually sufficient information in available catchment attributes data about similarities and differences between catchment-level rainfall-runoff behaviors to generate out-of-sample simulations that are generally more accurate than current models when operating under ideal (i.e., calibrated) conditions, i.e., when using under idealized conditions. In other words, existing models are generally less accurate when working under idealized conditions than out-of-sample simulations. We found evidence that including physical limits in LSTM models improves simulations, which we believe should be the primary focus of future research on physics-guided artificial intelligence. Putting in place additional physical constraints on the LSTM models
Reducing Significances of Mesh Sensors Technologies through Dimensionality Reduction Algorithm
Get PDFIn today\u27s world, the breadth of real-time applications and networks is not limited to business and social activities. They are expanding as a field to provide improved and competitive settings for a variety of activities such as home, health, and commercial procedures. Data analytic method is used to maintain network accessibility as well as the robustness of expert services. It is necessary to clean up the data in order to reduce the computational complexity of extracting and pre-processing models. Because present approaches are sophisticated, they necessitate large computations. To this effect, the objective is to deploy a machine learning algorithm – “cuckoo search algorithm” for dimensionality reduction problems in data extraction for IoTs application. The cuckoo search-based feature extraction algorithm is a mutant algorithm that organizes itself depending on the unpredictable amount of input and generates a new and improved feature space. After the cuckoo search-based feature extraction is implemented, a few test benchmarks are provided to assess the performance of mutant cuckoo search algorithms. As a result of the low-dimensional data, classification accuracy is improved while complexity and expense are lowered
A Systematic Review of the Continuous Professional Development for Technology Enhanced Learning Literature
Get PDFThere is a large body of international research on raising the quality of education, with particular emphasis on CPD to support professional and pedagogical growth. From an educator’s perspective, there is widespread agreement that effective CPD is an important component of educational success. Therefore, it is unsurprising that research interest in this area has grown, particularly in light of the digital agenda. In a TEL context, educators report one of the main barriers to effective use is the lack of training in this area. This review of literature will examine some of the key ideas that form successful TEL CPD delivery, more specifically with relation to transformative models of CPD. Likewise, the section attempts to understand the context in which educators are operating and make sense of the challenges that relate to continuing professional development (CPD). In order to fully explore this phenomenon, personal development (PD) frameworks are explored, with a specific focus on Aileen Kennedy’s (2005) 9 typologies
Diagnosing Epidermal basal Squamous Cell Carcinoma in High-resolution, and Poorly Labeled Histopathological Imaging
Get PDFThe most appropriate method to uncover patterns from clinical records for each patient record is to create a bag with a variety of examples in the form of symptoms. The goal of medical diagnosis is to find useful ones first and then map them to one or more diseases. Patients are often represented as vectors in some aspect. Pathologists and dermatopathologists diagnose basal cell carcinomas (BCC), one of the most frequent cutaneous cancers in humans, on a regular basis. Improving histological diagnosis by producing diagnosis ideas, i.e. computer-assisted diagnoses, is a hotly debated research topic aimed at improving safety, quality, and efficiency. Due to their improved performance, machine learning approaches are rapidly being used. Typical images obtained by scanning histological sections, on the other hand, frequently have a resolution insufficient for today\u27s state-of-the-art neural networks. Furthermore, weak labels hamper network training because just a small portion of the image signals the disease class, while the majority of the image is strikingly comparable to the non-disease class. The goal of this work is to see if attention-based deep learning models can detect basal cell carcinomas in histological sections and overcome the ultra-high resolution and poor labeling of full slide images. With an AUC of 0.99, we show that attention-based models can achieve nearly flawless classification performance
Polymerization Techniques for Enhanced ZnS Nanostructure Performance in Industrial Settings
Get PDFThis study aims to optimize polymerization techniques for enhancing the performance of Zinc Sulfide (ZnS) nanostructures within polymer matrices for industrial applications. Despite the promising properties of ZnS nanostructures, their practical use is often limited by challenges related to nanoparticle dispersion and compatibility within polymers. To address this research gap, we systematically investigated in-situ, emulsion, and solution polymerization techniques. ZnS nanostructures were synthesized using a chemical precipitation method and characterized through XRD, SEM, and TEM to ensure high purity and controlled morphology. In-situ polymerization emerged as the most effective method, providing uniform dispersion and strong interfacial bonding. The optimized nanocomposites demonstrated significant improvements in mechanical strength, thermal stability, and electrical conductivity, confirmed by TGA, DSC, and UV-Vis spectroscopy. The findings underscore the critical role of tailored polymerization techniques in maximizing the industrial applicability of ZnS nanostructures. Policymakers and industry leaders can leverage these insights to develop high-performance materials for applications in electronics, coatings, and sensors, ultimately driving innovation and competitiveness in advanced manufacturing sectors
Artificial Intelligence Waves on Space Computation Management: A Review Report
Get PDFWhen writing about the history of the internet, it is important to note that aerospace was among the significant pioneers in computer networking computer network for private was used in first airline reservation system “SABRE” in 1960 for American airlines. While sage was the first computer system in the world, its deficiencies led to the development of ARPANET. These systems formed the foundations for the internet and the development of other computer programs in aerospace, any deficiency led to the invention of a new program, giving birth to programming, CAD, and CAM that brought about simulations. Aerospace computing has evolved over the years and is now carrying the whole weight of the aerospace industry. Before the launch of any space vehicle or satellite, simulation has become a necessary step, checking for weaknesses for corrections to be done on the ground. Besides, computer simulation has been essential in training, facilitating the training of pilots worldwide. This article presents more information regarding the application of AI in aerospace computing, flight simulations, and their advantages in the aerospace industry.