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A reverse logistics model for optimization in waste collection
Sustainability has become a major concern in the development of human society. This requires solution of certain issues and involves social, technical, legislative, and other factors. An important concern is to minimize the generation of wastes, prevent environmental deterioration caused by the generation of wastes, and to enhance the value of recovery from the wastes. The reverse logistics network is helpful in this regard as its mission is to collect and transport used products and packages based on the balance of cost and environment. A good reverse logistics network is important for firms to gain more profits. This paper proposed a linear programming model for reverse logistics in which collection is done when the recyclables bin is half full. This limit can be varied from place to place, depending on the collection of recyclables. The model aimed to reduce transportation cost by setting up a schedule for collection and took into account the profit obtained by recycling. It also considered a penalty for late collection so that there is no piling up of waste, thus reducing the probability of items deteriorating due to weather or moisture content. 2015. -
Elementary Statistical Methods
This is the first book of two volumes covering the basics of statistical methods and analysis. Significant topics include concepts of research and data analysis, descriptive statistics, probability and distributions, correlation and regression, and statistical inference. The book includes useful examples and exercises as well as relevant case studies for proper implementation of the discussed tools. This book will be a valuable text for undergraduate students of statistics, management, economics, and psychology, wanting to gain basic understanding of statistics and the usage of its various concepts. The Editor(s) (if applicable) and The Author(s). under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Computational Aspects of Business Management with Special Reference to Monte Carlo Simulation
Business management is concerned with organizing and efficiently utilizing resources of a business, including people, in order to achieve required goals. One of the main aspects in this process is planning, which involves deciding operations of the future and consequently generating plans for action. Computational models, both theoretical and empirical, help in understanding and providing a framework for such a scenario. Statistics and probability can play an important role in empirical research as quantitative data is amenable for analysis. In business management, analysis of risk is crucial as there is uncertainty, vagueness, irregularity, and inconsistency. An alternative and improved approach to deterministic models is stochastic models like Monte Carlo simulations. There has been a considerable increase in application of this technique to business problems as it provides a stochastic approach and simulation process. In stochastic approach, we use random sampling to solve a problem statistically and in simulation, there is a representation of a problem using probability and random numbers. Monte Carlo simulation is used by professionals in fields like finance, portfolio management, project management, project appraisal, manufacturing, insurance and so on. It equips the decision-maker by providing a wide range of likely outcomes and their respective probabilities. This technique can be used to model projects which entail substantial amounts of funds and have financial implications in the future. The proposed chapter will deal with concepts of Monte Carlo simulation as applied to Business Management scenario. A few specific case studies will demonstrate its application and interpretation. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Big data analytics lifecycle
Big data analysis is the process of looking through and gleaning important insights from enormous, intricate datasets that are too diverse and massive to be processed via conventional data processing techniques. To find patterns, trends, correlations, and other important information entails gathering, storing, managing, and analyzing massive amounts of data. Datasets that exhibit the three Vs-volume, velocity, and variety-are referred to as "big data. " The vast amount of data produced from numerous sources, including social media, sensors, devices, transactions, and more, is referred to as volume. The rate at which data is generated and must be processed in real-time or very close to real-time is referred to as velocity. Data that is different in its sorts and formats, such as structured, semi-structured, and unstructured data, is referred to as being varied. 2024, IGI Global. All rights reserved. -
DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI
A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. The input Magnetic Resonance Imaging (MRI) image is fed to the pre-processing stage. Tumour areas are segmented by utilizing SegNet in which the factors of SegNet are biased using DTDO. The image augmentation is carried out using eminent techniques, such as geometric transformation and colour space transformation. Here, features such as GIST descriptor, PCA-NGIST, statistical feature and Haralick features, SLBT feature, and CNN features are extricated. Finally, the categorization of the tumour is accomplished based on ZFNet, which is trained by utilizing DTDO. The devised DTDO is a consolidation of DTBO and CDDO. The comparison of proposed DTDO-ZFNet with the existing methods, which results in highest accuracy of 0.944, a positive predictive value (PPV) of 0.936, a true positive rate (TPR) of 0.939, a negative predictive value (NPV) of 0.937, and a minimal false-negative rate (FNR) of 0.061%. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Autism spectrum disorder detection using brain MRI image enabled deep learning with hybrid sewing training optimization
Autism spectrum disorder (ASD) is brain enabled disorder representing behaviors in a repetitive manner and social deficits. In this paper, ASD is diagnosed using brain magnetic resonance imaging (MRI) enabled deep learning with a hybrid optimization algorithm. Also, the hybrid optimization algorithm utilized is hybrid sewing training optimization (HSTO) which trains ZFNet for ASD detection. Pre-processing of the MRI image is done by Wiener filter and the filtered image is fed for region of interest extraction. Moreover, pivotal region extraction is carried out by the proposed HSTO, which is finally allowed for ASD detection by ZFNet. The proposed HSTO is formed by combining sewing training-based optimization and hybrid leader-based optimization. Furthermore, the performance of HSTO_ZFNet is found by five performance metrics of accuracy with 95.7%, true negative rate with 92.6%, true positive rate with 93.7%, false negative rate with 68.7%, and false positive rate with75.9%. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
Even Numbered Saturdays are More Joyful for Bank Employees in India - A Critical Analysis
Journal of Exclusive Management Sciences, Vol. 5, Issue 4, ISSN No. 2277-5684 -
A simulation model to estimate the amount of waste collected in a common bin after compulsory segregation /
Mathematics Applied In Science And Technology, Vol.7, Issue 1, pp.314-318, ISSN No: 0973-6344. -
Some case studies for non-parametric tests for ordinal data /
International Journal Of Advanced Research In Engineering Technology & Sciences, Vol.2, Issue 7, pp.309-313, ISSN No: 2394-2819. -
Some notes on z-scores and t-scores /
International Journal Of Scientific Research And Management, Vol.3, Issue 4, pp.2608-2610, ISSN No: 2321-3418. -
Some case studies on importance of variables and scales of measurement in social sciences research /
International Advanced Research Journal In Science, Engineering And Technology, Vol.2, Issue 3, pp.34-37, ISSN No: 2393-8021 (Online) 2394-1588 (Print). -
Some interesting case studies using bayes theorem /
International Journal Of Scientific Research, Vol.4, Issue 4, pp.522-523, ISSN No: 2277-8179. -
Some pointers on one way ANOVA in spss /
International journal For Research In Applied Science And engineering Technology, Vol.3, Issue 9, pp.298-301, ISSN No: 2321-9653. -
Some examples in usage of parametric tests /
International Journal of Research In Commerce IT And Management., Vol.5, Issue 11, ISSN No: 2231-5736. -
A case study of "Parivarthana" - Towards zero waste /
International Journal For Research In Applied Science & Engineering Technology, Vol.4, Issue 6, pp.463-466, ISSN: 2321-9653. -
Retail landscaping in India - Challenges and strategies /
International Journal in Management and Social Science, Vol. 4, Issue 11, pp. 24-34, ISSN No. 2321-1784. -
The effect of hopeful lyrics on levels of hopelessness among college students
Hopelessness is a product of negative future expectations, negative feelings toward the future, and feeling a lack of control over future improvements. College students are seen to experience hopelessness. This study aimed to reduce levels of hopelessness in college students through an intervention that involved listening to songs having hopeful lyrics. The sample consisted of college students (N = 66), who were randomly assigned to three groups, namely the lyrics-music group, music-only group, and the control group (no intervention). The Becks Hopelessness Scale was used to measure their levels of hopelessness before the intervention and at the end of four weeks. The lyrics-music group and the music group participants were exposed to songs and instrumental tracks, respectively, twice a week, for four weeks. The Wilcoxon Signed-Rank test for related samples was used to analyze the effect of the intervention on levels of hopelessness. The KruskalWallis test was used to analyze the differences across the three groups. Results indicated that the lyrics-music group had a significant decrease in levels of hopelessness after the intervention. However, the music group and the control group showed no significant decrease. There was a significant difference between the three groups with regard to the difference score obtained from pre to post intervention. Thus, the evidence suggests that hopeful lyrics do have an effect on hopelessness and can be seen as differing from the functions of music alone. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Detection and Robust Classification of Lung Cancer Disease Using Hybrid Deep Learning Approach
Effective lung cancer diagnosis and treatment hinge on the early detection of lung nodules. Various techniques, such as thresholding, pattern recognition, computer-aided diagnostics, and backpropagation calculations, have been explored by scientists. Convolutional neural networks (CNNs) have emerged as powerful tools in recent times, revolutionizing many aspects of this field. However, traditional computer-aided detection systems face challenges when categorizing lung nodule detection. Excessive reliance on classifiers at every stage of the process results in diminished recognition rates and an increased occurrence of false positives. To address these issues, we present a novel approach based on deep hybrid learning for classifying lung lesions. In this study, we explore multiple memory-efficient and hybrid deep neural network (DNN) architectures for image processing. Our proposed hybrid DNN significantly outperforms the current state-of-the-art, achieving an impressive accuracy of 95.21%, all while maintaining a balanced trade-off between specificity and sensitivity. The primary focus of this research is to differentiate between CT scans of patients who have early-stage lung cancer and those who do not. This is achieved by utilizing binary classification networks, including standard CNN, SqueezeNet, and MobileNet. 2023 IEEE. -
Bronchop Neumonia Detection Using Novel Multilevel Deep Neural Network Schema
Pneumonia is a dangerous disease that can occur in one or both lungs and is usually caused by a virus, fungus or bacteria. Respiratory syncytial virus (RSV) is the most common cause of pneumonia in children. With the development of pneumonia, it can be divided into four stages: congestion, red liver, gray liver and regression. In our work, we employ the most powerful tools and techniques such as VGG16, an object recognition and classification algorithm that can classify 1000 images in 1000 different groups with 92.7% accuracy. It is one of the popular algorithms designed for image classification and simple to use by means of transfer learning. Transfer learning (TL) is a technique in deep learning that spotlight on pre-learning the neural network and storing the knowledge gained while solving a problem and applying it to new and different information. In our work, the information gained by learning about 1000 different groups on Image Net can be used and strive to identify diseases. 2023 EDP Sciences. All rights reserved. -
A Novel Energy-Efficient Hybrid Optimization Algorithm for Load Balancing in Cloud Computing
In the field of Cloud Computing (CC), load balancing is a method applied to distribute workloads and computing resources appropriately. It enables organizations to effectively manage the needs of their applications or workloads by spreading resources across numerous PCs, networks, or servers. This research paper offers a unique load balancing method named FFBSO, which combines Firefly algorithm (FF) which reduces the search space and Bird Swarm Optimization (BSO). BSO takes inspiration from the collective behavior of birds, exhibiting tasks as birds and VMs as destination food patches. In the cloud environment, tasks are regarded as autonomous and non-preemptive. On the other hand, the BSO algorithm maps tasks onto suitable VMs by identifying the possible best positions. Simulation findings reveal that the FFBSO algorithm beat other approaches, obtaining the lowest average reaction time of 13ms, maximum resource usage of 99%, all while attaining a makespan of 35s. 2023 IEEE.