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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 -
Role of Additive Manufacturing and Thermal Spray Processed Materials in Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) Applications
Additive manufacturing (AM) significantly contributes to the development of electric vehicles (EVs) and hybrid electric vehicles (HEVs), providing lightweight, complex, and customized components. This study explores AMs role in advancing EV and HEV technology, with a special focus on integrating thermal spray coatings (TSCs) to enhance component performance. By employing TSCs in AM-fabricated components, manufacturers can improve surface characteristics, wear resistance, and corrosion protection critical factors for long-lasting EV/HEV systems. The synergy between AM and TSC enhances key parts such as battery enclosures, thermal management systems, and structural frameworks by optimizing their thermal insulation, durability, and energy efficiency. Additionally, AM enables efficient material use and lightweighting, which reduces vehicle weight and enhances energy conservation, addressing industry needs for sustainable solutions. This chapter reviews the current applications and future potential of TSC in AM components, highlighting its role in meeting the rigorous demands of the automotive sector. Findings suggest that combining AM and TSC opens pathways for advanced, sustainable EV and HEV designs, aligning with the global shift toward cleaner energy and resource-efficient manufacturing. 2026 selection and editorial matter, R. Suresh, C. Durga Prasad, Satish Kumar, K.N. Bharath, and Ajith G. Joshi; individual chapters, the contributors. -
Complex Network Articulation Points Detection and Centrality Measures
To clearly understand how network structure and function interact is a basic difficulty in the study of large networked systems. An old-fashioned idea from graph theory, called articulation points, may be used to do this. In a network, a node If removing it causes the network to become disconnected or causes more network components to get linked, it is an articulation point (AP). Single points of collapse are represented as articulation points in networks. The major goal of this research is to provide a method for identifying the articulation points and centrality measures. We can locate the articulation points considerably more quickly and effectively by using TARJANS Algorithm, which uses depth-first search. It must fulfill two requirements to qualify as an articulation point. For the root node of a DFS traversal to be an articulation point, it must contain at least two offspring nodes that are members of various sub graphs. It has been discovered that articulation points (APS) are crucial for maintaining the reliability and connection of several real-world networks. By assigning each node in the graph a scalar value based on an assumption, centrality metrics may be used to quantify each nodes significance. A fundamental centrality metric is node degree. In terms of node neighbors, it is equivalent. Hence, the more neighbors a node has, the more central and densely linked it is, and the more it affects the network by having more neighbors. ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025. -
MCCLDP: Multi Class Cotton Leaf Diseases Prediction and Classification using Deep Learning Model
Cotton plant disease detection is critical for sustainable agriculture and reducing crop losses. This paper proposes a novel Multi-Stream Attention-Guided Hybrid CNN (MAH-CNN) for accurate classification of cotton leaf diseases. The model leverages pre-trained ResNet152v2 and DenseNet-121 backbones for hierarchical feature extraction, complemented by a shallow CNN for localized texture analysis. A spatial attention mechanism enhances focus on disease-relevant regions, mitigating background noise. Features from the global and local streams are fused and passed through a lightweight classification head. The model achieves superior performance in terms of accuracy 97.32%, F1 score 98%, and specificity 100% on benchmark datasets which are available in open access, outperforming existing state-of-the-art methods. The integration of Grad-CAM provides interpretability, fostering trust in automated disease detection systems. 2025 IEEE. -
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. -
Mathematical models in waste management
Waste management is a major issue faced by municipalities all over the world. The major problem associated with the waste managements newlineis its high cost and main part of the cost comes from its collection and transportation. This problem can be effectively overcome by the application of mathematical models. newlineAn important aspect of waste management is locating facilities like truck locations, transfer stations, compost units etc. The location of facilities when collection of waste happens at multiple time periods is newlineconsidered for cost minimization. The rapid increase in the population of cities as a result of vast urbanization and the corresponding shrinking of land has given rise to increase in apartment complexes in newlineall the cities. The waste management practices here have to be planned carefully as they are sources of large quantities of waste. They are also potential sites for recycling and composting as waste management newlinepractices can be introduced at apartment level itself, so that transportation burden is less. Components like fixed cost /maintenance cost and operational costs are considered for the study in cities as well newlineas in apartments. Testing the mathematical model is done using different scenarios and the results are used to draw conclusions. These results showed that the model works best when processing facilities are nearer to the transfer stations so that there is no additional cost incurred at that point for transportation. In addition, it was clear that the cost of the transportation is brought down using the model, as the newlineamount transported to landfills decreases. newlineScheduling a set of resources to a set of jobs can be done using resource calendar, which shows the availability of resources and the various time periods at which it a particular resource is available. There are different types of jobs and various types of resources. -
Development and Implementation of Algorithm for Image Preprocessing of Microorganism
The digital revolution has changed most aspects of modern life. Nowhere has the change been more fundamental than in the field of microscopy. Researchers who use the microscope in their investigations have been newlineamong the pioneers who applied digital processing techniques to images. Vision is most powerful of the five senses of human being. Digitized visual information provides high impact on the subject. Digital image processing is concerned with the extraction of useful information from images. Visual newlineinformation from microscopic images of microorganisms is analyzed regularly. This has resulted in a need to understand and implement digital processing on microscopic images. The purpose of this thesis is to bring new digital image processing techniques for the noise removal of microscopic image of microorganisms. The digitized image processing includes image representation; improving image quality by removing noise; newlineand enhancing the quality of microscopic images. At the outset, the thesis elaborates on the concepts around microscopic images and their digital image processing. Various existing algorithms are studied for their efficacy. This thesis gives three different techniques of image processing based on the noise level in microscopic images. The thesis newlinedevelops the techniques of image processing through Simulation , which is well accepted tool in the field of engineering. MATLAB has been used in this study to simulate the image processing algorithms. The algorithms developed in the study will be helpful in everyday life through better analysis of microscopic images of microorganisms. The thesis is a contribution to the medical field with better analytical techniques. This research work overviews different image processing techniques used in the analysis of microscopic images and other type of images. After reviewing, use of microscopic imaging is presented. Special emphasis is on two types of noise called Gaussian noise and Impulsive noise is given.