Volume 11 | Issue 9 |

 Abstract

Strategic Management Journal as an academic publication specialized in the discipline of strategic management. It provides a description of its birth and evolution, mission and scope, and the editors responsible for its development over time. Moreover, the article analyses some key aspects of the journal such as its main statistical data, the role in the development of the discipline, its importance and impact in the context of the broader field of management, and its growing international profile as it became global publication. Finally, the article describes the history of the SMJ Best Paper Prize in recognition of the most outstanding papers published .

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Joel bessis

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Linked Feature Set with Enhanced Logistic Regression for Online Payment Fraud Classification

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Classification Enhanced Logistic Regression. Feature Subset, Fraud Detection, Machine Learning, Online Payment system

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The recognition and early prevention of leaf diseases in time is very essential for improving the crop production. Convolutional Neural Networks are widely used in autonomous driving, computer vision, and medical imaging. Different deep learning models are proposed by the researchers for detection of diseases in plants. A novel CNN model is implemented in this paper to identify and diagnose plant diseases.The standard CNN model requires a huge number of parameters, computation cost is high and also the training time is more. Standard convolution is replaced with the depth wise separable convolution in order to minimize the number of parameters and computation cost. The implemented model is tested on the leaves of tomato crop in plant village dataset. The implemented CNN model attained classification accuracy rates of 98.45%,98.52%,99.05%,99.34%,99.46%,99.60% using standard inception V3, ResNet101V2, modified inception V3, EfficientNetB0, ResNet 50V2 and DenseNet121 respectively. The implemented DWCNNmodel has taken less number of parameters and achieved better performance when compared with other deep learning models with reference to accuracy.

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Plant diseases, Deep learning, point wise convolution, convolutional neural network, depth wise convolution, transfer learning

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Silent spring is an ecological work by Rachel Carson and it describe about the dangers of the Pesticides on the environment

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Birds life, Plant life, Pesticides

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This paper situates itself as an attempt at exploring the rich cultural heritage embedded within the Naga folklores. It is built upon the premise that the Nagas had a strong sense of connection with nature and that this connection permeated almost all aspect of Naga way of life. This paper specifically looks at the interface between Religion and Nature as embodied by the rich folklores. To supplement and support its claim, the paper attempts at a close reading of a poem titled "Shusie Aneipfu" and translated as "My Fair Beloved" which is a retelling of a Tenyimia folklore in the form of a love ballad.

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Religion, Nature, Folklore and Oral Tradition

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The concept of emotional intelligence has been developing since early 20th century with Thorndike introducing the concept of social intelligence. It has gained importance as organisational leaders have learnt that emotional intelligence leads to success and growth of the organisation. This has led to significant research efforts towards defining, understanding, and measuring emotional intelligence. Several models are developed to explain emotional intelligence and there is a need to synthesis these and present the evolutionary path of emotional intelligence. The vital milestones in the development of emotional intelligence concept which have led to the present time emotional intelligence models are chronicled by reviewing the literature.

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emotional intelligence, emotions, evolution, EQ, social intelligence

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This article's main goal is to clarify some of the problems with digital filmmaking and identify the effects of digital technology on the filmmaking process. This study examines the evolution of filmmaking techniques throughout cinematic history and examines the significance of the digital revolution. The study explores how film may thrive in the digital age and considers if the current situation is fundamentally different from the uncertainties surrounding the birth of cinema. To provide a current, correct, and unbiased consideration of the subject, this research utilizes a variety of websites, screenings, and textbooks. It should be emphasized that as the author of this piece, I have a background mostly in audio and video production, having earned a postgraduate degree.

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Filmmaking, Digital Technology, Photography, Production, Pre-Production, Post-Production etc.

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o The purpose of this research is to give a brief overview about career development because many organizations and employees find it challenging to develop an effective career development strategy. This research addresses career and career development definitions, career development components, process and strategies. It also addresses the career management model, finalizing with recommendations for organizations and employees and a conclusion2

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'transitory careers' and 'spiral careers'. Transitory careers

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Lung cancer continues to pose a significant global health threat, with over 1.15 million new cases diagnosed worldwide, making it the leading cause of cancer-related deaths. While smoking remains the most prominent risk factor for lung cancer, it is crucial to recognize that this disease can also affect individuals who have never smoked. This project introduces a comprehensive framework designed to predict lung cancer at an early stage, offering a ray of hope for individuals facing this life-threatening condition. The framework primarily focuses on the realm of computer science, with machine learning as its cornerstone. Leveraging extensive datasets, we meticulously preprocess the data and employ advanced techniques for feature extraction, including Multi-level Discrete Wavelet Transform, Principal Component Analysis (PCA), and Gray-level Co-occurrence Matrix (GLCM). These texture features serve as critical input for our machine learning classification algorithms. Our system utilizes a range of machine learning classifiers, including Support Vector Machines (SVM), Random Forest, and Artificial Neural Networks (ANN). These classifiers are trained on the extracted features, enabling the system to distinguish between different types of lung cancer. Specifically, we address the classification of non-small-cell lung cancer (NSCLC), with further subcategorization into lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD), which together constitute approximately 85% of lung cancer cases. Through rigorous evaluation, encompassing essential parameters such as accuracy, recall, and precision, we assess the performance of each classification algorithm. The results obtained empower us to predict whether a given tumor is benign or malignant, facilitating early intervention and treatment. This innovative framework offers a promising avenue for the early detection of lung cancer, potentially saving countless lives. By harnessing the power of machine learning and data analysis, we aim to enhance the prognosis and management of lung cancer, ultimately contributing to a brighter future for those affected by this devastating disease.

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Lung Cancer, Machine Learning, Multi-level Discrete Wavelet Transform, PCA, GLCM, SVM, Random Forest, ANN, KNN

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Abstract: In last few years most of the applications are using Python for development because it supports multiple programming paradigms, provides a large collection of comprehensive standard libraries and Embedded Librarie. It is a High level programming language, Platform independent, dynamically typed, Portability, procedure oriented, Object oriented Language. We can build python application which help data analysts to analyze large amounts of data for scientific computing and machine Learning applications which enables computers to learn automatically from past data. In this paper, I explored all the built-in libraries to develop different computer science application such as Image Processing, Data Science, Data Visualization, Cloud Computing, Machine Learning, Deep Learning, Natural Language Processing, Speech recognition, etc. And also, different frameworks for Python which can make the front-end work easier are also mentioned

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Python, frameworks, Data Science, Artificial Intelligence (AI), Machine Learning, Deep Learning, Image Processing, Data Science, Data Visualization, Cloud Computing, Machine Learning, Deep Learning, Natural Language Processing, Speech recognition

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Creative Commons Attribution 4.0 and The Open Definition