JOURNAL OF ALGEBRAIC STATISTICS

A Study Literature of Critical Review in Cloud Computing

Vybhavi. B — Mon, 30 Jan 2023 00:00:00 +0000

IT sectors boom with the knowledge in Cloud Computing. Remote servers stores data and applications. The data/applications are accessed through internet. The cloud computing services are based on distributive basis. The Various Forms of cloud computing are Public cloud, Hybrid cloud and Private cloud. In service model the different categories are Iaas, Paas and Saas[2]. In Saas, the Software application manages the host for Products and services on distributive model[1].

The End Clients are the consumers. Pass-Platform as a service . The underlying architecture has no control over the storage, servers and OS. Iaas[2]-Infrastructure as a Service uses the internet over computing resources.

An Assessment of Credit Management among Cotton Farmers in the North-Eastern Zone of Ghana: The Case of Farmers at the Bunkpurugu Zone and RMG Cotton Ghana Limited

Wed, 08 Mar 2023 00:00:00 +0000

Access to credit facilities remains a critical issue in the agriculture sub-sector of Ghana’s economy. It remains a major challenge that requires the attention of the stakeholders in the sector. In Ghana, there has been significant involvement of companies and corporate organisations in the area of credit access to farmers. In the cotton sector of the agriculture sub-sector for instance, there had been several companies participating in the credit advancement to farmers but could not sustain their operations and ended up folding up. Against this backdrop, the study sought to explore the management of credit among cotton farmers. It among other things examines the compositions of credits advanced to farmers, measures taken to monitor credits, challenges in credit management and the impact of credit on farmers’endeavours. The population targets for the study consist of farmers who are the majority, RMG Cotton staff/management, as well as MOFA district Officer at Bunkpurugu. The simple random sampling method was used to select farmers through, structured interviewswith the MOFA official. Purposive sampling was used on RMG cotton staff/management. Descriptive statistics: percentages and frequencies were used for analysing quantitative data while thematic analysis was employed for the qualitative data. The findings from the study indicate that all inputs are supplied to farmers on credit by the companies and repayment is through seed cotton purchases during the marketing period. It was also found that eventhough there has been training and monitoring, the issue of default was rife among the farmers. It is therefore recommended that stakeholders engage in rigorous training and education of farmers on the use and application of credits in the sector.

Comparative Analysis and Assesment on Different Hate Speech Detection Learning Techniques

Dharmveer Yadav, Mahaveer Kumar Sain, Abraham Amal Raj B — Mon, 30 Jan 2023 00:00:00 +0000

Increased social networking services have modified the way and scale of cyberspace communication. Even so, owing to the anonymity and mobility of these services, the no. of online hate speech is growing. In recent years, several groups, scholars, and social media networks have worked to mitigate the detrimental effect of hate speech on social media. Despite these attempts, social media users continue to be subjected to hate speech. The issue is significantly more visible in social organizations that foster public discourse. Because it is costly and time-consuming to automatically identify hate speech by human annotators, an algorithm is needed for automatic recognition. It has proved effective to transfer knowledge with the finished of a pretrained language model for various downstream tasks in the area of natural language processing (NLP). This paper presents a deep analysis of how hate speech is detected by state-of-art speech detection methods. There are various methods to perform multitask learning in deep learning.The deep multi-task learning system to leverage valuable knowledge from these multiple tasks in numerous benchmark data sets showthe effectiveness of the method which is convincing about the most advanced models. Such DL techniques are used to detect hate speeches from the benchmark datasets. Also, a comparative evaluation has also done using these different types of machine learning techniques that also included deep learning with word embedding methods for hate speech detection. From this comparative analysis and discussion found that machine learning techniques achieved good outcomes but Deep learning techniques beat them. However, deep learning with word embedding techniques has achieved more than 92% performance using Bi-GRU-GloVebut deep learning without word embedding methods outperforms and achieved more than 95% performance efficiency results using LSTM over state-of-art techniques.

Post COVID-19 Pandemic Effects on Customer Behavior Concerning Online Buying in Punjab

Vijay Laxmi, Shobhna Poddar — Mon, 30 Jan 2023 00:00:00 +0000

In this paper, post COVID-19 pandemic effects on consumer behavior concerning online buying in Punjab discussed. Statements were formulated on Likert scale to achieve the objective and a total sample size of five hundred respondents has been collected through a questionnaire. The respondents were engaged in the business activities, professors from teaching faculty from colleges and universities, housewives, students and employees in the private and public sector. Collected data was analyzed with SPSS software. Finally concludes the result related to online buying in Punjab after the COVID -19 Pandemic.

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Analysis of Balking & Probabilistically Modified Reneging in a M/M/1 Waiting Line under Multiple Differentiated Vacations

Mon, 05 Jun 2023 00:00:00 +0000

This paper discusses and investigates transient behavior of a M/M/1 waiting line undergoing multiple differentiated vacations in conjunction with impatient customers- manifested in the form of balking and probabilistically modified reneging. Special function, namely, confluent hypergeometric function, continued fractions, technique of probability generating function and Laplace transform are our mathematical tools to obtain relevant system size probabilities. Time varying mean, variance, throughput and delay time are the performance measures for which we have obtained explicit expressions. Steady-state expressions for system size probabilities and performance measures are also obtained. This paper is a generalized form of a model of Mishra et al. (2022).

Development of Amalgamation Approach to Strengthen Security using Watermarking.

Neha Saini, Nitin Kumar — Mon, 24 Jul 2023 00:00:00 +0000

In the era of Information and Communication Technology, the exchange of information has increased significantly, prompting a greater need for secure communication to ensure data security. The enlargement of the internet has introduced security challenges in watermarking. With the advancements in computer networks, digital data can be easily duplicated, modified, and illicitly distributed. As a result, copyright protection for digital data has become a crucial research focus in information security. Techniques such as encryption, steganography, and watermarking are employed to provide authenticity to multimedia data. Through an extensive review of high-quality research papers, it has been discovered that various methods exist for successful implementation of digital watermarking, tailored to specific application requirements. In our implemented work, the initial phase involves collecting a high-quality dataset from reliable sources. This is followed by rigorous testing against different attacks to ensure the proposed method's robustness. Through comparative analysis, it has been observed that the proposed method yields superior results compared to existing techniques.

Development of Amalgamation Approach to Strengthen Security using Watermarking: A Review

Neha Saini Nitin Kumar — Mon, 24 Jul 2023 00:00:00 +0000

The following paper presents a comprehensive conceptual framework addressing security issues in watermarking. The widespread availability and usage of digital data, such as videos, audios, and images, due to the expansion of the internet, have introduced security challenges. To establish authenticity in multimedia data, techniques like encryption, steganography, and watermarking are employed. Extensive analysis of several high-quality research papers reveals the existence of numerous methods that can successfully implement digital watermarking, tailored to specific application requirements. These methods include DWT-DFT-SVD, LWT-WHT-SVD, DWT-QR, and DWT-FFT-SVD. In our research work, we will proceed in three distinct phases. First, we will prepare a dataset upon which the chosen watermarking technique will be applied. Second, we will evaluate different watermarking techniques to identify the one that yields optimal results in terms of PSNR (Peak Signal-to-Noise Ratio) and processing time. In this phase, we will subject the images to various attacks to ensure the implemented method can withstand such adversarial attempts. Finally, in the third and final phase, we will execute the reverse process to extract the host image and watermark. By following this structured approach, we aim to contribute to the field of security in watermarking and provide insights into efficient and robust techniques for protecting digital content integrity.

Energy Generation through Multiple Renewable Sources for Low Power Gadgets

Khushboo, Rishu Bhatia — Mon, 24 Jul 2023 00:00:00 +0000

As time progresses, the demand for energy continues to rise, leading to the exploration of various energy sources. The global population is growing exponentially, resulting in a substantial energy requirement. Consequently, the world has been shifting towards renewable energy resources, as non-renewable resources are expected to be depleted in the near future. Our research focuses on designing a Simulink model for energy harvesting, utilizing multiple renewable energy sources. This model can be tailored to meet the energy harvesting needs of both large and small grids. Our Simulink model integrates wind energy, solar energy, piezoelectricity, and thermoelectricity as renewable energy resources. With these resources, we can ensure that at least one energy source will be available for harvesting. Furthermore, we anticipate that in the future, a radio frequency energy resource model could be integrated to enhance the system's robustness.

Energy Generation through Multiple Renewable Sources for Low Power Gadgets: A Review

Khushboo, Rishu Bhatia — Mon, 24 Jul 2023 00:00:00 +0000

Energy harvesting has emerged as a promising solution for powering low-power medical devices, offering the potential for sustainable and autonomous operation. This paper explores the application of various energy harvesting techniques, including solar, wind, piezoelectric crystal, and thermal, specifically tailored for powering low-power medical devices. Solar energy harvesting utilizes photovoltaic cells to convert sunlight into electrical energy. It offers a reliable and widely available source of power, especially for wearable and implantable medical devices. Wind energy harvesting harnesses the kinetic energy of air currents to generate electricity. Innovative designs and materials enable the development of compact and efficient wind energy harvesting systems for low-power medical devices. Piezoelectric crystal energy harvesting takes advantage of the piezoelectric effect to generate electrical energy from mechanical vibrations. Thermal energy harvesting utilizes temperature differentials to generate electricity. The integration of these energy harvesting techniques into low-power medical devices presents numerous opportunities, such as reducing the reliance on batteries and enabling long-term, maintenance-free operation. However, challenges related to efficiency, miniaturization, and device integration must be addressed to ensure reliable and sustainable energy harvesting solutions for medical applications. This paper provides an overview of the principles, technologies, and potential applications of solar, wind, piezoelectric crystal, and thermal energy harvesting for low-power medical devices, laying the foundation for future advancements in this field.

Big Data Analysis in Finance Management

Surendranadha Reddy Byrapu — Mon, 30 Jan 2023 00:00:00 +0000

We explore the utilization of machine learning (ML) techniques within the realm of finance research. Initially, we highlight a crucial distinction: supervised and unsupervised learning, the two primary ML categories, tackle distinct challenges compared to the traditional econometric methods. Subsequently, we delve into the contemporary landscape of ML applications in finance, identifying three primary types: (i) the creation of more advanced and innovative metrics, (ii) the minimization of predictive errors, and (iii) the expansion of the conventional econometric toolkit. By organizing these applications into this framework, we provide insight into potential future avenues for both researchers and practitioners. Our findings underscore the numerous advantages of ML methodologies in contrast to traditional approaches, underscoring the immense potential of ML in shaping the future of financial research.

Supply Chain Risk Management

Surendranadha Reddy Byrapu — Mon, 30 Jan 2023 00:00:00 +0000

"Logistics" encompasses the management of the movement of materials and services throughout their supply chains, from their raw state to their final form as finished products. To enhance customer value and gain a competitive advantage, businesses should adopt supply chain management strategies aimed at optimizing their supply-side operations. Manufacturers utilize supply chain management (SCM) to plan and execute cost-effective and efficient supply chains.

A supply chain comprises all the stages of the production process, from product creation to the oversight of supporting IT systems. Typically, SCM aims to consolidate all production and distribution stages within a single framework. By streamlining their supply chains, companies can achieve cost savings and enhance operational efficiency (Baldwin, 2012). Enhancing the precision with which the company monitors production, distribution, and sales is critical to attaining this level of efficiency. Globalization has not only affected contemporary businesses but has also accelerated the pace, intensity, and complexity of corporate processes, as it impacts the inventories of suppliers. Hence, effective supplier management is of paramount importance.

Some of the global market forces include the growth of the global economy, which serves as a primary driver for increasing consumer demand. According to the International Monetary Fund, the GDP of both developed economies such as the United States, Japan, and Europe, and developing economies like Brazil, Russia, India, and China, is expected to continue growing in 2019, albeit at a slower pace than in 2018. When demand rises, the supply must also expand to meet it. The rising cost of labor has, over time, contributed to pricing and supply challenges in recent years (Baldwin, 2012). The high labor costs have repercussions for international trade.

A Review On: Natural Sounding TTS System for Devanagari Language

Mon, 30 Jan 2023 00:00:00 +0000

This paper delves into the development and impact of Natural Sounding Text-to-Speech (TTS) technology for the Devanagari language. It emphasizes the pivotal role of TTS systems in enhancing digital content accessibility, engagement, and inclusivity for Devanagari speakers and readers. The study covers various aspects of TTS, including the unique challenges associated with Devanagari writing, the techniques and models used to create natural-sounding speech, and the wide-ranging applications spanning education, assistive technology, and beyond. Notably, the research underscores the importance of accurately capturing Devanagari's phonetic and prosodic nuances to ensure the highest level of speech quality and naturalness. The technology holds the potential to bridge language and educational gaps, facilitate information dissemination, and improve communication for individuals with limited literacy or visual impairments. It also highlights the need for further research and development to enhance linguistic and cultural adaptability, speech quality, and platform integration. Ultimately, the progress made in Devanagari TTS promises to transform digital content interaction and contribute to educational empowerment, content accessibility, and a more inclusive digital landscape.

Review on Medical Image Segmentation Methods

G. Arunalatha — Mon, 30 Jan 2023 00:00:00 +0000

Medical images, such as Ultrasound, X-Ray, Computed Axial Tomography (CAT) and Magnetic Resonance Imaging (MRI) are often stored in Picture Archiving and Communication Systems (PACS) and linked with other clinical information. Medical image segmentation aims to extract meaningful information such as shape, volume and motion of organs to detect abnormalities from the medical images by processing and analyzing. This paper provides a review on various medical image segmentation techniques. U-Net is a popular deep learning based semantic image segmentation technique. The medical image segmentation methods based on U-Net strategy were discussed.

Context Based Multilingual Spam Detection

Jyotiprakash Rout, Arpita Dharmavat, Ankita Tidake, Aditya Verma, Akash Raj — Mon, 30 Jan 2023 00:00:00 +0000

The proliferation of digital communication platforms has led to an alarming increase in spam messages across different languages, posing a significant threat to user experience and online security.Context-based multilingual spam detection is a promising new approach to spam detection. it takes into account the context of the message, such as the sender, recipient, and surrounding messages. Traditional spam filters often rely on static rule-based or language-specific approaches, which are inadequate in dealing with the evolving tactics employed by spammers. This paper presents a context-based multilingual spam detection framework that leverages advanced natural language processing techniques to effectively identify and filter spam messages in multiple languages.context-based multilingual spam detection is a more accurate and versatile way to detect spam, especially in a globalized world.It is important because 1)It improves the accuracy of spam detection, especially for new and emerging types of spam.2) Reduce the number of false positives. 3)Detect spam messages in multiple languages. 4)Be adapted to new languages and cultures as they emerge. The suggested context-based multilingual spam detection framework furnishes a viable alternative for dealing with the increasingly complex and diverse landscape of spam across different languages. Its adaptive and context-aware method not only improves spam detection effectiveness but also reduces the danger of false positives and negatives, ultimately improving the quality of online communication and user experience.

Degree-Based Topological Indices and M - Polynomial of Famotidine

V. Amudhamalar, G. Priyadharshini — Mon, 30 Jan 2023 00:00:00 +0000

Famotidine, also known as Pepcid, is a drug used to treat acid reflux and heartburn caused by food or drink. In this study, various topological indices of chemical graph theory were calculated to determine the physical and chemical properties of the studied molecules. These indices include the first and second Zagreb indices, the first and second Zagreb eccentricity indices, the first and second location indices, Famotidine Sanskruti indices and other topological indices.

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Retracted — Mon, 30 Jan 2023 00:00:00 +0000

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