Volume 9 | Issue 10 |

 Abstract

The rapid advancements in deep learning architectures have transformed various fields, enabling significant improvements in performance and efficiency. This study presents a comparative analysis of several state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Generative Adversarial Networks (GANs). We evaluate their performance based on key metrics such as accuracy, precision, recall, F1 score, and computational efficiency. By conducting experiments on diverse datasets relevant to real-world applications, including healthcare, autonomous vehicles, and natural language processing, we identify the strengths and weaknesses of each architecture. Our findings reveal that while some architectures excel in specific tasks, others offer versatility across multiple domains. The implications of these advancements are discussed, highlighting their potential to drive innovation in various industries. This study aims to provide insights for researchers and practitioners in selecting appropriate deep learning models tailored to their specific applications, thereby advancing the field of artificial intelligence.

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 Keywords

Deep Learning, Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers.

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 Abstract

When automation is driving the competitiveness of your business, organizations need ML to optimize their process. Process automation is vital within enterprise systems such as SAP to decrease manual interference, raise accuracy, and enhance operational flexibility. This paper investigates how to integrate ML into the SAP frameworks to automate core business functions, such as predictive analytics, anomaly detection, and decision processes. This study offers a framework of process automation using contemporary ML algorithms applied to process data, which exploits SAP's inherent capabilities for optimization. The research is to review existing literature on SAP's capacity to automate and implement ML and the challenges in implementation. The paper further details a complete methodology to embed ML models into SAP's enterprise systems and discusses case studies for implementations. Performance metrics and optimization outcomes are referenced via graphs and tables as visuals. Finally, this study concludes with future research directions and the possibility of future advancements in ML-enabled SAP process automation.

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 Keywords

Machine Learning (ML). Process Automation, SAP Frameworks, Predictive Analytics, Optimization

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Gavamayana Samvastara Satrasya Swaroopa Vivaranam

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Gavamayana Samvastara Satrasya Swaroopa Vivaranam

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Salesforce, a leading cloud-based Customer Relationship Management (CRM) platform, offers a suite of tools and applications designed to enhance business operations and decision-making processes. As organizations increasingly rely on data-driven strategies, optimizing Salesforce implementation has become crucial for improving business performance. This paper explores the strategies and methodologies for maximizing the effectiveness of Salesforce to drive better decision-making and overall business outcomes. The study begins by analyzing the core components of Salesforce, including its Sales Cloud, Service Cloud, Marketing Cloud, and Analytics Cloud, to understand their roles in facilitating decision-making. It then delves into best practices for customizing Salesforce solutions to align with organizational goals, focusing on areas such as data integration, automation, and user adoption. Key strategies discussed include tailoring Salesforce dashboards and reports to provide actionable insights, leveraging artificial intelligence (AI) and machine learning for predictive analytics, and implementing process automation to streamline operations. A significant aspect of this research is the exploration of data quality and management within Salesforce. Ensuring accurate, complete, and timely data is essential for reliable decision-making. The paper examines techniques for data cleansing, validation, and governance to maintain data integrity and improve the accuracy of business insights. It also highlights the importance of real-time data processing and how Salesforce's cloud-based infrastructure supports dynamic, on-the-go decision-making. User adoption and engagement are critical for the success of Salesforce implementation. The study reviews strategies for effective change management, including user training, support, and feedback mechanisms. It emphasizes the role of user-centric design in driving higher engagement and satisfaction, thereby maximizing the return on investment in Salesforce. Additionally, the paper addresses the integration of Salesforce with other enterprise systems, such as ERP and marketing automation tools. Seamless integration ensures that data flows smoothly across systems, providing a unified view of business operations and enhancing the accuracy of decision-making. The research explores various integration methods and tools available within the Salesforce ecosystem, including APIs and third-party connectors. Case studies of organizations that have successfully optimized their Salesforce implementations are presented to illustrate real-world applications and outcomes. These case studies provide insights into the practical challenges and solutions encountered during the optimization process, offering valuable lessons for other businesses. In conclusion, the paper underscores the transformative potential of an optimized Salesforce implementation for enhancing decision-making and business performance. By leveraging Salesforce's comprehensive suite of tools and adhering to best practices in customization, data management, and user engagement, organizations can achieve significant improvements in operational efficiency, strategic insight, and overall business success

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 Keywords

Salesforce, CRM, decision-making, business performance, data integration, automation, AI, machine learning, data quality, user adoption, system integration

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Key-value stores are pivotal in modern database architectures due to their simplicity and scalability. However, achieving consistent reads in distributed environments poses significant challenges. This paper explores various optimization techniques designed to ensure consistent reads in key-value stores, focusing on eventual consistency, strong consistency, and hybrid models. We delve into the mechanics of widely-used techniques such as quorum-based approaches, versioning, conflict resolution, and caching strategies. The analysis begins with eventual consistency, which allows read operations to return possibly stale data in favor of higher availability and partition tolerance. This approach's advantage is highlighted through techniques like gossip protocols and anti-entropy mechanisms, which synchronize data over time without immediate consistency. In contrast, strong consistency models prioritize consistency over availability, ensuring that all read operations reflect the most recent write operations. Techniques like Paxos and Raft are examined for their ability to maintain strong consistency through consensus algorithms, albeit at the cost of reduced availability and increased latency. Hybrid models, such as causal consistency, offer a middle ground, allowing reads to be consistent within causally related operations while still offering some level of availability. The paper also explores client-centric consistency models like session consistency, which provide a more tailored approach to consistency by focusing on individual client interactions. Through a series of benchmarks and case studies, this research demonstrates the trade-offs inherent in each optimization technique, providing insights into their applicability based on workload characteristics and system requirements. We also explore emerging trends and future directions in key-value store optimization, such as machine learning-based predictive models for consistency management. Ultimately, this paper aims to equip database architects and engineers with a comprehensive understanding of the techniques available for optimizing consistent reads, empowering them to make informed decisions when designing and implementing key-value stores.

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 Keywords

Key-value stores, consistent reads, eventual consistency, strong consistency, quorum-based approaches, versioning, conflict resolution, caching strategies, Paxos, Raft, causal consistency, client-centric consistency, session consistency, machine learning-based predictive models.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

C?rv?ka is a major materialistic Philosophical school in Indian Philosophy. Their main point is to emphasis on man`s glorification in this universe. Human life is full of hazardous problems as social, economic, natural and many more. The key point of c?rv?ka about man's happiness lies in his own daily days thinking. The problems of social injustice, discrimination of sex, caste are eventually mitigated in their ethical overview. Present life is more important to them and enjoyment in this life is highly appreciated in c?rv?kas ethical theory.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Happiness, material life, perceptual knowledge, pleasure.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Sustainable development has become the buzz word all over the globe from past few decades. Sustainable agriculture is necessary to attain the goal of sustainable development. Green revolution has brought spectacular increase in production and productivity in our country. Food production has increased from 69 million tonnes in 1965 to 275 million tonnes in 2016-17. The increased food grains supply during the last five decades in many parts of the country has ensured national food security. But it has shown the undesirable effects on natural resources, such as soil, water, biodiversity and human health. Farmers are using more fertilizers to get the yield as they were getting with lesser fertilizers twenty to thirty years ago. Consumption of chemical fertilizers {NPK) has been increasing in India during the past fifty three years. It was only 5.43 lakh tones in 1963-64 i.e., before green revolution and reached 259.49 lakh tones in 2016-17. Consumption of pesticides increased from 24.32 thousand tonnes in 1970-71 to 52.75 thousand tones in 2016-17. Injudicious use of fertilizers and exorbitant use of pesticides has led to enormous levels of chemicals buildup in our environment. . It is adversely affecting the health of human being, plants and animals. The present system of agriculture is unsustainable and self destructive. In this context the present study focus on the trends in consumption of fertilizer and pesticides in Indian agriculture and the need for organic farming in India.

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Organic farming, Indian farmers, Agriculture

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The main aim of this study is to assess the cyber security measures used in mobile banking. Nowadays, cyber security in mobile banking is something that needs to be taken into consideration. Banking systems against security threats are activities to protect the expected future value of the information contained in the processing cycle from the threat of danger, both known and unknown potentials, by applying the rules of procedure and protection on IT and banking systems. The impact of activity against this danger is expected to minimize the potential for losses on any threat to protect information, maintain the stability of the process, and reduce the risk of disruption to the activities [1]. Data security is the protection of data from destructive forces and unwanted actions. The concept of "cyber security" originates from "data security," where the information security system aims to create a condition where an organization's information can compete with high confidentiality, integrity, and accessibility. But now, it is closely related to efforts to address the threat to information and IT technology, considering the prospect of an attack against the source of information up to a state of information in transit, because the higher the intensity of information technology, the greater the threat [1]. And the threat is not necessarily a bad thing sourced from domestic and the possibility of information leakage due to an uncontrolled cyber-attack. An attack is an act of intelligence that cannot be underestimated.

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 Keywords

Mobile banking, security measures, fraud, cyber security, systems, computing, software, operating systems, Android, hacking, data breach, authentication, Mobile users

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Creative Commons Attribution 4.0 and The Open Definition

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Because of the British legacy and its influence on contemporary public administration, the civil servants in India lacks Individual Social Responsibility (ISR) in their profession. In this background, this research attempt to study the understanding and practice of ISR by in-service civil servant in Karnataka at their individual and professional level respectively. The nature of the study is descriptive and exploratory. A non-probability sampling technique is adopted. The studies rely on both primary and secondary data. The sample size is 160 consisting of four districts, all level official groups and three types of areas in Karnataka. It used to statistical analysis, such as - percentage analysis and descriptive analysis data interpretation and analysis. The research concludes that, most of the in-service civil servants in Karnataka are aware about the concept of ISR. However, it not well practiced in their profession due many reasons. The study received many valuable suggestions to better practice of ISR by civil servants in Karnataka.

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 Keywords

Individual Social Responsibility, Civil Servant, Public Administration and Karnataka Administration.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The understanding about the literary context is an integral part of the study of any work of literature. The context in which the literature of a period is placed defines the contours of that work. It adds to the knowledge about the undercurrents which advertently or inadvertently influence the shaping up of different genres of literature. Similar is the scenario of literature from Kashmir, it is filled with moral perplexity, uncertainty, nostalgia, illusion and loss. A wave of different writers is competing to jump into the fray and make their voices heard. Including women and men alike, the ongoing conflict in the region has paved the way for questioning of accepted socio-political and traditional beliefs. The insurgency has changed the very dynamics of the region marked by general disillusionment, skepticism, cynicism and irony. Neurosis, displacement, psychological disorder and social gloom become the order of the day. This paper attempts to have a layered discussion of such a book titled, Tell Her Everything, written by Mirza Waheed.

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Disillusionment, Gloom, Neurosis, Nostalgia, Loss, Uncertainty etc

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