In the epoch of digital transformation and increasing customer expectations, the traditional support ticketing system faces significant challenges in meeting the demands of sophisticated customers and the competitive marketplace. This research proposes a novel approach to enhance support ticketing systems by integrating AI Chatbots and Business Intelligence (BI) tools. The study aims to investigate the effectiveness of these technologies in automating and optimizing support ticketing processes, thereby reducing the time and cost associated with issue resolution and increasing customer satisfaction. By leveraging AI Chatbots, the research seeks to automate recurring issues, thereby freeing up support staff to focus on more complex tasks. Additionally, integrating BI tools is explored to provide insights into employees\' performance and customer feedback, identify trends, and prioritize issues, leading to improved response times and resolution quality. This research contributes to the literature by proposing an approach for integrating AI Chatbots and Business Intelligence (BI) into existing support ticketing systems, aiming to enhance customer satisfaction and service quality.

Organizations are experiencing technological advancement by implementing
innovative technologies like AI, IoT, Cloud Computing, and ML into e-commerce and ERP
systems. This research will determine how these technologies improve efficiency,
scalability, and customer experience in these domains.
AI helps to establish individual customer relationships and automates accounting
and management procedures; IoT for business presupposes the availability of up-to-date
data on the volumes and condition of products in stock and other organizational and
technical factors related to the most influential work of the enterprise.
Cloud computing is a multi-tenant, multi-tier environment that can be easily
expanded and contracted and typically supports easy collaboration and access. Artificial
neural networks foster value creation with self-service and tailored merchandising.
While the advantages are enormous, data, integrations, and talent issues continue to
plague the industry. More robust cyber defenses, interoperability communication
platforms, and qualified employees must be addressed to mitigate these challenges.
The future of e-commerce and ERP technologies looks bright as the development
and integration processes guarantee growth and development records for these
technologies shortly. Thus, this research indicates the revolutionary power of AI, IoT,
cloud technologies, and ML, which companies can use as a guide for integrating digital
solutions into the value chain to achieve higher performance and competitiveness in the
digital economy.

Enterprise Resource Planning (ERP) systems have become a critical backbone for many organizations, integrating core business processes and data across the enterprise. However, traditional ERP implementation methodologies have often been plagued by lengthy timelines, budget overruns, and user resistance to change. In an increasingly dynamic business environment, organizations, including banks, require more agile and adaptable approaches to ERP implementation to stay competitive and responsive to evolving needs. One promising solution is the integration of Agile methodologies, such as Scrum, into the ERP implementation lifecycle. This paper explores the key advantages of adopting an Agile Scrum methodology for ERP implementation projects in the banking sector. It examines how Scrum\'s iterative sprints, cross-functional teams, and continuous feedback loops can be effectively applied to the various stages of the ERP lifecycle, from initial planning to final deployment and beyond. Furthermore, the paper discusses the necessary organizational and cultural shifts required to successfully integrate Agile practices into a traditionally rigid ERP environment within the banking industry.

The rise of Software as a Service (SaaS) models has introduced transformative paradigms in software engineering, challenging traditional approaches. This paper compares SaaS ERP models, which are business process-driven, with traditional software engineering methodologies, which are requirements-driven. SaaS, being subscription-based, necessitates rapid adoption and continuous updates to enhance reliability and efficiency. Centralized maintenance reduces downtime, while cloud-based solutions handle variable workloads and facilitate remote work. This research also examines the role of change management in project management for adopting new process models under SaaS. Despite security risks and potential vendor lock-in, SaaS enhances innovation, collaboration, user experience, and adaptability. This study explores how SaaS impacts traditional methodologies, aiming to ensure efficiency and competitiveness in the evolving technological landscape.

In the complicated and cross-sectioned type, this study is about the critical digital transformation that obtains results through Enterprise Resource Planning systems in automating the signature process in reporting in the real estate development sector. The guide will start with a brief account of the findings of prior research conducted on the influence of ERP software systems on the oil and gas industry\'s supply chain management and then advance into how ERP-integrated software packages can simplify workflow in signature automation reporting. Additionally, this research adopts a similar qualitative methodology used in the previous studies by administering questionnaires to representatives in the real estate development fund companies\' organizations to evaluate the effectiveness of the signature automation assured by the ERP-driven software.

Background: The Domesticated silkworm, Bombyx mori, an economically important insect has been used as a lepidopteran molecular model next only to Drosophila. Compared to the genomic information in silkworm, the protein-protein interaction data are limited. Currently various high throughput methods are in vogue to identify protein-protein interactions but none of these methods are applied to silkworm. Therefore information on protein-protein interaction network in silkworm is essential for further understanding of the cellular processes. The silkworm protein sequences were obtained from the SilkDB database and the experimentally identified PPI maps from five model organisms such as E.coli, C.elegans, D.melanogaster, H. sapiens, S. cerevisiae were used to infer the PPI network of silkworm using the well-recognized Interlog based method.
Results: Totally 7736 protein-protein interaction pairs were predicted among the 14623 silkworm proteins which include 2700 unique proteins of the silkworms. These were validated using the iPfam interaction domains and the gene expression data. Totally 625 PPI pairs of predicted network were associated with the iPfam domain-domain interactions and the random network has average of 9. In the gene expression method, the average PCC value of the predicted network and random network was 0.29 and 0.231±0.00042 respectively. It reveals that the predicted PPI networks of silkworm are highly significant and reliable.
Conclusions: The predicted silkworm protein-protein interaction data are available at SilkPPI (http://210.212.197.30/SilkPPI/). This is the first PPI network for the silkworm which will provide a framework for deciphering the cellular processes governing key metabolic pathways in the silkworm, Bombyx mori.

The purpose of this study was review the existing literature on coaches’ sources of knowledge in various sports. The review highlighted coaches’ knowledge is built from a broad range of sources. It is influenced by a complex mix of formal, non-formal, informal directed and self-directed learning experiences. However, this developmental mix for coaches is largely individualized and ad hoc. While the literature suggests the balance tends to be toward informal learning, the optimal mix of learning experiences needs to be addressed. In this respect, the researches available on coaching knowledge are limited by a tendency to focus on expert or elite coaching practitioners. This group of coaches has been shown to favor self-directed learning and therefore engage in activities to match.

Iranian people were hopeful to have the activities and systematic economical, social and political relations in constitutional revolution by forming the versed in law/law abiding administration and their participation in administering/managing the society affairs. A look in the achievements of the constitutional revolution in stabilizing the political administration and beginning the rule of Reza Shah indicated that the formation of political power in Iran is the result of fundamental elements and the politico-economical conditions of the society. The worlds’ economical system was not ineffective on the formation trend of political, social and economical currents of Iran. The worlds’ economical system caused the Old Iranian economy could not move towards self-sufficient, inborn, dynamic and stable economy. This should be noticed that formation and continuance of one way management is a multidimensional phenomenon under internal effective factors more than that of resulting from external factors. The results obtained from this article indicate that the political structure of Iran during Ghagar period that was under the influence of social rifts and interferences (including the religious, population and racial inequalities etc) on one hand and the simple tribal structure on the other hand, prevented the formation of foundations/institutions and desired political structure. The political culture that resulted from this political and social structure didn’t provide the opportunity to activate the society in the political activities.

To investigate the effect of substrate on some parameters of ornamental plant experiment Atlas of Iran 2011-2012 in greenhouse located at Km 10 North Road Zahedan - Khash in a factorial randomized complete block design with 5 replications. Factors examined include various levels of zeolite and vermicompost (0, 5 and 10 percent).Iranian petunia seeds in pots and grown to flowering stage was studied in the liter. The results showed that the chlorophyll A content increased with increasing zeolite has a direct relationship. Chlorophyll B in 5% zeolite shows the highest. Effect of zeolite shows the main elements of K and N decreases with increasing zeolite . The amount of phosphorus in the root level and the highest 10 % of the lowest value indicates the level of 5 % . The study showed that nitrogen and phosphorous and 10 % at the highest level and was not significantly different at 0 and 5 % .But at 5% potassium zeolite showed the highest values .Chlorophyll A in the 10 % highest and 5 % lowest level displays . However, chlorophyll B decreases with increasing levels of vermicompost. The amount of N and P in roots increased with increasing levels of vermicompost . , But the rate of increase in K indicates the amount of vermicompost element has an inverse relationship . Vermicompost on shoot N concentration element has an inverse effect .P values of 5 % and a maximum of vermicompost at 5% level represents the lowest. Final results showed that compared with zeolite , vermicompost effect on the economic growth factors and is more affordable .

Data aggregation protocols are required in Wireless Sensor Networks (WSNs) to extend the network lifetime by reducing the energy consumption. The existing DAO-LEACH (Data Aggregation-Optimal LEACH) protocol for WSN is insecure and prone to false data injection. This is enhanced in terms of security and fault-tolerance based on Gracefully Degraded Data Aggregation (GDDA) to ensure the integrity of the aggregated data and Hybrid Layer User Authentication (HLUA) to ensure the confidentiality of the aggregated data. This data aggregation scheme rejects the false data from compromised and malfunctioning sensor nodes (SNs). HLUA consists of a combination of Secret Key Cryptography (SKC) method such as MAC (Message Authentication Code) algorithm and Public Key Cryptography (PKC) method such as Elliptic Curve Cryptography (ECC). MAC algorithm is used between the cluster heads (CHs) and SNs to fulfill lower power demand, while ECC is applied for User Authentication (UA) between CHs and users. The enhanced DAO-LEACH protocol is resistant to security attacks such as, replay attacks, node compromising attacks, and impersonation attacks. It performs better in terms of energy consumption, number of alive nodes, end-to-end delay (EED), and false data detection, compared to SCAR (simple cluster-based data aggregation and routing), ESPA (Energy-efficient Secure Path Algorithm), DKS-LEACH (deterministic key management based LEACH), SEDAN (Secure and Efficient Data Aggregation protocol for WSNs), and DAA (Data Aggregation and Authentication).

Compression of large collections of data can lead to improvements in retrieval times by offsetting the CPU decompression costs with the cost of seeking and retrieving data from disk. In this paper, the author has study the different compression method which can compress the large DNA sequence. In this paper, authors have explored the DNA compression method that is COMRAD, which is used to compare with the dictionary based compression method i.e. LZ77, LZ78, LZW and general purpose compression method RAY. In this, authors have analyzed which one algorithm is better to compress the large collection of the DNA Sequence. Compression table and the line graph show that which compression algorithm has a better compression ratio and the compression size. It also shows that which one has better compression and decompression time.

Technological advancements in the field of World Wide Web help the user to retrieve enormous number of documents as a response to a given query. Many of the research scholars proposed various techniques for retrieving the most relevant documents. All such techniques are applicable to normal web documents, where they are unsuitable for the Geo-spatial documents since it has complex information like direction, location, etc. To handle such information efficiently and to retrieve the spatial information a framework termed Semantic and Feature Aggregated Information Retrieval (SFAIR) has been proposed in this paper. This technique has four components namely (1) Clustering, (2) Indexing, (3) Retrieval, and (4) Ranking. Context-based Query Weighting (CQW) approach clusters the documents that are present in the corpus and indexed using multilevel hashing. On receiving the user query through the user interface, retrieval component uses Feature Probability and Density (FPD) technique retrieves the document that matches the user query. The FPD technique depends upon the features. The Semantic Density (SD) technique ranks the retrieved documents. Experimental result demonstrates the efficiency of the SFAIR technique over the existing technique.