Mohd Aarif, Ph.D.
@cagstudycenter.com
Associate Editor
CAG Study Center
Dr. Mohd Aarif attended the prestigious Aligarh Muslim University in Aligarh, UP, India, where he earned both a PhD and an MBA in tourism management. He has been an educator, mentor, researcher, and author for many years. He co-founded the Myra Institute of Skill and Development in Gurugram.
In addition to being a founding member of the CAG Study Center, he also contributes as the Global Research Network's editor-publication. Three books and over twenty-five research publications in journals with citation databases such as Scopus, SCI, ESCI, and UGC-CARE bear his name.
His email address is drmohd03@
EDUCATION
PhD 2018
B.Ed. 2013
MA English 2012
MTA 2010
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Muhammad Asim, Aamir Raza, Muhammad Safdar, Mian Muhammad Ahmed, Amman Khokhar, Mohd Aarif, Mohammed Saleh Al Ansari, Jaffar Sattar, and Ishtiaq Uz Zaman Chowdhury
IGI Global
This chapter explores the connection between sustainable agriculture and the Sustainable Development Goals (SDGs). It discusses various practices like conservation agriculture, organic farming, agroforestry, and precision agriculture, and how they contribute to various SDGs. It focuses on SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 15 (Biodiversity Preservation), and SDG 1 and 8 (Rural Development). The chapter also discusses barriers to widespread adoption, including economic, technological, and sociocultural factors. It uses case studies to illustrate successful models and offers policy recommendations, emphasizing national policies aligning with sustainable agriculture, fostering international cooperation, and investing in education and capacity building. The chapter provides valuable insights for policymakers, researchers, and practitioners in agriculture, sustainability, and development.
Iskandar Muda, Jaymin Arvind Shah, Jarudin, Gioia Arnone, Mohd Aarif, and I. Sravan
AIP Publishing
Raghavendra, Sohail Imran Khan, Vidhya Ranganathan, N. Jayashri, Mohd Aarif, and Elizabeth Chacko
AIP Publishing
Premlatha Soundarraj, Mohd Aarif, S. Gangadharan, Syed Rizwan Naqvi, Noor Kareem AssiHalaf, and Ahmed Salih Mahdi
IEEE
The convergence of smart product packaging and IoT marketing has transformed commerce. This study examines the fundamental ramifications of convergence and its potential to improve customer engagement. Our research shows the transformational potential of these technologies via quantitative and qualitative analyses.Smart packaging outperforms non-smart items, giving firms an advantage, according to quantitative data. Regression and correlation analysis confirm IoT data-customer interaction. Our study also emphasizes ethical data acquisition, which supports data privacy and consumer protection.Consumers may expect personalized experiences, transparency, and real-time feedback from this technology transformation. Smart product packaging and IoT marketing enable readers to make educated decisions and influence product development to meet changing consumer expectations.This research allows academics to study the ideas and models that affect consumer engagement. Data privacy and consumer protection may inform IoT marketing and smart device packaging policy.Our research guides organizations and customers towards better customer interactions, data-driven decision-making, and ethical data practices in this changing age. The future promises revolutionary customer contact.
Lipsa Das, Pooja Anand, Afsana Anjum, Mohd Aarif, Nitu Maurya, and Ajay Rana
IEEE
Smart homes are becoming an increasingly popular trend in the modern world. The rise of the Internet of Things (IoT) has led to the integration of homes with devices and appliances that can be controlled and monitored centrally, typically through a smartphone or tablet. These devices can range from simple gadgets like smart thermostats, lights, and locks to more advanced appliances like smart refrigerators, ovens, and entertainment systems. The potential of smart homes to change the way we live cannot be overstated. The sample papers on this topic reveal the various ways in which smart homes are transforming our daily lives. They examine how smart homes can enhance energy efficiency, promote sustainability, improve health outcomes, enable aging in place, enhance home security, and provide accessibility for individuals with disabilities. Furthermore, the papers discuss the impact of smart homes on social connectedness, leisure time, and the future of work. They examine how smart homes can foster social interaction and community engagement through shared spaces and communication technologies. They also explore how smart homes can be used to create personalized living spaces that cater to individual needs and preferences. While the benefits of smart homes are significant, the papers also highlight the challenges associated with their adoption. Issues such as privacy and security concerns, legal and ethical issues, and user experience design are all areas that require careful consideration. In conclusion, the sample papers on smart homes highlight the potential of this emerging technology to change the way we live, work, and interact with our environment. However, they also emphasize the need for a thoughtful and ethical approach to the development and implementation of smart home technologies. The smart home revolution is still in its infancy, and there is much to be learned about how these devices will shape our lives in the years to come.
Mohammad Salameh Zaid Almahairah, Sushmita Goswami, Purushotam Naidu Karri, I. Mohan Krishna, Mohd. Aarif, and Geetha Manoharan
IEEE
Technological advances and “Supply Chain Risk Management or SCRM” have a clear relationship since disruptions have an impact on SCM. The practical and quantitative participation in technology-enhanced SCRM also reflects the high number of disruptors and wide-ranging effects in this industry. People's inclination to invest in the “Internet of Things (IoT) and big data analytics (BDA)” appears to be to be affected by either the supply or demand of technological advancements. The required sensor and connectivity technologies have already advanced and are now available to the majority of businesses. On the other hand, businesses require more operational data to deal with the erratic and dynamic nature of supply systems. The state-of-the-art IoT breakthroughs in current supply chain risk management research are objectively summarised in this study. The major goal is to locate scholarly literature examples that demonstrate how businesses can use real-time information from operational objects to inform decisions. IoT device adoption and proliferation meet the needs for data to be gathered and analysed on changing business scenarios across the supply chain. However, it is still unclear how businesses may directly incorporate data recorded by the IoT into their decision-making. There are many physical items with only the most basic data able to manage multiple thanks to the resource-intensive nature of today's SCM tasks including shipping, storing, and servicing. Secondary qualitative method is used to collect relevant and factual information related to this research topic.
Jitendra Kumar Chaudhary, Mohd Aarif, N. Raghava Rao, Rajeev Sobti, Suresh Kumar M.V, and Muralidhar L B
IEEE
The incorporation of machine learning tactics into commercial procedures heralds a paradigm-shifting period in which enterprises harness cutting-edge technology to maximise productivity, cultivate flexibility, as well as elevate client experiences. This paper examines the many applications of machine learning, demonstrating its contribution to automation, increased productivity, and customized client experiences. In terms of methodology, to comprehend the complex relationships between business processes and machine learning, an interpretivist approach is used. Using a framework for deductive reasoning, the study uses secondary data sources, such as academic journals and case studies, to confirm as well as enhance preexisting beliefs. The investigation reveals machine learning's analytical strength in identifying recurring patterns and patterns, its critical function in automation to free up human resources for strategic projects, and its forecasting power for resource optimization. Machine learning integrated into business processes provides a flexible toolkit that can be tailored to satisfy the demands of individual organizations. The case studies of Amazon, Google, and Netflix provide empirical evidence that supports the practical application of machine learning in optimizing operations, improving user experiences, and promoting and cultivating consumer loyalty. This report sheds light on how machine learning is influencing a paradigm change regarding the way businesses operate. Organizations that embrace this technology transformation are positioned to be trailblazers, resilient, and innovative, ready to navigate the future.
Lipsa Das, Rahama Salman, Shaista Sabeer, Subuhi Kashif Ansari, Mohd Aarif, and Ajay Rana
IEEE
Maintaining customer retention stands as a pivotal element in ensuring business success, given that the costs associated with acquiring new customers often surpass those linked to retaining existing ones. Recognizing the significance of retaining a loyal customer base, businesses prioritize strategies and initiatives that cultivate lasting relationships with their current clientele, understanding that sustained customer loyalty contributes substantially to long-term viability and profitability.. Machine learning techniques offer a promising approach to predicting customer behavior and developing personalized retention strategies. In this research paper, we explore the use of machine learning for customer retention, specifically focusing on predictive modeling and recommendation systems. We review recent literature on the topic, discussing different machine learning algorithms and techniques that have been applied to customer retention. We also present a case study of a company that has successfully implemented machine learning-based retention strategies, highlighting the benefits and challenges of this approach. Our findings suggest that machine learning can be an effective tool for improving customer retention, but its success depends on several factors, including data quality, model accuracy, and implementation strategy. Our conclusion involves an exploration of the consequences of our research for forthcoming studies and practical applications within business environments..
Kanahaiya Lal Ambashtha, N.S Vijayalakshmi, Mohd Aarif, R. Jeevalatha, Ramu Kuchipudi, and Thokala Sai Krishna Reddy
IEEE
The travel and tourism sector is one of the world’s most important economic drivers. This study analyzes the growth and transformation of the worldwide tourism sector. What keeps the economy going, what generates employment, what contributes to social stability, and what drives societal development. The industry is crucial to the global economy, supporting the livelihoods of hundreds of millions of people. When it comes to economic activity, tourism often serves as the only game in town on many islands. The ultimate goal of the role is to encourage the development of sustainable economies. From the greatest global travel companies to the smallest tour operators or hostel proprietors, the travel and tourism business employs millions of people worldwide. It can achieve real changes in the political and social systems through our work together. Preprocessing, feature selection, and model training are the three main components of the proposed method. Preprocessing is performed to clean the data. The model’s efficacy is measured using LSTM-AE after undergoing a filter-based and recursive feature selection process.
Mano Ashish Tripathi, Ravindra Tripathi, Femmy Effendy, Geetha Manoharan, M John Paul, and Mohd Aarif
IEEE
Machine learning (ML) is an artificial neural network (ANN) that helps developers improve their software’s predictive abilities before they have all the data they need. Because information is so priceless, progress toward fully autonomous agents requires better methods for managing the omnipresent content infrastructures that exist today. All sorts of fields have benefited from advancements in computer vision and AI, from medical diagnosis to data presentation and operations to scientific study, and so on. Learning from polluted or erroneous data may be expensive, much as training for a sport can be dangerous to those who are vulnerable to injury. An organization will incur costs rather than see benefits if its algorithms are improperly taught, as explained in Approaching Data Science. Organizations need to be able to verify the quality and consistency of any large datasets, as well as their sources, to ensure the efficacy of any algorithm.
Jehan Kadhim Shareef Al-Safi, Atul Bansal, Mohd Aarif, Mohammad Salameh Zaid Almahairah, Geetha Manoharan, and Firas Jamil Alotoum
IEEE
Several more individuals consider the internet of things to be the upcoming big idea, however, they are woefully ignorant of its standards as well as advantages. It is believed that by the finish of 2022, anywhere from 50 billion to 60 billion internets things will have been implemented in our rapidly expanding globe. A large number of advantages of the internet of things in history’s rapidly expanding and operating globe. As everyone understands, the internet of things has presently evolved into an essential component of our creation, and it’s experiencing quick advancements in the field. Now that our subject has informed us that innumerable threats have been carried out against the internet of things, we will talk about how the internet of things must be handled or supervised so that an efficient tracking of the information can be carried out, as well as how it must create a secure space or evaluation of the information, and how it ought to defend itself against suspicious threats carried out again on financial collectibles as well as companies.
N. Madhumithaa, G. Manikandan, S. Kalaivany, R. Selvameena, Pramod Kumar Patjoshi, and Mohd Aarif
IEEE
We need the ability to quickly adapt to new supply chain operations in order to keep up with the ever-evolving nature of today’s business climate. Supply chain management activities are one area where software agents have been widely implemented and used with great effectiveness. Agents’ actions are defined by their intended function, and their utility is evaluated in light of their design goals. Managing intricate supply networks now requires the use of decision-support tools. By quickly identifying and fixing bottlenecks and capitalising on market opportunities, logistics diagnostics helps freight transport businesses thrive for the long haul. The management system takes a logistical approach in order to prioritise value. Values and needs of consumers are always changing, and so are the goods and services that meet their needs. used to create those values are the foundation of the logistic method. The research’s ultimate goal is to formulate a systematic means of creating a supply chain management system and performance index. This study employed the research approaches of system analysis, logical-structural procedures, categorization, and grouping. Incorporating big data analytics with the ability to apply autonomous corrective control actions, this article creates a multi-agent-based supply chain management system with an enhanced performance index. The implications of the system on the flexibility of the supply chain are examined.
Rathnakar Gatla, Atul Bansal, Saket Narendra Bansod, Iskandar Muda, Harish Chowdhary, and Mohd Aarif
IEEE
Despite growing enthusiasm, it is still difficult to successfully implement AI in corporate settings. The results of recent polls indicate that as many as 90% of AI projects fail to meet their objectives. There is a dearth of research into the outcomes of successful AI applications, which might serve as a treasure trove of information for businesses just starting out on their AI journey. Accordingly, the purpose or the goal of this study is to gain a better understanding of how artificial intelligence (AI) works, people, and processes may be effectively managed to produce results. Businesses’ productivity has been boosted by the rise of e-commerce as a result of the information technology revolution and the emergence of the digital economy. Artificial intelligence and management information systems are increasingly being used to boost online sales. That technique is now widely used in industrial production and commercial operations. Businesses may save money, increase management efficiency, and increase sales and marketing revenues by using MIS systems and the internet to do business online. This research uses a change management-focused, whole-systems approach to learn more about how management information systems (MIS) and artificial intelligence (AI) can be used to bring in more money.
Melanie Lourens, A. Tamizhselvi, Brijesh Goswami, Joel Alanya-Beltran, Mohd. Aarif, and Durgaprasad Gangodkar
IEEE
The “Internet of Things (IoT)” is an internet protocol for which real-world, virtual as well as digital objects are given recognition, detecting, connectivity, and process technology so they can interact with one another and other Internet-connected devices and services to carry out users’ tasks. There are many IoT solutions available to improve and comfort civilian lives. Additionally, the use of IoT technology in the automotive sector gave rise to the concept of the “Industrial Internet of Things (IIoT),” which has simplified the usage of Cyber Physic Systems, which enable machine and human communication. In general, the variety, heterogeneity, and vast volume of data produced by these businesses make the use of traditional database management systems inappropriate. While constructing IoT data management systems, a number of special issues should be taken into account. These varied guiding notions have led to the proposal of range Of iot data management strategies. The Internet of Things will undoubtedly become a realization as more gadgets are linked to the Internet. Massive amounts of information will be instantiated by items in environment. A high rate and numerous increments will be made to its quantity. This research paper has highlighted database management and its challenges in IoT technology through secondary qualitative analysis.