International Journal on Recent and Innovation Trends in Computing and Communication
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Energy Efficient Approach for Multi-level Routing in Wireless Sensor Networks
Get PDFThe wireless sensor network is the decentralized kind of network which allows sensor nodes to join or leave the network according to their wish. The implementation of sensor network is done at far places, and they are small sized. Thus, energy consumption becomes the main issue of WSN. The data, whose collection is done from the aimed environment, is transmitted directly to the main station due to the restricted energy of sensor nodes. The sink node receives the data transmitted from various sensor nodes. The decision-making process is deployed by recognizing and eliminating the similarity among the data of diverse sensor nodes. In addition, the sink makes the deployment of obtained data locally as well as transmits these data to the networks which are executed far away. The existing research work employs CTNR, an energy efficient protocol that is capable of enhancing the duration of WSN. The CTNR protocol is consisted of two-level hierarchies for mitigating the energy consumption of wireless sensor network. The CTNR protocol selects the CHs (cluster heads) in the network based on the distance and energy. This research work will focus on enhancing the CTNR routing algorithm so as the life span of network can be prolonged
Customer Sentiment Analysis Based on App Reviews for the Automotive Industry
Get PDFThis paper places a strong emphasis on the importance of customer feedback and sentiments which are available on digital media. OEMs must organize their digital strategy, budget their IT spending, and integrate IT with their corporate goal to improve their brand's perception and win over customers. For this research, the methodology adopted was to do the customer sentiment analysis towards the IT applications that automobile companies are offering by analyzing the customer feedback and reviews in the app market. It will help the companies understand customer sentiment and what customers like and dislike about their offerings. Businesses can use this input to improve their products or services, which will encourage repeat business by building a loyal customer base. it is crucial for automakers to track all of their customer’s touchpoints and enhance their experience at each one by considering customer feedback and implementing new application features and technology
Amotivation as a Predictor of Academic Achievement: A Comparative Study of Science and Art Students' GPA
Get PDFIntrinsic motivation is characterized by engaging in activities for their inherent satisfaction, whereas amotivation is marked by a lack of intention to act, stemming from beliefs of ineffectiveness or disinterest in the activity. Prior research has established a positive correlation between intrinsic motivation and academic performance among science students; however, its applicability to arts students remains unexplored. The present study aims to examine the hypothesis that motivation, in its various forms, correlates with the Grade Point Average (GPA) among both science and arts undergraduates.
A convenience sampling strategy yielded 230 science students (GPA range: 1.8 – 3.89) and 284 arts students (GPA range: 1.5 – 3.84), who participated in a structured questionnaire interview. This instrument assessed intrinsic motivation, amotivation, and study effort using a 5-point Likert scale (ranging from 1 = strongly disagree to 5 = strongly agree). Average scores were computed and contrasted between the bottom and top quintiles of GPA within each discipline.
The findings revealed a universally high level of study effort across participants. Notably, students within the top 20% GPA bracket reported significantly greater academic effort than their lower 20% counterparts. A distinct pattern of significant amotivation was observed among science students with lower GPAs. Conversely, arts students with lower GPAs exhibited high levels of intrinsic motivation, akin to those observed in students with higher GPAs
Brief Analysis of Methods for Detecting Moving Objects Using Computer Vision
Get PDFIn many computer vision applications, moving object detection has drawn notable interest. The scientific community has made numerous contributions to address the significant difficulties of moving object detection in practical settings. The research thoroughly analyzes several moving object recognition methods, which are divided into four groups: methods based on background modeling, Approaches rooted in frame differences, methods based on visual motion estimation, and methodologies based on deep learning. Additionally, thorough explanations of numerous techniques in each category are offered
Adjustment Problems of Non-Resident Students Pursuing Higher Education in Mizoram: A Critical Review of Literature
Get PDFAdjusting to a new environment is critical for non-resident students’ successful engagement with their learning at colleges and university. Identifying factors that causes problems in their adjustment will be of great significance to help improve their overall development. This paper critically reviews previous literature that investigated issues that hinder non-resident students’ adjustment in higher education institutions. The findings indicated that language barrier, social support, length of stay, perceived discrimination or prejudice, establishing relationships, and homesickness were the most significant variables related to the adjustment problems of non-resident students
Blockchain-Enabled Energy Trading Platforms: Reviewing Current Implementations, Challenges, and Future Prospects
Get PDFBlockchain-enabled energy trading platforms have emerged as promising solutions for transforming traditional energy markets by enabling peer-to-peer (P2P) energy transactions and decentralized energy management. This abstract provides an overview of current implementations, challenges, and future prospects of blockchain-enabled energy trading platforms. Blockchain technology, known for its decentralized and immutable ledger system, offers several advantages for energy trading applications. By leveraging blockchain's transparency, security, and trustworthiness, energy trading platforms enable direct transactions between producers and consumers, bypassing intermediaries and reducing transaction costs. Moreover, blockchain facilitates the integration of renewable energy resources, demand response mechanisms, and smart grid technologies, fostering a more resilient and sustainable energy ecosystem. Several blockchain-enabled energy trading platforms have been deployed worldwide, showcasing diverse use cases and operational models. Platforms such as Power Ledger, Grid+, and WePower facilitate P2P energy trading among prosumers (producer-consumers) within microgrids or virtual power plants, empowering individuals and communities to monetize their excess energy generation and optimize their energy consumption patterns. These platforms utilize blockchain-based smart contracts to automate energy transactions, ensure transparent billing, and enable real-time settlement, enhancing efficiency and accountability in energy markets. Despite the potential benefits, blockchain-enabled energy trading platforms face several challenges and limitations. Scalability and throughput constraints of blockchain networks, interoperability issues among different blockchain protocols, and regulatory uncertainties pose significant barriers to widespread adoption. Moreover, the integration of physical energy infrastructure with blockchain technology requires robust cybersecurity measures to protect against cyber threats and ensure the integrity and reliability of energy transactions. Looking ahead, the future prospects of blockchain-enabled energy trading platforms are promising, with opportunities for innovation and growth. Advances in blockchain scalability solutions, such as sharding and layer-2 scaling solutions, hold potential for addressing scalability challenges and enabling large-scale deployment of energy trading platforms. Moreover, the emergence of interoperability protocols and industry standards, coupled with regulatory frameworks conducive to blockchain adoption, can foster greater interoperability and regulatory clarity in energy markets
Implications of Big Data Analytics, AI, ML, and DL in Bangladesh’s Healthcare System
Get PDFThis scoping review explores the intersection of big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL) within the healthcare ecosystem of Bangladesh. The study identifies key applications such as disease outbreak prediction, patient monitoring, and medical image processing, which are increasingly powered by big data technologies. Despite technological advancements, significant barriers remain, including lack of infrastructure, insufficient training, and concerns over data security. The authors examine current initiatives by both public and private healthcare institutions and categorize them based on technological readiness and effectiveness. The review further highlights the need for interoperability standards and regulatory frameworks to ensure ethical use of patient data. Moreover, the paper emphasizes the potential of AI and big data to bridge the urban-rural healthcare divide by enabling remote diagnostics and personalized treatment plans. Through a multi-stakeholder analysis, including policy makers, healthcare providers, and IT vendors, the study proposes a strategic roadmap for nationwide digital transformation. The authors conclude that a well-coordinated approach involving capacity building, data governance, and public-private partnerships is essential to harness the full potential of big data in the healthcare domain of developing nations like Bangladesh
"Leveraging Artificial Intelligence in Health Informatics: Association Rule Mining for Enhanced Medical Insights".
Get PDFThe healthcare sector has witnessed numerous prospects in health informatics because to the significant progress made in artificial intelligence (AI) in recent years. The present work investigates the incorporation of association rule mining (ARM), a crucial machine learning methodology, inside the field of health informatics in order to obtain improved medical insights. The use of Association Rule Mining (ARM) was employed to analyze a comprehensive dataset consisting of patient records, medical histories, and treatment outcomes obtained from three tertiary institutions spanning a period of five years. The primary objective was to uncover concealed patterns and establish correlations among different health metrics. The results of the study demonstrated noteworthy correlations among specific comorbidities, combinations of medications, and the resulting health outcomes. These relationships had not been previously identified by conventional analytical approaches. For example, an unforeseen association was observed between a specific co-occurrence of hypertension, diabetes, and a particular pharmacological category, which was found to be correlated with enhanced rates of patient recovery. These insights possess significant value for healthcare practitioners as they facilitate tailored patient therapy and enhance the quality of medical decision-making through the provision of informed information. Moreover, the integration of ARM with pre-existing electronic health record systems significantly enhances the capacity for obtaining real-time and dynamic insights into the health status of patients. Nevertheless, it is worth noting that there were also notable issues pertaining to data protection, integrity, and standards. Subsequent investigations should prioritize the resolution of these obstacles and the substantiation of the identified correlations in heterogeneous populations. The utilization of artificial intelligence (AI), particularly in the form of the ARM system, within the field of health informatics highlights the significant capacity of contemporary technology to bring about substantial changes in the provision of healthcare services and the well-being of patients
Efficient Design & Analysis of Phase Locked Loop Using High Performance Voltage Control Oscillator with Four Outputs for Communication Standard Applications
Get PDFPhase Locked Loop (PLL) with multiple outputs in low power and high speed is designed with 45nm size IC’s. Proposed PLL is designed using BSIM4 model for n and p channel CMOS transistor which is supported by microwind 3.1 VLSI software. To obtain the layout of proposed PLL, CMOS circuit of each element of proposed PLL is converted into physical layout using lambda based rules of microwind 3.1 software. After cascading the layout of each element, final layout is obtained. This paper particularly focuses on analysis and design of phase-locked loop with low power consumption with high performance using VLSI technology. When compared to conventional design, here focus is made on VCO. The input frequency is collected from external source which is used to generate four frequencies provided for the circuits of communication standard in high-speed Integrated Circuits
Leveraging Artificial Intelligence in Business Using Sap S/4hana Cloud ERP & Sap Joule Enterprise Applications
Get PDFLeveraging modern technology, such as artificial intelligence (AI), has become essential for organizations to stay powerful and competitive in the age of digital transformation. Businesses may access a multitude of benefits across multiple domain names by integrating AI capabilities into employer structures using SAP S/4HANA Cloud ERP and SAP Joule enterprise applications, which are at the vanguard of this integration. Businesses can make data-driven decisions based on real-time insights and predictive analytics, optimize processes through automation and predictive maintenance, enhance customer reports with personalized interaction, drive innovation by identifying new trends and opportunities, and support circular economy initiatives and sustainable practices by utilizing AI in those solutions.
The SAP S/4HANA Cloud ERP and SAP Joule organization programs' architecture frameworks are made to leverage the power of artificial intelligence, the Internet of Things, and advanced analytics. This allows for seamless integration and informed decision-making across a wide range of business aspects. This paper provide a full range of AI-powered capabilities catered to specific business needs, from intelligent manufacturing and consumer engagement to smart method automation and predictive analytics