International Journal on Recent and Innovation Trends in Computing and Communication

Redefining Citizenship in the Digital Age: Understanding Smart Digital Census for Citizen's Population Data Collection and Analysis.

2024-04-23

A novel idea in population data gathering is the Smart Digital Census, which makes use of cutting-edge tools like mobile computing, cloud computing, and data analytics to improve the precision and effectiveness of conventional census methods. The traditional census method can be expensive, time-consuming, and inaccurate, and on the other end, the Smart digital censuses provide a practical, affordable, and precise alternative. This paper study tries to fully comprehend the smart digital census and how it differs from conventional census techniques. We'll start by outlining what a smart digital census is and why nations ought to implement it. The significant characteristics of the smart digital census are then highlighted, including real-time data collecting, increased accuracy, and lower costs. This article also examines how the Smart Digital Census might affect other fields like corporate intelligence, policymaking, and urban planning. Finally, the comparative advantage of the smart digital census over conventional census methodologies is highlighted in the paper's results, as how it will affect future data collection and analysis. Future research directions in this area are also covered in the paper.

Comparative Analysis of the Student Learning Performance Behavior in An Online and Modular Class in Computer Art and Technological College, Inc. High School Department

Ian Clint E. Offemaria, Lany L. Maceda — 2024-04-23

The shift to distance teaching and learning during the COVID-19 pandemic brought about a challenge for both teachers and students. To face these difficulties in teaching in the High School department at the Computer Arts and Technological College, Inc., a blended learning strategy in the context of teaching and learning the students were employed. An online strategy that facilitated the transition from traditional face-to-face learning to an online setting. The present study identifies the online setting of teaching and learning and evaluates the behavior of the students based on the learning experience and performance. A comparative analysis was employed in conducting the study. The data were gathered using an online survey questionnaire from random respondents and a semi-structured interview was conducted to collect the general weighted average (GWA) of the students. A weighted mean is used to test the behavior of the students learning experience and a T-test using a two-tailed test was used to test and analyze the behavior performance of the high students taking online and modular classes. The results of the study indicated that the majority of the respondents preferred the limited F2F modality for the coming school year. The behavior of the students based on the learning experience taking the online and modular classes has been attained and there is no significant difference in the behavior performance of the high students taking online and modular classes.

Impact of Business Skill Acquisition on Business Performance of Business Education Graduates in Nigeria

2024-04-23

The researchers investigated the impact of business skill acquisition on business performance of business education graduates in Nigeria. Analytical survey design was adopted for the study involving 200 business education graduates with at least, 5 years of cognate practical experience in business using purposive sampling technique. The research questions were answered using percentages and frequencies while the hypotheses were tested using Chi-square statistics. The finding revealed that Business Education Graduates in Nigeria acquire business skills and also, the business skills acquired influence their business performance positively. The findings further revealed that the differences in the expected and observed business skills acquired by the business education graduates and the expected and observed influence of the business skills acquired on their business performance are significant at 0.05 level of significance. Based on the findings of this study, it was concluded that business education graduates in Nigeria acquired relevant business skills such as management skills, business plan and project development skills, problem-solving skills, innovation and creativity skill, accounting and record keeping skills, customer loyalty and retention skills, among others. These skills have significant positive improvement on the skills expected of them. More so, the skills are evidently seen in the business performance of business education graduates as indicated by the results from the analyses and findings. Based on the findings, it was recommended that adequate empowerment for business expansion in area of material resources and legal support should be granted to business education graduates by the Federal, State and Local Government to encourage total diligent and full commitment in business establishment and growth..

Energy Efficient Approach for Multi-level Routing in Wireless Sensor Networks

Seema Chhabra, Rajbir Kaur — 2024-04-23

The 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.

Review Paper on Systematic Study of Leaf Disease Detection Using Accurate and Efficient ML Technique.

2024-04-23

In recent years, plant diseases have posed significant threats to global food security and ecosystem stability. Timely detection and management of these diseases are imperative to mitigate their adverse effects. This paper introduces Foliage Guard, a novel smart plant leaf disease detector leveraging machine learning techniques for accurate and efficient disease identification. Leaves Guard employs state-of-the-art image processing algorithms to analyze leaf images captured using low-cost sensors or smartphones. The system utilizes a deep learning architecture trained on a diverse dataset of plant diseases to classify the health status of leaves accurately. Additionally, Foliage Guard incorporates real-time disease monitoring and alert mechanisms, enabling farmers and gardeners to take pro active measures against outbreaks. Through extensive experimentation and validation of various plant species, Foliage Guard demonstrates superior performance compared to existing approaches, with high accuracy and rapid processing times. The proposed system holds promise for revolutionizing plant disease management practices, offering a cost-effective and accessible solution for early disease detection and prevention in agriculture and horticulture sectors.

Conceptualizing Sustainable Smart Country: Understanding Its Dependency on Smart Security Structure.

Muhammad Younus, Dyah Mutiarin, Achmad Nurmandi — 2024-04-23

This paper explores the concept of a sustainable smart country and its dependence on smart security structures. It aims to understand the relationship between sustainable development and smart security and how the latter can contribute to the former. The paper defines a sustainable smart country and its key features, examines the role of smart security in achieving sustainable development goals, and analyzes the challenges and opportunities associated with implementing a smart security structure in a sustainable smart country. The research methodology involves a comprehensive literature review of relevant academic and policy sources. The findings will contribute to the ongoing debate on the role of smart security in sustainable development and provide insights for policymakers, researchers, and practitioners. It summarizes the study's purpose, basic design, major findings, interpretations, and conclusions. The research methodology is also highlighted, and the study's potential contribution to the ongoing debate on smart security in sustainable development is highlighted.

Advanced IoT Technology and Protocols: Review and Future Perspectives

Mohsen S. Alsaadi, Naif D. Alotaibi, Wasim A. Ali — 2024-04-23

The Internet of Things (IoT) has emerged as a disruptive paradigm, altering how we interact with our surroundings and enabling a plethora of novel applications across multiple sectors. This literature review provides a complete overview of the Internet of Things, including applications, technology, protocols, modeling tools, and future directions. The assessment begins by looking at a wide range of IoT applications, such as smart cities, healthcare, industrial automation, smart homes, and more. It then looks into the underlying technologies that enable IoT deployments, including low-power wireless communication protocols, edge computing, and sensor networks. Protocols and routing methods designed expressly for IoT networks are also described, as well as simulation tools used to simulate and evaluate IoT systems. The discussion focuses on critical insights and consequences for the future of IoT, including challenges and potential in security, interoperability, edge intelligence, and sustainability. By tackling these obstacles and using emerging technologies, IoT can create disruptive change across businesses while also improving quality of life. This review seeks to give scholars, practitioners, and stakeholders a thorough grasp of IoT and its implications for the future.

Efficacy of Reflective Questioning Instructional Strategy on Students’ Achievement in Gas Laws Contents of Secondary Schools Chemistry

2024-04-23

This study investigated the efficacy of reflective questioning instructional strategy on students’ achievement in gas laws contents of secondary schools Chemistry. A pre-test-post-test, control group quasi-experimental research design was adopted in the study. The participants were 129 students (72 females and 57 males) of four intact classes sampled from two government owned secondary schools in Nigeria using multi-stage sampling procedure. Two intact classes each were assigned to experimental and control groups respectively. Data collection was done using a 25-item gas laws achievement test (GLAT). Data collected were analyzed using mean, standard deviation and analysis of covariance (ANCOVA). The result showed that there was a significant difference between the experimental and control groups indicating that reflective questioning instructional strategy enhances students’ achievement in gas laws. Also, there was no significant influence of gender on the mean achievement scores of students in gas laws and there was no significant interaction effect of questioning instructional strategies and gender on students’ achievement in gas laws. Chemistry teachers and pre-service teachers should be trained on how to adopt reflective questioning instructional strategy in the classroom instructions..

Deep Learning Methods for Tooth Detection and Classification in Various Dental Image Datasets: A Taxonomy and Future Directions

2024-04-23

Deep learning approaches have made significant advancements in recent years, generating considerable interest in using them for medical image analysis. In dentistry, the precision of tooth detection and classification serves as the cornerstone of dental practice as it can identify the presence of dental abnormalizes at an early stage. This paper presents an exploration of the potential of deep learning methods for tooth detection and classification across a variety of dental imaging datasets including radiographs, cone-beam computed tomography (CBCT) scans, and photograph images. Convolutional Neural Networks (CNNs) have emerged as one of the most widely used and effective deep learning methods in the field of dental disease diagnosis and medical image analysis. The study aims to conceptualize how these models can effectively learn intricate tooth features, despite having variations in tooth morphology, image quality, and imaging techniques. It highlights the increasing role of deep learning in diagnosing dental diseases and emphasizes the importance of accurate tooth classification for effective treatment planning. The study reviews existing research in deep learning-based tooth classification, discusses challenges including dataset scarcity and model interpretability, and suggests future directions.

A Mobile Application Framework to Classify Philippine Currency Images to Audio Labels Using Deep Learning

2024-04-23

This research presents a mobile application framework designed to empower visually impaired individuals in Legazpi City by providing real-time audio feedback for currency identification. Leveraging deep learning techniques, the proposed framework employs a robust model trained on a comprehensive dataset of Philippine currency images. The deep learning model is capable of accurately classifying various denominations of bills and coins, enabling the development of an inclusive solution for the visually impaired community. The researcher employed a qualitative approach in this study, which included a focus group discussion. Respondents were chosen using purposive sampling. Among those who responded were masseuses, chiropractors, herbal street vendors, and students. Through an online meeting, the selected participants contributed to the focus group discussion. In addition, an in-depth informal interview was conducted to gather additional information for the development of an architectural framework. Based on the result of this study, it was discovered that by implementing this architectural framework, these groups would be able to more easily identify money, increasing efficiency and reducing errors in cash transactions. The use of audio labels is particularly helpful for visually impaired individuals, as it provides an accessible way for them to independently handle and identify money.

Comparing Two Inclusion Techniques in Timed Automata

Hafedh Mahmoud Zayani, Refka Ghodhbani, Taoufik Saidani — 2024-04-23

Verifying the correctness of real-time systems often involves checking language inclusion between timed automata. This problem determines if the language of a system implementation is a subset of the language specified by its design. While the general case is undecidable, recent advancements have proposed techniques for specific scenarios. This paper compares two such techniques: a zone-based semi-algorithm for non-Zeno runs and a time-bounded discretization approach. We analyze their strengths and weaknesses, highlighting cases where each method is advantageous. The comparison highlights the timed bounded discretized language approach's advantages in terms of guaranteed termination and lower memory usage.

Prediction of Heart Disease Using Machine Learning Techniques

Mohammed Jasim A.Alkhafaji, Samah A Nasir, Zinah alhussein — 2024-04-23

A potential strategy in the healthcare industry is the prediction of cardiac disease using machine learning algorithms. Worldwide, heart disease continues to be one of the major causes of death, and successful treatment and prevention depend greatly on early identification. Large volumes of patient data may be analyzed using machine learning algorithms to find patterns and risk factors that might lead to the onset of heart disease. These algorithms use supervised learning, unsupervised learning, and ensemble approaches to assess a variety of data sources, including clinical test results, patient demographics, and medical records. Machine-learning algorithms may be trained on historical data from a variety of patients to discover complicated associations and generate precise predictions about a person's risk of acquiring heart disease. Our objective is to create a machine-learning technique that reliably predicts heart disease and is computationally effective. Feature selection is a crucial step in the creation of prediction models as it permits the identification of the most significant risk factors for heart disease. Machine learning methods including logistic regression, support vector machines, decision trees, random forests, and neural networks are often used to predict cardiac disease. By examining extensive patient data, machine learning algorithms show considerable potential in the prediction of cardiac disease. In the battle against heart disease, their capacity to spot patterns and risk factors may result in early identification, individualized therapies, and better patient care.

Research Output on Strategy Formulation and Implementation: Global Picture, Development and Key Bibliometric Indicators

2024-04-23

Effective strategic management serves as the bedrock for an organization's vision, goal attainment, and stakeholder expectations. Consequently, the research focus on strategy formulation and implementation has garnered substantial attention in recent decades. This study aims to evaluate bibliometric indicators of research productivity related to strategy formulation and implementation through meticulous bibliometric analysis. The analysis leverages the R Bibliometrix library on scientific publications indexed in the Web of Science database. The dataset comprises 672 publications on strategy formulation and implementation, spanning the years 1971 to 2022. Authored by 1,280 contributors from 69 countries, these publications are dispersed across 374 diverse sources, including journals and books. Impressively, this body of work has garnered a cumulative total of 24,635 citations, averaging 36.66 citations per document. The top-ranking article, "The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation" by Robert M. Grant, stands out with 3,649 citations. Examining global scientific production, the United States emerges as the primary contributor with 154 publications (22.91%), followed by China with 56 (8.33%) and the United Kingdom with 54 (8.03%).

The study's findings offer valuable insights for researchers and organizations alike, shedding light on significant research contributions. This comprehensive assessment enables a nuanced understanding of the historical progression and growth within this domain. Additionally, it identifies current focal points of research and highlights areas that warrant attention in future studies.

Fostering Effective Human-AI Collaboration: Bridging the Gap Between User-Centric Design and Ethical Implementation

Vidushi Singh — 2024-04-24

The synergy between humans and artificial intelligence (AI) systems has become pivotal in contemporary technological landscapes. This research paper delves into the multifaceted domain of Human-AI collaboration, aiming to decipher the intricate interplay between user-centric design and ethical implementation. As AI systems continue to permeate various facets of society, the significance of seamless interaction and ethical considerations has emerged as a critical axis for exploration.

This study critically examines the pivotal components of successful Human-AI collaboration, emphasizing the importance of user experience design that prioritizes intuitive interfaces and transparent interactions. Furthermore, ethical implications encompassing privacy, fairness, bias mitigation, and accountability in AI decision-making are thoroughly investigated, emphasizing the imperative need for responsible AI deployment.

The paper presents an analysis of diverse scenarios where Human-AI collaboration manifests, elucidating the impact on various sectors such as education, healthcare, workforce augmentation, and problem-solving domains. Insights into the cognitive augmentation offered by AI systems and the consequential implications on human decision-making processes are also probed, offering a comprehensive understanding of collaborative problem-solving and decision support mechanisms.

Through an integrative approach merging user-centric design philosophies and ethical frameworks, this research advocates for a paradigm shift in AI development. It underscores the necessity of incorporating user feedback, participatory design methodologies, and transparent ethical guidelines into the development life cycle of AI systems. Ultimately, the paper proposes a roadmap towards fostering a symbiotic relationship between humans and AI, fostering trust, reliability, and enhanced performance in collaborative endeavors.

This abstract outline the scope, key areas of investigation, and proposed outcomes of a research paper centered on Human-AI collaboration, providing a glimpse into the depth and breadth of the study.

Optimal Growth Strategy in a Research Organization

Vivek Katiyar — 2024-04-25

For a balanced growth of research organization, an interdependence of size, structure, functions and supporting facilities is an imperative need. This balance is often threatened during the process of growth with the result that the quantity of work suffers. The paper presents a theoretical outline of a model that can be used for the study of growth process in research organizations and thus help research planners and laboratory directors in arriving at decision relating to the development of specialized support facilities for research and integrating these facilities into a single, viable unit through proper structural changes in the organizational set-up.

Brief Analysis of Methods for Detecting Moving Objects Using Computer Vision

Gaurav Sharma — 2024-04-25

In 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.

Utilizing Random Forest for DDoS Attack Detection

Anupama Mishra — 2024-04-25

The “Distributed Denial of Service” (DDoS) attack represents one of the most common forms of cyber assaults. Top of FormThe goal of DDoS is to overwhelm the server machine with an overwhelming number of data packets. This causes the bulk of the network bandwidth and server resources to be used leading to a Distributed denial-of-service problem. In this paper, we employed a random forest classifier for detecting the DDoS attack. This leads to an improvement in accuracy as well as a reduction in the amount of processing overhead required. Utilizing the CICDDOS2019 dataset, our experimental results showcased an impressive accuracy rate of 99.81%.

Latest Trending Techniques and ML Models for Big Data Analytics - A Review

Princy Tyagi — 2024-04-25

As businesses increasingly rely on Big Data for decision-making, understanding cutting-edge methods is crucial. This review synthesizes recent advancements, highlighting the most impactful techniques and ML model algorithms are essential for analyzing and drawing insights from vast amounts of data. This research paper explores the latest techniques and ML algorithms used in big data analytics, including deep learning, neural networks, and decision trees. The challenges of big data analytics are discussed along with how these techniques can help overcome them. Real-world examples of how these techniques have been used in big data analytics are also provided.

Potential Role of ICTs in Social Development

Princy Tyagi — 2024-04-25

The use of internet can be used to make a difference in a few ways, including through click-through donations to charities, education, community service, signing online petitions, and information retrieval. The primary goal of this paper is to investigate the relational repercussions of online platforms. To concentrate on the social ramifications of web-based business and other Information technology-empowered innovations in an efficient way, hardly any regions have been chosen Another term for particular topics of societal importance could be: especially those that are of significant societal relevance? Additionally, the negative effects of the influence of online platforms and IT on societal dynamics have been investigated to support the actual impact of e-commerce on society. Conclusions are offered at the section's conclusion.

Occurrence of Scabies among Children and Effectiveness of an Awareness Programme on Knowledge and Practice among Mothers of Children in selected slums of Doiwala, Dehradun, Uttarakhand

Deeksha Joshi, Harleen Kaur, Shobha Masih, Sanchita Pugazhendi — 2024-04-25

Scabies is a common dermis invasion throughout the world. It is found more in developing countries and spreads very rapidly from one person to another in family members living together, schools and hospitals etc. Scabies is always an active parasite in the human body which is spread by a mite called Sarcoptic. The WHO described scabies as an illness that is affecting people's lifestyles but is a neglected disease. The research was to identify the occurrence rate of childhood scabies and improve the understanding and practices of the mothers of child regarding scabies. An experimental investigation was performed employing one group pre-test & post-test design to assess occurrence of scabies among children and to evaluate the impact of an awareness initiative regarding scabies among mothers of children of slum area of Doiwala. Data were collected by symptoms checklist, knowledge questionnaire and practice checklist. Scabies affected 8% of children aged 1 day to 12 years. Post-test knowledge (20.01±1.9) significantly surpassed pre-test (10.89±2.6) (t=40.21, p<0.05). Post-test practice (16.12±1.6) significantly improved over pre-test (11.07±2.5) (t=26.78, p<0.05). Knowledge and practice correlated moderately positively (0.6).

Fractional Ownership of Shares Concept and Challenges in the Way in Indian Markets

Prayank Sharma — 2024-04-25

The Indian financial market is growing at a very fast pace and same is happening to the stock markets too. Fractional ownership of a share is a new concept Indian financial environment is ready to accept and implement or not. What can be the challenges in implementation of the concept? This article is a view point about the positiveness and its utility of the concept of fractional shares and the amendments requirements in the concerned laws and acts to make it a practically possible.

The Impact of Sales Promotion Schemes on Mobile Phone Services: A Comparative Analysis of Prepaid and Postpaid Mobile Users in Dehradun

Rajat Praveen Dimri — 2024-04-25

India's telecommunications business has significantly grown in the modern era of the information society. India's demand for mobile services has seen a spectacular increase, which has sparked fierce competition among various providers of telecommunications services. These companies provide a variety of promotional plans to encourage clients. In order to make educated decisions in this dynamic sector, it has become essential to comprehend consumer preferences and behavior. This study intends to evaluate consumer attitudes of several Dehradun-based mobile phone service providers, with a particular emphasis on their pricing policies and levels of satisfaction with their services. It also aims to predict how mobile phone services will affect socioeconomic developments in the future. To accomplish these goals, 150 mobile users from the city in Dehradun were randomly chosen as a sample. The results show that respondents preferred prepaid cell services over postpaid ones. The study also shows a significant relationship among respondents' monthly income and educational background and the marketing techniques they selected.

Emoticon Generation, Expression Recognition, and Gender Classification Using Deep Learning in Real-Time

Bineet Kumar Joshi, Bansidhar Joshi — 2024-04-25

Images play an increasingly important role in identifying a person's gender and emotional state in today's digital environment, but there are still methodological hurdles to overcome. Image processing utilizing deep learning algorithms is the way to go. Our study's overarching goal is to find ways to bridge the communication gap through the use of emoticons based on the emotions conveyed in photographs and snapshots. We have utilized the Keras framework to implement a deep learning algorithm called a Convolutional Neural Network (CNN) and evaluated it using Tensor Flow to predict gender. The goal is to create a new dataset of pictures free of noise and then utilize those images as inputs to a convolutional neural network (CNN). The algorithm's result is supposed to be more trustworthy gender identification based on increased accuracy. We have implemented an LSTM-RNN (Long short-term memory recurrent neural network) for emotion identification and facial expression detection. Feature selection is the most crucial step since it will ultimately aid in emoticon generation.

Natural Language Processing in Biomedical Literature for Analysing the Effects of Neurodynamic in Pain and Disability in Carpal Tunnel Syndrome

Aditi Kuliyal, Manish Kumar Jha, Vaibhav Agarwal, Arun Pathak — 2024-04-25

Carpal tunnel syndrome (CTS) a most common peripheral neuropathy characterised by numbness, tingling in the sensory distribution area of the of the median nerve, particularly in the thumb , index finger ,middle finger and radial side of ring finger along with motor weakness, distal to wrist that results into decreased hand grip strength and hand function disability. CTS puts an economy burden on healthcare services as its incidence and prevalence are increasing day by day although a slight decline in numbers has been seen over a period time. Fuzzy logic retains expert information in an intelligent system that may be effectively utilized by others, simulating the cognitive decision-making abilities of the specialist, and helping junior doctors with less expertise make better diagnoses. Therefore, the use of such an expert system is advised to speed up and enhance the accuracy of the diagnosis in patients with suspected CTS by studying different literatures. To device, evidence based therapeutic protocol from biomedical literature for the treatment of pain and disability in CTS. To analyse the effect of openers, sliders, and tensioners on NPRS and disability in carpal tunnel syndrome, using biomedical literature. Therefore, we draw the very encouraging conclusion that further research on the application of such a fuzzy expert system for medical opinion prediction and diagnosis is warranted.

The Synergy of Machine Learning and AI in Cybersecurity: Exploring Issues and Challenges

Anupama Mishra — 2024-04-25

In the continuously evolving domain of cybersecurity, the groundbreaking fusion of ML and AI has inaugurated a new epoch of intelligent systems adept at promptly detecting and countering stealthy cyber threats.Top of Form Nonetheless, harnessing the full potential of these cutting-edge technologies while ensuring their efficacy and dependability demands concerted and sustained effort. The purpose of this comprehensive research is to examine the many multifaceted challenges in the cybersecurity environment and explore innovative solutions inherent to the utilization of machine learning to deal with these challenges.

A Review: Internet of Things with Machine Learning to Develop Intelligent Systems

Vibhor Sharma, Deepak Srivastava, Vinay Avasthi — 2024-04-25

A fresh era of internet-connected sensing gadgets that bridge the gap between the real and virtual worlds has begun, thanks to the rapid advancements in hardware, software, and communication technologies. About twenty-five and fifty billion internet-enabled gadgets are predicted to be in use worldwide. The term "Internet of Things" is a term used to signify a system of electrical devices that communicate with one another. There is a wide variety of infrastructure, retail, transit, and individual healthcare services and applications made possible by the Internet of Things. IoT is a driving force behind the evolution of the Internet and other forms of modern communication technology. Smart computation and evaluation of massive data are crucial to the growth of Internet of Things applications. IoT applications may benefit from data science tools by discovering new patterns and insights in data. Industry applications of data science with the Internet of Things focus on volume, velocity, and pattern identification. With the help of machine learning's predictive analysis, programs can now anticipate both welcome and unwanted occurrences. Thus, machine learning systems not only identify out-of-the-ordinary conduct but also aid in deducing and predicting broader societal tendencies. Continuing modification and monitoring is necessary for efficacy and effectiveness in data analysis. There are two sections to this article: the first discusses the many uses of the Internet of Things where machine learning plays a role in creating an intelligent system, while the second looks ahead to the potential of IoT and machine learning in the advancement of communication devices. Questions such as "What is the classification of artificial intelligence that can be implemented in IoT?" and "How could machine intelligence be implemented in IoT applications?" will be answered in this article.

A Survey on Using Machine Learning to Predict Diabetes Early on

Satyendra Singh Rawat, Amit Kumar Mishra, Deepak Motwani — 2024-04-25

Diabetes is a category of metabolic disease caused by a prolonged high blood sugar level. It is sometimes referred to as a chronic disease. If accurate early prediction is achievable, it can considerably lower the risk factor and severity of diabetes. Combining data mining methods with machine learning, a subsection of artificial intelligence, offers promise in the field of prediction. Data is widely available in the healthcare industry, and in order to improve prognosis, diagnosis, therapy, medication development, and healthcare in general, information must be extracted from it. Based on the World Health Organisation's 2014 report, diabetes is a type of chronic disease with the fastest global growth rates. To illustrate the widely used techniques for early diabetes detection—which are based on cutting-edge technologies including machine learning, cloud computing, etc.—we have reviewed a few significant pieces of literature in this study. The findings suggested that artificial intelligence-based methods are more effective in the early detection of diabetes in patients. Here, we used the Random Forest model to conduct an experiment using a diabetes dataset. First, the dataset is resampled and then used to train and test the Random Forest model. On all performance criteria, the Random Forest attained values above 96%.

Prediction of Machining Conditions Using Machine Learning

Ashutosh Bhatt, Pooja Joshi, Gaurav Aggarwal — 2024-04-25

The new blast of Machine Learning (ML) and Artificial Intelligence (AI) shows extraordinary expectations in the forward leap of additive manufacturing (AM) process displaying, which is an important step toward determining the cycle structure-property relationship. The advancement of standard AI apparatuses in information science was primarily attributed to the extraordinarily huge amount of named informational collections, that may be obtained throughout the trials or first-rate reenactments. To completely take advantage of the force of AI in AM metal while lightening the reliance on "enormous information", everybody set an Improved Neural Network (INN) structure if the wires the two information and first actual standards include the preservation laws of energy, mass, and energies, towards the NN to illuminate the growing experiences. We suggest compressed-type strategies in the Dirichlet limit regulation in light of a Heaviside capability, that may precisely uphold the BCs and speed up the growing experience. The hotel structure was applied to two agent metal assembling issues, that includes the NIST AM-Benchmark series test. The examinations show that the Motel, owing to the extra actual information, may precisely foresee the temperature and also liquefy pool elements throughout the AM processes in metal along a moderate measure of named informational collections.

Exploring Cloud Computing Challenges: A Thorough Examination of Issues in the Cloud Environment

Vinay Avasthi — 2024-04-25

Cloud computing has evolved into a critical component of contemporary enterprises, providing various advantages including scalability, adaptability, and cost efficiency. However, Cloud Computing also introduces a number of security vulnerabilities that can be exploited by cybercriminals. This article provides an overview of the most common cloud vulnerabilities and their impact on cyber security threats. Among various issues, DDoS issue is very serious, so we identify the causes and issues that address these issues.

Exploring Internet of Things and its Applications for Enhanced Living, Industry, and Environment

Vinay Avasthi — 2024-04-25

The Internet of Things has changed many facets of our life. This critical analysis evaluates IoT's varied applications and their effects on life, industry, and the environment. The study shows IoT's dynamic role in modern society, its limitations, and its possibilities. It begins with how IoT innovations are redefining better living. This transition is driven by smart homes, healthcare, and wearable tech, boosting convenience and well-being. We discuss IoT's quality-of-life improvements and future advances. It highlights IoT's tremendous applications to boost production. We have also discussed the issues and challenges faced in IoT. This research work gives insights of the IoT regarding its fundamental, Applications and Issues along with future direction for research.

Yoga and Artificial Intelligence: A Review of The Potential Applications of AI in Yoga Research and Practice for Neurological Disorders

Somlata Jha, Prakhar Singh, Siddhant Rajhans — 2024-04-25

Yoga has become an integral component of many people's lives around the world in recent years. Yoga emphasizes physical, mental, and spiritual links and is a health-promoting exercise method. On the other hand, doing yoga incorrectly and spraining your muscles can lead to pain and other health complications. A broad spectrum of issues impacting the nervous system are designated as neurological disorders. These conditions can be minor, like migraines, or severe, like multiple sclerosis and Parkinson's disease. Due to their complexity, many illnesses can be difficult to diagnose and treat. The use of holistic methods, especially yoga, has demonstrated potential in recent years for symptom relief and enhancing the general health of those with neurological illnesses. In addition, the development of artificial intelligence (AI) has improved our comprehension of these illnesses and offered creative approaches to individualised therapy regimens. The goal of artificial intelligence is to simulate, create, apply, and study the theory, technique, application system, and technology involved in increasing human intelligence. This paper discusses the benefits, drawbacks, and future prospects of applying artificial intelligence to the field of mental illness in an effort to serve as a resource for the field's sustainable development and also gives a succinct synopsis of the utilization of AI for examining yoga's impacts on neurological health and the potential for personalized therapies

Cloud-Native Application Development: Tools, Techniques, And Case Studies

Ashok Choppadandi — 2024-04-25

Building, delivering, and managing existing applications in cloud processing environments is known as the "cloud native" product approach. In order to meet customer demands, modern enterprises must create incredibly flexible, versatile, and adaptive systems that they can update quickly. In order to achieve this, they employ modern tools and processes that inherently facilitate the development of applications on cloud infrastructure. These cloud-native innovations give adopters a creative advantage by enabling rapid and continuous adjustments to applications without compromising service delivery. Organisations that adopt the cloud-native methodology might avoid investing in the acquisition and maintenance of costly physical infrastructure. Long-term reserve money is subsequently put to good use. The cost of money for developing cloud-native systems may also benefit the customers.