VOLUME 12, ISSUE 9, SEPTEMBER 2023

Abstract: In recent years, advancements in Natural Language Processing (NLP) techniques have revolutionized the field of accessibility and exclusivity of testing, particularly for visually impaired individuals. CBT has shown in years back its relevance in terms of administering exams electronically, making the test process easier, providing quicker and more accurate results, and offering greater flexibility and accessibility for candidates. Yet, its relevance was not felt by the visually impaired students as they cannot access printed documents. Hence, in this paper, we present an NLP-driven Computer-Based Test guide for visually impaired students where the NLP-driven Computer-Based Test guide employs state-of-the-art machine learning algorithms to provide real-time assistance and support to visually impaired students. The system utilizes optical character recognition (OCR) technology to convert the text-based questions and the options and also uses automatic speech recognition (ASR) technology to convert spoken words into text-based in a machine-readable format. Subsequently, the NLP model processes the converted text, enabling the system to comprehend and analyze the content. The system uses a pre-trained model to interpret the spoken answers and provides instant feedback to the students, validating their responses and guiding them through the test. The methodology adopted for this system is Object Oriented Analysis and Design Methodology (OOADM) where Objects are discussed and built by modeling real-world instances. To validate that the system is not perverse, the system is further evaluated to test for accuracy using sample labels (A, B, C, D, E, F, G) to compare with the voice recordings obtained from 20 visually impaired students which is been predicted by the system to attain values for precision, recall, and F1-scores. These metrics are used to assess the performance of the model and have indicated that this model is proficient enough to give its better performance to the evaluated system. Keywords: Natural Language Processing (NLP), Computer-Based Test (CBT), Visual Impairment, Multiple-Choice Question, MCQ, Screen reader. Works Cited: Tubo, Faustinah Nemieboka, Ikechukwu E. Onyenwe, Doris C. Asogwa " DEVELOPMENT OF AN NLP-DRIVEN COMPUTER-BASED TEST GUIDE FOR VISUALLY IMPAIRED STUDENTS", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 1-10, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12901

Abstract: Leaf disease detection is a critical task in agriculture, aiding in the early identification and treatment of plant diseases to ensure optimal crop health. This paper presents a comprehensive approach to automating leaf disease detection using advanced image processing and deep learning techniques in Python. The methodology involves preprocessing the input images to enhance features and extract meaningful information. Subsequently, a Convolutional Neural Network (CNN) model is trained on a curated dataset comprising healthy and diseased plant leaves. The CNN learns to classify leaves into respective disease categories, enabling automated detection. The trained model is evaluated based on various metrics such as accuracy, precision, recall, and F1-score to assess its performance. Additionally, a real-world application of the model is demonstrated through predictions on unseen leaf images. The results showcase the efficacy of the proposed approach in accurately identifying plant leaf diseases, laying the foundation for further advancements and integration into agricultural practices.

Keywords: Python Programming, Leaf Disease Detection.Image Processing, Convolutional Neural Networks (CNN) Works Cited: V.Shankar, Mrs. R.Femila Goldy " LEAF DISEASE DETECTION USING PYTHON ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 11-16, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12902

Abstract: The emergence of Smart Factories plays a pivotal role in the Manufacturing process, necessitating prompt and dependable communication. Edge computing, a novel cloud concept, offers proximity to networks' edges, delivering low-latency, energy-efficient IoT device communication in smart factories. This survey delves into the application of edge computing and the challenges it poses in the realm of smart factories. Its ultimate aim is to uncover gaps in existing research and propose future directions. A systematic review of literature was conducted, with a particular focus on the past five years. The goal was to identify key use cases and advantages of edge computing in Smart factories. The topics covered included a comparison of edge computing and cloud computing in Smart factories, enhancements in IoT performance, and the reduction of latency in Smart factories. The study also analyzed various aspects of quality of service, such as latency, reliability, resource optimization, and processing costs. The findings of this research shed light on the benefits of edge computing in smart factories, including reduced latency, improved security, and enhanced energy efficiency. However, the study also identified challenges such as gaps in standardization and the complexity of architectural design. By examining case studies from different industries, the research provided a diverse range of perspectives. Furthermore, the study addressed the limitations of edge computing and proposed potential solutions, thus contributing to the existing body of knowledge and facilitating future research in this field. This study offers valuable insights into the role and challenges of edge computing in Smart factories. By thoroughly analyzing its advantages and disadvantages, the research provides guidance for its implementation and highlights opportunities for future opportunities . Researchers and practitioners with an interest in edge computing for Smart factories will find this study to be a valuable resource. Keywords: IoT, Edge Computing, Cloud Computing, Fog Computing, Smart Factory, Industry 4.0, Smart Manufacturing Works Cited: Jamilu Ibrahim Argungu , Mustapha Malami Idina , Umar Aliyu Chalawa , Musa Ummar , Sadiq Buhari Bello , Ibrahim Arzika , Baba Ahmad Mala " A Survey of Edge Computing Approaches in Smart Factory ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 17-30, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12903

Abstract: Sensor–equipped smartphones and wearable device are transforming the way of health monitoring. Big data generated by sensor, sensitive application like health monitoring and surveillance system cannot be transferred to and processed by cloud. Moreover, faster processing is required by several internet of things (IOT) application, but current cloud capability will be unable to process such application. The emergence fog computing provide solution by bringing computing resources such as routers much closer to user, also reduce propagation latency for application that require real time response compared to cloud domain. Despite the benefit offered by FC, there are some limitations of fog computing model which focus from a limited perspective on either accuracy of result or response time both not both. Deep learning algorithms, with their ability to process large scale datasets, have recently started gaining tremendous attentions in the fog computing literatures. However, no comprehensive literature review exists on the applications of deep learning approaches to solve complex problems in fog computing and IoT healthcare. To fill this gap, we conducted a comprehensive literature survey on when deep learning meet fog computing and IoT healthcare. The survey shows that when deep learning algorithms meet fog computing architectures in IoT healthcare are increasingly becoming an interesting research area for solving complex problems. We introduce a new taxonomy of deep learning techniques in fog computing and IoT healthcare. The synthesis and analysis of the articles as well as their limitation are presented. A lot of challenges were identified in the literature and new future research directions to solve the identified challenges are presented.

Keywords: Deep learning, fog computing, Internet of Things (IoT), Cloud computing Works Cited: Abdulhafiz Sabo, Habib Shehu Jibrin, Muhammad Zia-Ul-Rahaman Abubakar, Jamilu UsmanWaziri " WHEN DEEP LEARNING MEETS FOG COMPUTING & IoT HEALTHCARE: A REVIEW", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 32-41, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12904

Keywords: Machine learning, Regression, tesla Works Cited: Vinodhini. D, Mr. Manikadan. N " PROJECTING THE PRICE OF STOCKS USING REGRESSION MODEL", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 42-48, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12905

Keywords: Organ Donation, Deceased   Donor, Promotion donor, Transplantable Organs Works Cited: Bharathy G, Mr Dinakar Jose S " Organ And Blood Donation System Using Machine Learning Xgboost", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 49-52, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12906

Abstract: The field of Natural Language Processing (NLP) has gained significant attention in recent years, particularly in the context of fake news detection and categorization. NLP techniques offer powerful tools to analyse and understand textual data, allowing us to identify patterns, sentiments, and linguistic features that can help distinguish between true and false news. In this project, we aim to predict false news and determine their respective categories using NLP techniques. To achieve this, we will employ a combination of supervised machine learning algorithms and NLP methods. Firstly, we will gather a dataset consisting of news articles labelled as either true or false. The dataset will also include information regarding the category or topic of each news article. These categories may range from politics and sports to entertainment and science. Next, we will pre-process the textual data by performing tasks such as tokenization, stop-word removal, and stemming. These steps will help to clean and transform the raw text into a format suitable for analysis.

Keywords: False news prediction, news categorization, NLP techniques, supervised machine learning, textual data, tokenization, stop-word removal, stemming, TF-IDF, word embedding’s, logistic regression, random forests, support vector machines. Works Cited: G. Agasthiya, Mrs. S. Jancy Sickory Daisy " FAKE NEWS DETECTION USING NLP", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 53-60, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12907

Abstract: Crime is a deed that is based on an offensive act, but to overcome such offensive acts it has always been necessary to utilize different means to minimize them in short time. Some of these crimes result in danger to both the environment and human life. Every country in the world seeks peace because it enables societies to flourish, and economies to grow and achieve new heights of success over time. Contrary to this, an unpeaceful environment full of illegal activities brings the downfall of societies, communities, and countries.A mobile application will be developed using react js for notification on weapon detection. Thus, this project helps in effective prediction of detect the weapon at public places in real time applications

Keywords: mobile application, weapon Works Cited: Sanjay Kumar VM, Dr. S. Roselin Mary " UNIQUE WEAPON DETECTION SYSTEM PROMOTING SECURE CITY WITH AUTO ALERT SYSTEM", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 61-63, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12908

Keywords: Block chain, anti-counterfeiting, Mechanism – transparency, traceability, authentication. Works Cited: Divya Bharathi B, Iraniya Pandiyan M, Kumaran M " Blockchain Technology: A Robust Solution for Fake Product Identification in Various Industries", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 64-69, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12909

Keywords: Eye disease detection, Diabetic retinopathy, Cataract, Myopia, Glaucoma, Age-related eye disorders, Hypertension-induced eye disorders, Retinal images, Machine learning, Early detection. Works Cited: Varalakshmi. M, Iraniya Pandiyan. M, Kumaran. M " Deep Learning Advancements in Multispectral Eye Disease Detection: A Comprehensive Review ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 70-74, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12910

Abstract: It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Such problems can be tackled with Data Science and its importance, along with Machine Learning, cannot be overstated. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classification

Keywords: Credit Card, Card-Present Fraud, Fraud Detection, Card-Not-Present Fraud Works Cited:

Vijayakrishnan MC, Eby Chandra, Kumaran M " Credit Card Fraud Detection ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 75-79, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12911

Abstract: The human face is a significant organ for conveying a person's mood. However, making a recommendation playlist based on the current mood by detecting the users face expression can be a work intensive and effective thing. This research is focused on detecting the facial expression using a music application and recommending songs according to that mood. Emojis is also included so that the user can choose emojis to covey their current mood. Emojis may include happy, sad, angry and neutral. Datasets of facial expressions as well as the songs can be  taken for this research. The goal of this study is to identify the users mood by two methods there by giving them a better playlist of music. This study would give more accurate result compared to the previous works.

Keywords: facial expression detection, feature set, data set, music recommendation Works Cited:

Dr. TEGIL J JOHN, AYANA.N, VARSHA.P, ARCHANA.K" Recommendation of Music From Users Mood Using Machine Learning Model ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 80-83, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12912

Abstract: The aim of this review was to explore the current evidence for conversational agents or chatbots in the field of psychiatry and their role in screening, diagnosis and treatment of mental illnesses. Technologies like Artificial intelligence, data science and machine learning are getting upgraded. The advancement in available, portable, low cost handheld device like mobile phones and availability of network connection has resulted in the user’s mobility at an unprecedented level. We evaluate different methodologies like  state phase annotation, smart goal annotation, collection process, agreement results as well as annotation skills for achieving the health goals. The user has to give  their health regarding queries based on that virtual assistant suggest appropriate solution. The facilities like report generation as well as scheduling assignment are provided. It will increase the interaction between humans and machines with the help of different technologies, vast dialogue ,conversational knowledge based, general knowledge based. The system using different algorithms for disease recognition, behavior abnormality detection, prediction etc. Experimental result shows that: Compared with traditional methods, the proposed method is more accurate and faster also User can get service anywhere and anytime.

Keywords: CHATBOT, MENTAL HEALTH Works Cited:

Dr. TEGIL J JOHN, ADI VIGNESH KV, RANIYA JUBIN, AYSHA KV " REVIEW ON: VIRTUAL ASSISTANT IN MENTAL HEALTH ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 84-87, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12913

Abstract: Natural disasters are events that can't be prognosticated both by position and time of circumstance. Natural disasters beget property losses and can indeed take lives. The running of rapid-fire evacuation must be done by the SAR platoon to help victims of natural disasters to reduce the quantum of loss, Bst in reality, there are numerous obstacles in the evacuation process. Starting from facing delicate terrain to necessary outfit limitation. In this exploration, a system designed to descry victims of natural disasters uses image processing where the picture is carried out using a drone that points to help find victims in delicate or vulnerable locales when reached directly by humans. Grounded on this back ground, this exploration proposes the development of a system for the discovery of victims of natural disasters that aims to help the SAR platoon and natural disaster levies in searching for victims who are in hard to reach places. The You Only Look formerly( YOLO) system is enforced using the python programming language related to image processing. From the exploration that has been done, the delicacy result of detecting objects of disaster victims is 89. Works Cited: Dr. Tegil J John, Vyshnavi S Prakash, Adi Vinayak K V, Archana M " OBJECT DETECTION IN DISASTER MANAGEMENT", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 92-95, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12915

Abstract: The project “Deepfake Detection Using Xception and Mobilenets Deep Learning Models” is a web-based application for identifying deepfake media contents i.e., image and video using deep learning technologies. Deepfake can be simply defined as “an image or video of a person in which their face or body has been digitally altered so that they appear to be someone else”. It is a controversial technology with many wide-reaching issues impacting society, e.g., election biasing. The existing system is based on cross-domain fusion, which works on the basis of traditional spatial domain features. This method had utilized the publicly deepfake datasets, and the results show that the method is effective particularly on the Meso-4 Deepfake Database. But this system is only capable of analysing the spatial features, so we propose a system that can process both image and video input and performs both spatial and depth-wise analysis over the input data. The deep learning models Xception and Mobile Net are the two approaches used for classification tasks to detect deepfakes. We utilize training and evaluation datasets from Face Forensics++ comprising four datasets, Face swap, Face2Face, Deepfake, Neural Texture generated using four different and popular deepfake technologies. The input is analysed for both spatial and depth features which is made possible through Xception and Mobile nets that uses depth wise convolutions. It is capable of detecting almost all kind of deepfakes since we train our model with dataset that contains the data obtained from popular deepfake creation. Keywords: Deep learning, Web, Database, Texture. Works Cited: D. Rupasri, M. Kumaran, J. Lin Eby Chandra " Deepfake Detection Using Xception and Mobilenets Deep Learning Mod", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 95-99, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12916

Abstract: Lip reading, the capacity to understand spoken language by visually examining the motions of a speaker's lips, offers enormous potential to improve human-computer interaction and close communication gaps for the hearing-impaired. This project introduces "LipNet," a cutting-edge web application that leverages deep learning technology to enable real-time lip reading and automatic speech recognition. The application's core functionality is built upon a state-of-the-art deep neural network architecture, tailored specifically for lip reading tasks. The network is trained on extensive datasets of labelled video sequences, to ensure robustness and adaptability in diverse scenarios. LipNet offers a user-friendly web interface, allowing users to upload the video. The system rapidly processes the visual data, extracting facial landmarks and lip features with exceptional precision. Through a combination of convolutional and recurrent layers, the deep learning model transforms these visual cues into text representations of the spoken content. LipNet's high-performance architecture ensures reduced latency, making it suitable for real-time lip reading applications, facilitating instantaneous communication for the hearing-impaired. This web application serves as a stepping stone towards a more inclusive and accessible future, where technology fosters seamless understanding and connectivity between individuals, regardless of their auditory abilities.

Keywords: LipNet, Deep Learning, visual data. Works Cited: Ramya H, Sundararajan G, Kumaran M " LipNet: Bridging Communication Gaps through Real-time Lip Reading and Speech Recognition ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 100-104, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12917

Abstract: Most agricultural crops have been badly affected by the effect of global climate change in India. In terms of their output over the past 20 years. It will allow policy makers and farmers to take effective marketing and storage steps to predict crop yields earlier in their harvest. This project will allow farmers to capture the yield of their crops before cultivation in the field of agriculture and thus help them make the necessary decisions. Implementation of such a method with a web-based graphic software that is simple to use and the machine learning algorithm can then be distributed. The results obtained are granted access to the farmer. And yet there are various methods or protocols for such very data analytics in crop yield prediction, and we are able to predict agricultural productivity with guidance of all those algorithms. It utilizes a Random Forest Algorithm. By researching such problems and issues such as weather, temperature, humidity, rainfall, humidity, there are no adequate solutions and inventions to resolve the situation we face. In countries like India, even in the agricultural sector, as there are many types of increasing economic growth. In addition, the processing is useful for forecasting the production of crop yields.

Keywords: predictive modeling, feature selection, data preprocessing, regression analysis, agricultural forecasting, remote sensing data, weather data, soil data, precision agriculture, crop management, decision support system, machine learning models, data – driven farming, yield optimization, agriculture technology, big data in agriculture. Works Cited: Shanmuga Priya M, Lin Eby Chandra M, Kumaran M "CROP YIELD PREDICTION USING RANDOM FOREST ALGORITHM ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 105-108, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12918

Abstract: To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck

Keywords: cloud, network, infrastructure, data security Works Cited: Harish Babu P, Sundar Rajan, Kumaran. M " A DYNAMIC RESOURCE ALLOCATION FOR HIERARCHICAL FEDERATED LEARNING USING DECENTRALIZED EDGE INTELLIGENCE ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 109-113, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12919

Abstract: The educational realm has experienced substantial shifts due to the growing integration of e-learning and the emergence of Competency-Based Curriculum (CBC) as an innovative educational paradigm. This empirical review seeks to investigate the potential harmonization between CBC and e-learning through a thorough analysis of established research. The primary objective of this review is to uncover the synergistic possibilities between these two approaches and to present empirical substantiation of how CBC can strengthen the effectiveness of online learning. Through a comprehensive examination of diverse research studies, this review aims to determine the intrinsic benefits and challenges tied to the fusion of CBC within e-learning contexts. Moreover, it aims to clarify the far-reaching implications for educational institutions and policymakers as they navigate the complex intersection of these progressive methodologies.

Keywords: Competency Based Curriculum, Competency Based Education, E-learning, Online Learning, Digital Learning. Works Cited: Roselida Maroko Ongare " Competency-Based Curriculum as a Catalyst for Enhancing E-Learning: An Empirical Review ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 114-121, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12920

Abstract: Handwritten signature identification and verification has grown to be an active region of research in latest years. Handwritten signature identification systems are used for identifying the person amongst all customers enrolled within the gadget even as handwritten signature verification systems are used for authenticating a consumer via evaluating a specific signature with his signature that is stored in the gadget. This paper affords an evaluation for commonly used methods for pre-processing, function extraction and classification techniques in signature identity and verification structures, similarly to an assessment between the structures implemente in the literature for identification strategies and verification strategies in on line and offline systems with taking into consideration the datasets used and outcomes for every system.

Keywords: Handwritten signature, verified signature Works Cited: Jishnu P, Dr. A. Rengarajan" SIGNATURE VERIFICATION SYSTEM ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 122-125, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12921

Abstract: Machine learning models for COVID-19 detection using chest X-ray images are important because they are fast, accurate, non-invasive, and accessible. They can be used to identify patients who are at high risk of complications, monitor the progression of the disease, and develop new diagnostic and treatment strategies. The ultimate goal of the research is to develop an accurate, reliable, and cost-effective automated diagnostic tool for COVID-19 detection using CXR images, which can help to reduce the spread of the disease and improve patient outcomes. In this study, the researcher invented a new system for COVID-19 detection based on image processing with chest X-ray images. The primary focus of the experiment is to detect various diseases using chest X-ray images.

Keywords: KNN, RF, NN Works Cited: Dr. Santosh Jagtap " The Impact of Machine Learning in COVID-19 Detection and Diagnosis ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 126-131, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12922

Abstract: In this research paper the effect of annealing temperatures on thin films CdS /Cu2S/ATO glass substrate is studied. The room temp. deposited films are annealed at 100°C-400°C and are optically anayzed by UV-Visible spectrophotometer to study transmission spectra and band gap. The estimated values of optical band gap of Cu2S/CdS films are 2.75eV, 2.73eV, 2.70eV, 2.66eV for annealing temperature of 100oC, 200oC,300oC and 400oC respectively.

Keywords: CdS, Cu2S, ATO, UV-Visible spectrophotometer etc. Works Cited: Mahendra Kumar " Effect on optical properties of annealing temperatures on thin films CdS/Cu2S/ ATO glass substrate for solar device", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 132-134, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12923

Abstract: This research delves into the intricacies of implementing Artificial Intelligence (AI)technologies within Management Information Systems (MIS) by County Governments in Kenya. The study aims to investigate the challenges and barriers that organizations encounter during the process of integrating AI technologies within their MIS. By examining the specific impediments that hinder the smooth integration of these advanced technologies, the study aims to provide a comprehensive overview of the factors that influence the adoption landscape. Through quantitative methodologies, the research sought to contribute to a nuanced understanding of the complexities surrounding AI implementation within MIS in the County Governments in Kenya. By shedding light on these obstacles, the research endeavors to provide insights that contribute to a deeper understanding of the complexities associated with technology adoption in the County Governments in Kenya landscape. Keywords: Artificial Intelligence, Management Information Systems, Integration, County Governments Works Cited: Charles Owuor Omoga " Challenges in Implementing Artificial Intelligence within Management Information Systems: Case of County Governments in Kenya", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 135-143, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12924

Abstract: The ubiquity of mobile devices has fundamentally altered the landscape of internet usage, with mobile web browsing now surpassing desktop usage. This paradigm shift presents unique challenges in web performance optimization, particularly for mobile devices with varying hardware capabilities and network conditions. This paper explores advanced techniques for accelerating mobile web performance, focusing on modern websites that demand high interactivity and rich user experiences. Through a comprehensive review of current literature and an analysis of emerging technologies, we propose novel approaches to enhance mobile web performance. Our findings indicate that a combination of server-side optimizations, client-side rendering techniques, and intelligent resource management can significantly improve mobile web performance metrics, leading to enhanced user satisfaction and engagement.

Keywords: Mobile Web, Web Performance, Mobile User Experience, Device Optimization, Modern Websites