This project addresses the global health challenge posed by the prevalence of diabetic retinopathy (DR) by developing an efficient automated diagnostic system. The dataset, consisting of diverse high-resolution retinal images, underwent preprocessing to categorize images into No DR (0) and DR (1-4) classes. The First initial binary classification model using a Convolutional Neural Network (CNN) discriminated between healthy and diseased retinas. Subsequently, The second multi-class CNN model was designed to predict the severity of diabetic retinopathy (DR) across a spectrum from mild (1) to proliferative DR (4), enabling a fine-grained analysis for early identification of cases requiring urgent intervention. To address real-world complexities, potential noise in the dataset, including artifacts and exposure variations, was acknowledged. The CNN models were designed to exhibit resilience to these challenges, ensuring robust performance in clinical settings. Preprocessing is considered the common occurrence of image inversion in retinal imaging by incorporating anatomical features, such as macula position and notches, to correctly identify image orientation and enhance result interpretability. The proposed automated analysis system demonstrated promising results in accurately categorizing retinal images into No DR and DR, as well as assigning severity scores for diabetic retinopathy. This project contributes significantly to computer-aided diagnostics, Supplying a dependable instrument for promptly identifying and addressing cases of diabetic retinopathy.
该项目通过开发高效的自动诊断系统，应对糖尿病视网膜病变（DR）的流行给全球健康带来的挑战。数据集由各种高分辨率视网膜图像组成，经过预处理后将图像分为无 DR（0）和 DR（1-4）两类。第一个使用卷积神经网络（CNN）的初始二元分类模型可区分健康视网膜和病变视网膜。随后，第二个多类 CNN 模型被设计用来预测糖尿病视网膜病变（DR）的严重程度，范围从轻度（1）到增殖性 DR（4），从而能够进行精细分析，及早识别需要紧急干预的病例。为了应对现实世界的复杂性，我们考虑到了数据集中可能存在的噪音，包括伪影和曝光变化。CNN 模型的设计能够应对这些挑战，确保在临床环境中发挥强大的性能。预处理被认为是视网膜成像中常见的图像反转现象，它结合了解剖学特征，如黄斑位置和切口，以正确识别图像方向并提高结果的可解释性。拟议的自动分析系统在准确地将视网膜图像分为无 DR 和有 DR 两类以及为糖尿病视网膜病变的严重程度评分方面取得了可喜的成果。该项目为计算机辅助诊断做出了重大贡献，为及时识别和处理糖尿病视网膜病变病例提供了可靠的工具。
{"title":"Diabetic Retinopathy Detection Using InceptionResnet-V2 and Densenet121","authors":"Gangumolu Harsha Vardhan, Meda Venkata Sai Jyoshna, Pamarthi Kasi Viswanath, Shaik Zubayr, Velaga Sravanth","doi":"10.55529/jipirs.42.30.40","DOIUrl":"https://doi.org/10.55529/jipirs.42.30.40","url":null,"abstract":"This project addresses the global health challenge posed by the prevalence of diabetic retinopathy (DR) by developing an efficient automated diagnostic system. The dataset, consisting of diverse high-resolution retinal images, underwent preprocessing to categorize images into No DR (0) and DR (1-4) classes. The First initial binary classification model using a Convolutional Neural Network (CNN) discriminated between healthy and diseased retinas. Subsequently, The second multi-class CNN model was designed to predict the severity of diabetic retinopathy (DR) across a spectrum from mild (1) to proliferative DR (4), enabling a fine-grained analysis for early identification of cases requiring urgent intervention. To address real-world complexities, potential noise in the dataset, including artifacts and exposure variations, was acknowledged. The CNN models were designed to exhibit resilience to these challenges, ensuring robust performance in clinical settings. Preprocessing is considered the common occurrence of image inversion in retinal imaging by incorporating anatomical features, such as macula position and notches, to correctly identify image orientation and enhance result interpretability. The proposed automated analysis system demonstrated promising results in accurately categorizing retinal images into No DR and DR, as well as assigning severity scores for diabetic retinopathy. This project contributes significantly to computer-aided diagnostics, Supplying a dependable instrument for promptly identifying and addressing cases of diabetic retinopathy.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"7 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140434937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The comprehensive management of various health conditions within the community is heavily reliant on the crucial role of medications. Objective: The primary objective of this research is to investigate medication usage patterns, adherence, and associated factors among a diverse participant pool. The study aims to assess the prevalence of prescription medication use, consumption patterns, adherence rates, and the methods employed by participants for managing their medicines. Furthermore, the study explores participants' experiences with side effects and evaluates their satisfaction with prescribed treatments. Methods: A prospective cross-sectional design was employed for data collection, utilizing a self-administered Medication Usage Survey distributed through Google Forms. Participants were recruited through various channels, and data were collected anonymously. Results: A total of 103 participants contributed to the study, with a diverse demographic composition. The majority identified as female (60.19%), and participants spanned various age groups, reflecting a comprehensive representation. Geographically, the study included participants from multiple locations, with Bengaluru being the predominant location (80.58%). Participants reported diverse health conditions, with 69 individuals (66.99%) on prescription medications. Consumption patterns revealed that 57.3% took medications daily, while adherence varied, with 36.9% reporting missed doses. Side effects were reported by a small percentage (12.66%) of participants, and various methods were employed for managing medicines. Overall, treatment satisfaction varied among participants. Conclusion: This research provides valuable insights into medication usage patterns and associated factors among a diverse participant pool.
{"title":"The Pillars of Safety: Unveiling the Impact of Medication Usage on Public and Patient Wellbeing","authors":"Zaid Khan, Ramya Cv, M. Rekha","doi":"10.55529/jcpp.42.1.15","DOIUrl":"https://doi.org/10.55529/jcpp.42.1.15","url":null,"abstract":"Background: The comprehensive management of various health conditions within the community is heavily reliant on the crucial role of medications.\u0000\u0000Objective: The primary objective of this research is to investigate medication usage patterns, adherence, and associated factors among a diverse participant pool. The study aims to assess the prevalence of prescription medication use, consumption patterns, adherence rates, and the methods employed by participants for managing their medicines. Furthermore, the study explores participants' experiences with side effects and evaluates their satisfaction with prescribed treatments.\u0000\u0000Methods: A prospective cross-sectional design was employed for data collection, utilizing a self-administered Medication Usage Survey distributed through Google Forms. Participants were recruited through various channels, and data were collected anonymously.\u0000\u0000Results: A total of 103 participants contributed to the study, with a diverse demographic composition. The majority identified as female (60.19%), and participants spanned various age groups, reflecting a comprehensive representation. Geographically, the study included participants from multiple locations, with Bengaluru being the predominant location (80.58%). Participants reported diverse health conditions, with 69 individuals (66.99%) on prescription medications. Consumption patterns revealed that 57.3% took medications daily, while adherence varied, with 36.9% reporting missed doses. Side effects were reported by a small percentage (12.66%) of participants, and various methods were employed for managing medicines. Overall, treatment satisfaction varied among participants.\u0000\u0000Conclusion: This research provides valuable insights into medication usage patterns and associated factors among a diverse participant pool.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"106 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140438072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. M. Sium, Afrin Sharabony, Dr. Kazi Md. Fazlul Haq
This study investigates the escalating issue of urban air pollution in Dhaka and its surrounding areas, focusing on the post-monsoon period. Utilizing Aeroqual Series 500 air quality monitors, this research measured concentrations of NO2, SO2, CO2, CH4, PM2.5, and PM10 at 24 strategically selected sites in Dhaka, Narayanganj, and Gazipur. The findings reveal elevated levels of NO2 across multiple regions, notably exceeding the standard threshold of 0.053 ppm, with Gulistan, Mirpur10, Gabtuli Darus-salam, Farmgate, and Savar exhibiting the highest concentrations. Additionally, Gulistan displayed a significant peak in SO2 levels at 0.3 ppm. Areas adjacent to the Buriganga River, specifically Lalbagh and Kadamtuli, were identified as heavily polluted as they have been characterized by strong odour and poor air quality. High concentrations of CH4 and CO2 were detected in the New Market, Zinda Park, and Jirani Bazar, surpassing established safe levels. The study highlights Dhaka's alarming average Air Quality Index (AQI) of 186.8, with a peak of 395 at Joydebpur Rail Station and a low of 110 at Panam City. This research underscores the critical need for enhanced air quality monitoring and control strategies in Dhaka, highlighting the severe health risks posed by industrial and vehicular emissions in rapidly urbanizing regions.
{"title":"Comparative Evaluation of Post-Monsoon Crossroads Air Quality Variations in Major Cities of the Greater Dhaka Region","authors":"S. M. Sium, Afrin Sharabony, Dr. Kazi Md. Fazlul Haq","doi":"10.55529/jeimp.42.1.18","DOIUrl":"https://doi.org/10.55529/jeimp.42.1.18","url":null,"abstract":"This study investigates the escalating issue of urban air pollution in Dhaka and its surrounding areas, focusing on the post-monsoon period. Utilizing Aeroqual Series 500 air quality monitors, this research measured concentrations of NO2, SO2, CO2, CH4, PM2.5, and PM10 at 24 strategically selected sites in Dhaka, Narayanganj, and Gazipur. The findings reveal elevated levels of NO2 across multiple regions, notably exceeding the standard threshold of 0.053 ppm, with Gulistan, Mirpur10, Gabtuli Darus-salam, Farmgate, and Savar exhibiting the highest concentrations. Additionally, Gulistan displayed a significant peak in SO2 levels at 0.3 ppm. Areas adjacent to the Buriganga River, specifically Lalbagh and Kadamtuli, were identified as heavily polluted as they have been characterized by strong odour and poor air quality. High concentrations of CH4 and CO2 were detected in the New Market, Zinda Park, and Jirani Bazar, surpassing established safe levels. The study highlights Dhaka's alarming average Air Quality Index (AQI) of 186.8, with a peak of 395 at Joydebpur Rail Station and a low of 110 at Panam City. This research underscores the critical need for enhanced air quality monitoring and control strategies in Dhaka, highlighting the severe health risks posed by industrial and vehicular emissions in rapidly urbanizing regions.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"418 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-20DOI: 10.55529/jecnam.42.19.32
Susmita Timilsina, Govinda Jnawali
The study focuses on the Generation’s Z student’s preference factor for purchase decision of two wheelers in Butwal sub -metropolitan studying in community and public colleges This paper focuses on the behavioral intentions of the z generations students for acceptance of new technological products, i.e (two-wheeler) and the factors considered to be vital for the purchase of two wheeler. The primary sample of 395 structure questionnaires was collected from Z youth (18-25). The Descriptive statistics and chi squared test through IBM SPSS 25 is adopted to find the empirical fit with the hypothesis framed. The chi square analysis was done to examine association between demographic variables and purchase decisions of two wheelers. The results of chi square analysis indicated that buyer’s marital status, occupation, religion, mode of payment, purpose of two wheeler purchase, number of family members and annual family income are significantly associated with purchase decision. The various categories of demographic characteristics analyzed in the study influence buyer two wheeler brand purchase decision. The results for the marketers of twowheeler focusing on the z generation. The finding suggest the manufacturer’s credibility, reliability, price of vehicle, band image, mileage, cost of maintenance, resale value and the facility conditions influences the purchase decision of the buying the two wheelers.
本研究重点关注在布特瓦勒（Butwal）次大都市的社区和公立学院就读的 Z 世代学生在决定购买两轮车时的偏好因素 本论文重点关注 Z 世代学生接受新技术产品（即两轮车）的行为意向，以及购买两轮车的关键因素。本文从 Z 世代青年（18-25 岁）中收集了 395 份结构问卷作为主要样本。通过 IBM SPSS 25 进行了描述性统计和卡方检验，以发现经验与假设的契合度。对人口统计学变量与两轮车购买决策之间的关系进行了卡方分析。卡方分析结果表明，购买者的婚姻状况、职业、宗教信仰、付款方式、购买两轮车的目的、家庭成员数量和家庭年收入与购买决策有显著关联。本研究分析的各类人口特征都会影响购买者的两轮车品牌购买决策。研究结果适用于以 Z 世代为重点的两轮车营销人员。研究结果表明，制造商的信誉、可靠性、车辆价格、品牌形象、里程数、维护成本、转售价值和设施条件会影响两轮车购买者的购买决策。
{"title":"Factors Affecting Two-Wheeler Purchase Decision among College Students","authors":"Susmita Timilsina, Govinda Jnawali","doi":"10.55529/jecnam.42.19.32","DOIUrl":"https://doi.org/10.55529/jecnam.42.19.32","url":null,"abstract":"The study focuses on the Generation’s Z student’s preference factor for purchase decision of two wheelers in Butwal sub -metropolitan studying in community and public colleges This paper focuses on the behavioral intentions of the z generations students for acceptance of new technological products, i.e (two-wheeler) and the factors considered to be vital for the purchase of two wheeler. The primary sample of 395 structure questionnaires was collected from Z youth (18-25). The Descriptive statistics and chi squared test through IBM SPSS 25 is adopted to find the empirical fit with the hypothesis framed. The chi square analysis was done to examine association between demographic variables and purchase decisions of two wheelers. The results of chi square analysis indicated that buyer’s marital status, occupation, religion, mode of payment, purpose of two wheeler purchase, number of family members and annual family income are significantly associated with purchase decision. The various categories of demographic characteristics analyzed in the study influence buyer two wheeler brand purchase decision. The results for the marketers of twowheeler focusing on the z generation. The finding suggest the manufacturer’s credibility, reliability, price of vehicle, band image, mileage, cost of maintenance, resale value and the facility conditions influences the purchase decision of the buying the two wheelers.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"118 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-17DOI: 10.55529/jipirs.42.11.29
Suham A. Albderi
The idea of 5G innovations is a prevalent instrument for the pace of transmission and gathering of data and the accessibility of permitting all over the place. Notwithstanding that the fifth era convergences will embrace a keen procedure for the data transmission process. Sending and getting signals work in high coordination in 5G networks, since this innovation arranges flexible, geostationary earthbound correspondence with other medium and little circuit correspondences with short steering in straight correspondences, and the correspondence incorporates signal processing as well as way finding. In this study the responsiveness improvement of the correspondence range will be tested by applying blended deep learning methods, in which the data cross-over will be diminished with the upgraded smart control. Utilizing blended deep learning methods, this study exhibits the huge difficulties presented by 5G transmissions in keenly detecting the LTE signal range and different data in 5G remote sensor networks. Way obstructions are recognized as the essential hindrance. The states of the correspondence framework ought to be considered while plotting the network and sensors for the fifth era.
{"title":"Deep Learning Strategies for 5G and LTE Spectrum \u0000Sensing Communication","authors":"Suham A. Albderi","doi":"10.55529/jipirs.42.11.29","DOIUrl":"https://doi.org/10.55529/jipirs.42.11.29","url":null,"abstract":"The idea of 5G innovations is a prevalent instrument for the pace of transmission and gathering of data and the accessibility of permitting all over the place. Notwithstanding that the fifth era convergences will embrace a keen procedure for the data transmission process. Sending and getting signals work in high coordination in 5G networks, since this innovation arranges flexible, geostationary earthbound correspondence with other medium and little circuit correspondences with short steering in straight correspondences, and the correspondence incorporates signal processing as well as way finding. In this study the responsiveness improvement of the correspondence range will be tested by applying blended deep learning methods, in which the data cross-over will be diminished with the upgraded smart control. Utilizing blended deep learning methods, this study exhibits the huge difficulties presented by 5G transmissions in keenly detecting the LTE signal range and different data in 5G remote sensor networks. Way obstructions are recognized as the essential hindrance. The states of the correspondence framework ought to be considered while plotting the network and sensors for the fifth era.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"87 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online Food Ordering App is an application designed primarily for use in the food delivery industry. This system will allow hotels, restaurants and food courts to increase the scope of business by reducing the labour cost involved. The system also allows to quickly and easily manage an online menu which customers can browse and use to place orders with just few clicks. Admin employees then use these orders through an easy to navigate graphical interface for efficient processing. The online food ordering system provides convenience for the customers. This system increases the takeaway of foods than visitors. The online food ordering app set up menu online and the customers easily places the order with a simple click. Also, with a food menu online you can easily track the orders, maintain customer s database and improve your food delivery service. The proposed system leverages the latest in mobile technology to bridge the gap between restaurants, food enthusiasts, and delivery services. It offers a user-friendly interface that allows users to browse a wide range of cuisine options, explore restaurant menus, customize orders, and place them seamlessly. The system is powered by trending Artificial Intelligence Technologies like Machine Learning, Recommendation Engine, Route optimization for delivery and Image recognition for meal searching. This system will be a culinary companion that will connect users to their favourite food experiences without leaving the comfort of their homes or offices. Its dynamic features make it an indispensable tool for food enthusiasts and a valuable asset for the restaurant industry, creating a win-win situation for all stakeholders involved in the modern gastronomy ecosystem.
{"title":"Online Food Ordering System","authors":"Ulemu Mponela, Dr. Kadar Shereef, Dr. Tawarish","doi":"10.55529/ijitc.42.43.52","DOIUrl":"https://doi.org/10.55529/ijitc.42.43.52","url":null,"abstract":"Online Food Ordering App is an application designed primarily for use in the food delivery industry. This system will allow hotels, restaurants and food courts to increase the scope of business by reducing the labour cost involved. The system also allows to quickly and easily manage an online menu which customers can browse and use to place orders with just few clicks. Admin employees then use these orders through an easy to navigate graphical interface for efficient processing. The online food ordering system provides convenience for the customers. This system increases the takeaway of foods than visitors. The online food ordering app set up menu online and the customers easily places the order with a simple click. Also, with a food menu online you can easily track the orders, maintain customer s database and improve your food delivery service. The proposed system leverages the latest in mobile technology to bridge the gap between restaurants, food enthusiasts, and delivery services. It offers a user-friendly interface that allows users to browse a wide range of cuisine options, explore restaurant menus, customize orders, and place them seamlessly. The system is powered by trending Artificial Intelligence Technologies like Machine Learning, Recommendation Engine, Route optimization for delivery and Image recognition for meal searching. This system will be a culinary companion that will connect users to their favourite food experiences without leaving the comfort of their homes or offices. Its dynamic features make it an indispensable tool for food enthusiasts and a valuable asset for the restaurant industry, creating a win-win situation for all stakeholders involved in the modern gastronomy ecosystem.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"63 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud computing is changing game all around the industry. It’s delivering computing resources, such as storage, processing power without investing in and managing physical infrastructure, offering a more flexible and cost-effective approach to meet computational demands. Serverless computing is a cloud computing execution model where cloud providers automatically manage the infrastructure needed to run applications and scale. This paper examines use of AWS Lambda in Data processing, the leading serverless computing service of Amazon Web Services (AWS). Our study will explore the architecture, integration of AWS Lambda with other AWS services, real-world use cases, proposing best practices for Data processing. This research will provide a complete understanding of AWS Lambda and findings here are a valuable resource for people looking to unleash the benefits of AWS Lambda in a scalable and high-performance cloud-native application architecture.
{"title":"Exploring Usage of AWS Lambda in Data Processing","authors":"Shubhodip Sasmal","doi":"10.55529/ijitc.42.35.42","DOIUrl":"https://doi.org/10.55529/ijitc.42.35.42","url":null,"abstract":"Cloud computing is changing game all around the industry. It’s delivering computing resources, such as storage, processing power without investing in and managing physical infrastructure, offering a more flexible and cost-effective approach to meet computational demands. Serverless computing is a cloud computing execution model where cloud providers automatically manage the infrastructure needed to run applications and scale. This paper examines use of AWS Lambda in Data processing, the leading serverless computing service of Amazon Web Services (AWS). Our study will explore the architecture, integration of AWS Lambda with other AWS services, real-world use cases, proposing best practices for Data processing. This research will provide a complete understanding of AWS Lambda and findings here are a valuable resource for people looking to unleash the benefits of AWS Lambda in a scalable and high-performance cloud-native application architecture.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"111 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yash Khopkar, Avantika Deshmukh, Prof. Gufran Ansari
In today's digital age, mobile apps have transformed our daily lives, but for people with visual impairments, these apps often present accessibility challenges. This research addresses the need for improved solutions by focusing on "voice-guided assistance" for Android smartphones. The existing options for visually impaired users are fragmented, and this study aims to create an integrated mobile application, "Voice Companion," designed to enhance their digital experience. "Voice-Companion" is a specialized Android application designed for visually impaired individuals, developed in Java within the Android Studio environment. It leverages the Android OS to provide comprehensive non-visual access. With features like voice commands, object detection, messaging, a voice-activated calculator, location and time announcements, text-to-speech capabilities, and battery status updates, the user-friendly app bridges accessibility gaps, empowering visually impaired users for equal participation in the digital realm. Its modular, user-centric design emphasizes accessibility through seamless integration of voice commands and gesture recognition, facilitating efficient interactions with mobile devices. This research aims to create "Voice-Companion," enhancing smartphone accessibility through seamless voice command integration and gesture recognition. Anticipated results include improved digital engagement and quality of life for users with visual impairments.
{"title":"Voice-guided Mobile Assistance for the Visually Impaired","authors":"Yash Khopkar, Avantika Deshmukh, Prof. Gufran Ansari","doi":"10.55529/ijitc.42.6.17","DOIUrl":"https://doi.org/10.55529/ijitc.42.6.17","url":null,"abstract":"In today's digital age, mobile apps have transformed our daily lives, but for people with visual impairments, these apps often present accessibility challenges. This research addresses the need for improved solutions by focusing on \"voice-guided assistance\" for Android smartphones. The existing options for visually impaired users are fragmented, and this study aims to create an integrated mobile application, \"Voice Companion,\" designed to enhance their digital experience. \"Voice-Companion\" is a specialized Android application designed for visually impaired individuals, developed in Java within the Android Studio environment. It leverages the Android OS to provide comprehensive non-visual access. With features like voice commands, object detection, messaging, a voice-activated calculator, location and time announcements, text-to-speech capabilities, and battery status updates, the user-friendly app bridges accessibility gaps, empowering visually impaired users for equal participation in the digital realm. Its modular, user-centric design emphasizes accessibility through seamless integration of voice commands and gesture recognition, facilitating efficient interactions with mobile devices. This research aims to create \"Voice-Companion,\" enhancing smartphone accessibility through seamless voice command integration and gesture recognition. Anticipated results include improved digital engagement and quality of life for users with visual impairments.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing occurrence of Android devices, coupled with their get entry to to touchy and personal information, has made them a high goal for malware developers. The open-supply nature of the Android platform has contributed to the developing vulnerability of malware assaults. presently, Android malware (AM) analysis strategies may be labeled into foremost categories: static evaluation and dynamic evaluation. These techniques are employed to analyze and understand the behavior of AM to mitigate its impact. This research explores the performance of DL model architectures, such as CNN-GRU, as well as traditional ML algorithms including SVM, Random Forest (RF), and decision tree (DT). The DT model achieves the highest accuracy (ACC) of 0.93, followed by RF (0.89), CNN-GRU (0.91), and SVM (0.90). These findings contribute valuable insights for the development of effective malware detection systems, emphasizing the suitability and effectiveness of the examined models in identifying AM.
安卓设备的使用率越来越高，再加上它们可以获取敏感信息和个人信息，使其成为恶意软件开发者的目标。目前，安卓恶意软件（AM）分析策略可分为几大类：静态评估和动态评估。这些技术用于分析和了解 AM 的行为，以减轻其影响。本研究探讨了 DL 模型架构（如 CNN-GRU）以及传统 ML 算法（包括 SVM、随机森林 (RF) 和决策树 (DT)）的性能。DT 模型的准确率（ACC）最高，达到 0.93，其次是 RF（0.89）、CNN-GRU（0.91）和 SVM（0.90）。这些发现为开发有效的恶意软件检测系统提供了宝贵的见解，强调了所研究模型在识别 AM 方面的适用性和有效性。
{"title":"Exploring the Effectiveness of Machine and Deep Learning Techniques for Android Malware Detection","authors":"Khalid Murad Abdullah, Ahmed Adnan Hadi","doi":"10.55529/jipirs.42.1.10","DOIUrl":"https://doi.org/10.55529/jipirs.42.1.10","url":null,"abstract":"The increasing occurrence of Android devices, coupled with their get entry to to touchy and personal information, has made them a high goal for malware developers. The open-supply nature of the Android platform has contributed to the developing vulnerability of malware assaults. presently, Android malware (AM) analysis strategies may be labeled into foremost categories: static evaluation and dynamic evaluation. These techniques are employed to analyze and understand the behavior of AM to mitigate its impact. This research explores the performance of DL model architectures, such as CNN-GRU, as well as traditional ML algorithms including SVM, Random Forest (RF), and decision tree (DT). The DT model achieves the highest accuracy (ACC) of 0.93, followed by RF (0.89), CNN-GRU (0.91), and SVM (0.90). These findings contribute valuable insights for the development of effective malware detection systems, emphasizing the suitability and effectiveness of the examined models in identifying AM.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"38 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud computing is changing game all around the industry. It’s delivering computing resources, such as storage, processing power without investing in and managing physical infrastructure, offering a more flexible and cost-effective approach to meet computational demands. Serverless computing is a cloud computing execution model where cloud providers automatically manage the infrastructure needed to run applications and scale. This paper examines use of AWS Lambda in Data processing, the leading serverless computing service of Amazon Web Services (AWS). Our study will explore the architecture, integration of AWS Lambda with other AWS services, real-world use cases, proposing best practices for Data processing. This research will provide a complete understanding of AWS Lambda and findings here are a valuable resource for people looking to unleash the benefits of AWS Lambda in a scalable and high-performance cloud-native application architecture.
{"title":"Exploring Usage of AWS Lambda in Data Processing","authors":"Shubhodip Sasmal","doi":"10.55529/ijitc.42.35.42","DOIUrl":"https://doi.org/10.55529/ijitc.42.35.42","url":null,"abstract":"Cloud computing is changing game all around the industry. It’s delivering computing resources, such as storage, processing power without investing in and managing physical infrastructure, offering a more flexible and cost-effective approach to meet computational demands. Serverless computing is a cloud computing execution model where cloud providers automatically manage the infrastructure needed to run applications and scale. This paper examines use of AWS Lambda in Data processing, the leading serverless computing service of Amazon Web Services (AWS). Our study will explore the architecture, integration of AWS Lambda with other AWS services, real-world use cases, proposing best practices for Data processing. This research will provide a complete understanding of AWS Lambda and findings here are a valuable resource for people looking to unleash the benefits of AWS Lambda in a scalable and high-performance cloud-native application architecture.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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