International Journal on Recent and Innovation Trends in Computing and Communication
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Smart Traffic Flow: Engineering Turn Lanes, Slip Ramps, and Signal Systems for Congestion Mitigation
Get PDFUrban traffic congestion poses significant challenges to mobility, safety, and sustainability, particularly in rapidly growing metropolitan areas. This study evaluates the effectiveness of integrating dedicated turn lanes, slip ramps, and adaptive traffic signal control (ATSC) systems to mitigate congestion, using a case study of Peachtree Road and Lenox Road in Atlanta, Georgia. A mixed-methods approach, combining literature synthesis, VISSIM microsimulation, and field data analysis, was employed to assess six scenarios: baseline, turn lane, slip ramp, ATSC, integrated (with V2I communication), and multimodal (with pedestrian, cyclist, and transit priority). Results show that the integrated scenario reduced average delay by 41.4%, improved LOS from E to B, increased throughput by 14.6%, and decreased crashes by 25%. The multimodal scenario achieved a 36.2% delay reduction while enhancing non-motorized and transit performance. Challenges, including high costs, right-of-way limitations, and safety concerns, were identified, with recommendations for phased implementation and stakeholder engagement. The study provides a framework for designing smart traffic flow systems, emphasizing synergy, equity, and technological integration. Findings are relevant for urban planners seeking evidence-based solutions to congestion in high-traffic corridors
Adaptive Suspension Systems in Motorcycles: AI-Assisted Control and Structural Optimization
Get PDFThe evolution of motorcycle suspension design has expanded from conventional telescopic forks to advanced smart systems integrating sensors and adaptive dampers. This research proposes an adaptive suspension system that leverages artificial intelligence (AI) for real-time decision-making, structural optimization through finite element analysis (FEA), and the integration of digital twin technology for predictive maintenance. Simulation and computational analysis reveal superior load distribution, improved vibration damping, and reduced brake dive compared to traditional designs. AI-assisted adaptability enables the system to dynamically respond to diverse terrain conditions, ensuring enhanced rider comfort, stability, and safety. The findings contribute to the next generation of intelligent motorcycle dynamics
A Study on Financial Performance of Selected Public Sector Banks
Get PDFPublic sector banks are those in which the government holds more than 50% of the total stock. The government formulates all the financial guidelines for public sector banks. The public sector banks operate under the government to inspire trust in the depositors that their money is safe. The aim of the study is to find out the financial performance of selected Public Sector Banks using ratio analysis. Tools used for the study Mean, F-test or ANOVA (Analysis of Variances). Conclusion of the study Public sector banks want to increase the management capability to increase the profits,ROE, EPS, ROI and to increase the efficiency of banks
Biochemical Signposts: Navigating the Landscape of Early Cancer Diagnosis and Prognostic Insights
Get PDFThis exploration investigates the complicated scene of malignant growth from the perspective of "Biochemical Signs," expecting to upset early analysis and prognostic experiences. Utilizing an exhaustive methodology, biomarkers have distinguished across bosom, lung, and prostate tumors, with values going from 0.45 to 3.10. The coordination of cutting-edge imaging procedures, including PET and X-ray, brought about indicative precision paces of 89% and 85%, separately. Prognostic investigations divulged biomarkers' shifting effects on understanding endurance, with risk proportions going from 0.70 to 1.80 and p-values featuring factual importance. The coordination of genomic, proteomic, and metabolomic information created all-encompassing sub-atomic profiles, associated with unmistakable clinical results. For example, the luminal A subtype showed an incorporated score of 0.85, related to a 75% endurance rate. This study approves the proposed methodology as well as lays out its prevalence in exactness, awareness, and clinical pertinence when contrasted with existing methodologies. The blend of different information types and the vigor of the created demonstrative devices give an establishment to groundbreaking headways in malignant growth research and customized patient consideration
An Online Inquiry and Information Management System in Bicol Garments
Get PDFInformation management plays a very significant role in an organization. It helps the organization manage files and documents in an organized way. The main objective of this study is to propose a system that would help improve the process of handling and recording customer information as well as enhance communication between the customer and the business organization. In order to ensure the improvement of the process in Bicol Garments, the proponents have identified the following problems: manual listing and processing of customers' information and job orders; as well as communication problems. With this, business process management was used for modeling and reengineering the workflow process. The study utilizes business process management to show the existing process and the proposed new process for Bicol Garments. By analyzing the As-Is process, the proponents noticed several problems in the current process and proposed a To-Be BPMN in order to resolve the issue. The experimentation in the study also shows that the use of the proposed system will improve the current process at Bicol Garments and provide ease of access to the customer. The study has proposed several protocols to help improve the process. These protocols were simulated in several instances and have shown improvements. However, the result of this study cannot be completely generalized because it has not yet been implemented in Bicol Garments
Investigation of Evolutionary Computation Techniques for Enhancing Solar Photovoltaic Cell Performance
Get PDFThe pursuit of optimized solar photovoltaic (PV) cell parameters is critical for advancing renewable energy technologies amidst global energy security and climate change challenges. This research investigates the efficacy of particle swarm optimization (PSO) and gray wolf optimization (GWO) in fine-tuning PV cell behavior parameters. Leveraging evolutionary computation, the study aims to maximize energy output, minimize costs, and enhance system reliability by optimizing material properties, structural configurations, and operating conditions. Through iterative optimization, PSO and GWO navigate the parameter space with precision, yielding solutions that maximize energy yield and system efficiency
Developing a Trustworthy Cloud Service Framework for Cloud Computing Security
Get PDFCloud computing is quickly becoming an essential platform for sharing infrastructure, software, apps, and corporate resources. Cloud computing has many advantages, but users still have a lot of questions about the dependability and safety of cloud services. Concerns about the hazards associated with the possible exploitation of this technology to undertake criminal operations might threaten the undeniable success of cloud computing. To ensure happy customers, the cloud model must prioritize safety, openness, and dependability.Its main purpose is data security, which concerns everyone contemplating cloud services. A cloud-based assault protection system will safeguard data, communications, and information.According to studies, the recommended technique is successful, however updating tags and blocks when data is amended requires computation and communication expenses. Scalability, data secrecy, and decentralized double encryption improve security.The proposed method employs cloud servers for computation-intensive tasks and protects data content by depriving data owners and users of privilege information. Also ensures responsibility. Sharing health data on the cloud is feasible, cost-effective, efficient, adaptive, and better for individuals. This"Advanced Encryption Standard with Lightweight Cipher-text-Identity and Attribute-based Encryption" (AES-lightweight CP-ABE) aims to protect sensitive data
Exploring the Student Experiences of LMS (Learning Management System) in Higher Education
Get PDFThe study aims to investigate the student experience and perception of blending LMS (Learning Management System) with the traditional classroom instructional strategy. It also analyses the impact of LMS on students’ engagement and evaluate their satisfaction with it. The research used quantitative design with single group post-test only experimental design. The study used complete enumeration design where all 47 students of Masters in Education (M.Ed.) programme were exposed to experiment but only 42 students became part of study by responding to post test. Moodle was used as LMS and participants responses were sought on a reliable and valid self-developed instrument including twenty statements. The findings of the study reveal that students had a positive experience with the Learning Management System (LMS) and they found it easy to use, user-friendly, flexible, and beneficial for their studies. However, it was found that by giving better orientation to LMS and expediently resolving technical glitches, blended instructional strategy can be made more effective
Comparative Study of Rectangular Patch Antenna using FR4 and RT Duroid Substrates
Get PDFThis research presents a comparative analysis between RT Duroid and FR4 substrates for microstrip patch antennas using Zealand IE3D software. Spanning 3 GHz to 5 GHz, the study assesses parameters like return loss, VSWR, Axia, Antenna Efficiency, Radiation Efficiency, Gain, Directivity and Bandwidth. Results obtained through simulation are scrutinized to understand how substrate selection influences the antenna's behaviour and physical dimensions across the specified frequency range. This research provides valuable insights for microwave engineers, facilitating the informed selection of substrate materials based on application-specific requirements and optimizing the design of microstrip patch antennas for high-frequency communication systems
Cotton Crop Leaf Disease Detection System Using Machine Learning Approaches to Improve Efficiency”
Get PDFIndia's economy heavily depends on agriculture. Over 70% of people in India make their living from agriculture. Accurate and prompt diagnosis of illnesses that harm crops is one of the biggest challenges facing the agriculture sector. Diseases affect crop quality and have the power to destroy whole hectares of agricultural output, costing farmers a lot of money.
Current diagnosis methods need the presence of highly experienced professionals and take a lot of time to examine the damaged crop, understand the symptoms, identify the illness, and provide effective remedies. Due to the limitations of these methodologies, researchers are now looking for other approaches to early illness detection and classification. Addressing food security may be facilitated by smart farming and adequate infrastructure.
In recent years, machine learning has demonstrated tremendous potential in identifying and categorising trends in linked academic disciplines. The goal of the current study is to evaluate the accuracy, precision, recall, and training time of traditional machine learning techniques like the Support Vector Machine (SVM) and random forest against the performance of convolutional neural network (CNN) methods and architectures like Inceptionv3, VGG16, and RasNet50 with data augmentation and transfer learning. The models were trained with the use of a manually gathered database from a farm and a government organisation, which had four distinct classes of photos, including healthy plants. The highest performing model was the Inceptionv3 architecture of CNN with transfer learning, which achieved an overall accuracy of 94 percent and met the demand for a more reliable and effective classification model. Additionally, when the quantity of training data rose, it was shown that the performance of the developed models increased.
The outcomes obtained using transfer learning algorithms on CNN architectures are extremely encouraging, and they may be further refined to create a thorough leaf disease diagnosis system that can function in a real-world environment. As a result, it may enable the agricultural community to recognise problems and start prompt treatment without the intervention of qualified specialists