Authors: MD.Afroz, Assistant Professor B.Narsimha

Abstract: In Indian cities, air pollution is a major problem that affects both the environment and people's health. The traffic, congestion, and pollution levels of Mumbai, one of the most densely populated metropolises in the world, have been steadily rising in recent years. When it comes to city traffic, the top 20 locations are well-known for their extreme congestion. These areas are known as "hotspots" because of the large concentration of vehicles at certain intersections around the city. At the morning's peak hour (from 7:30 to 9:00 am) and the evening's peak hour (from 6:30 to 7:30 pm), cars travel at an average speed of 10 kmph and 8 kmph, respectively. Records show that evenings are the most polluting and crowded times of day. Congestion in the roadways exposes the general population to air and noise pollution. Central Pollution Control Board- specified noise levels are exceeded at the intersections, according to recorded data. The noise levels at the intersections are over 90 dB on average, even though they should be below 75 dB for industrial and 65 dB for commercial settings. 55 decibels for homes and 50 decibels for quiet zones. Vehicle emissions include a wide range of contaminants, including suspended particle matter (SPM), oxides of sulfur (SOx), PAN, and nitrogen oxides (NOx). Any illness affecting the respiratory system is caused by SPM. The research here takes RSPM 2.5 (respirable suspended particulate matter) into account. At 20 different hotspots, we assessed noise levels, RSPM 2.5, number of cars, and congestion at different times of the day and season. The aforementioned parameters are averaged and their standard deviations are computed. In addition, CalRoads View Software is used for data analysis and modeling. Predicting pollution concentrations near intersections where cars idle in waiting for signals is the job of CALINE 3. This program was useful for figuring out how pollutants were distributed at the intersections. Congestion areas and intersections were the sites of the surveys. The poll was conducted via a questionnaire and was open to retailers, street vendors, and traffic police. Breathing and respiratory issues affected the majority of those who participated in the poll. Many of them have had asthma diagnoses at some point. The exposed individuals showed signs of both acute and chronic illnesses. Lots of people who took the poll reported having an illness of the respiratory system, such as sinusitis, tonsillitis, bronchitis, pneumonia, etc. Researchers believe that traffic officers are the best demographic to investigate because of their heightened sensitivity to the effects of vehicle emissions on human systems, particularly the respiratory system and hearing impairments. Participants in the study will be traffic officials tasked with administrative tasks, who will serve as the control group, while traffic officials stationed at different intersections will serve as the exposed group. Individuals working in traffic were tested for noise-induced hearing loss (NIHL). The results clearly show that traffic cops have a greater risk of hearing loss. Unbelievably, 57.14% of traffic cops who have been on the job for more than 25 years have had serious injuries from NIHL. The current investigation did not find any cases of genetic or familial deafness or ear sickness among the participants. The prevalence of asthma was also assessed in other traffic staff. Asthma prevalence among traffic workers can be diagnosed using spirometry analysis. To find the maximum expiratory flow rate in liters per minute, one uses the MINISPIR S/N TO 1795. Asthma diagnosis incidence among traffic police officers aged 18–58 years old (FVC). The ages of the diagnosed traffic professionals range from zero to five, with exposure years in the upper limit. The PEFR is measured in both the exposed and control groups of traffic cops. Approximately eight hours of each workday expose traffic workers directly to particulate matter. Diagnosis ranges from less than five years to over twenty-five years of experience for traffic police. Rejection from the analysis was based on the fact that the traffic police were actively pursuing a smoking habit. The exposed and control groups were both tested for diseases. More than 35% more people in this group have asthma than in the control group. According to the data, there is also an increase in the number of cases of asthma. The effects of air pollution on traffic workers can be mitigated by the development and testing of protective gear. Surgical masks and handkerchiefs are examples of compensatory activities people take in response to pollution threats in order to lower their actual exposures. The research set out to develop a study mask capable of preventing exposure to potentially harmful particles such as dust, allergies, and infectious aerosols. Gradually, further features are included to minimize noise and dust particles that might potentially harm the eyes. We created and tested stage-wise masks. Nose piece with inhaler, exhaust fan near the section of the mouth or nose, lightweight, simple to wear, adaptable to all face sizes and shapes, low maintenance, aesthetically pleasing, and aesthetically pleasing are all features and components that were combined. To determine how effective the study mask was, it was compared to other masks on the market and put through its paces. But how well current face masks protect is anybody's guess. The purpose was to find out how well face masks protect the respiratory system against RSPM levels that might be hazardous. After subtracting the SPM particles from the total air volume inhaled in one minute, the readings on the muslin fabric are reported. Additional issues caused by RSPM are mitigated to a certain degree. The average number of particles collected in one minute is greater than 0.1 gm/min. Not only does the mask shield you from harmful RSPM levels and noise, but it also has an exhaust fan that draws carbon dioxide out of your mouth and keeps you well-ventilated. With this mask, you won't have to worry about the usual issues of sweating or itching while wearing it. Stages 1 and 2 of the mask's modification process were carried out utilizing 3D printer technology. The mask underwent both quantitative and qualitative testing in accordance with OSHA regulations, and test methods were prepared for submission to the ethics committee. All things considered, the outcomes are satisfactory. Additional improvements in terms of size variations, compatibility with additional safety gear like helmets, etc., will allow the device to be released to the public for usage.

Published by: vikaspatanker

Authors: S.Likhitha Priya, Assistant Professor B.Narsimha

Abstract: The transportation networks, economy, and quality of life in cities throughout the world are all negatively impacted by the pervasive problem of traffic congestion. Congestion relief is of critical importance in India since the country's fast urbanization and population increase are making the situation worse. By integrating user and expert perspectives and using a data-driven approach to prepare a knowledge base for traffic congestion, this thesis presents a holistic strategy to address road traffic congestion. The system is tailored to the lndian scenario in the context of inter-urban highways. One module of this thesis is based on user survey methodology, while the other is centered around applying deep learning techniques to analyze the dynamics of congestion. Several facets of congestion prediction are covered in this thesis's objectives. Through observation and feedback, it seeks to understand the causes that contribute to congestion and identify its characteristics from the perspective of travelers. To achieve this goal, we analyzed data from thirteen separate sites along the undivided interurban highway that were selected based on their mixed land use pattern and where congestion is an issue. The survey questionnaire yielded 206 responses, which constitute the data set. The Mann Whitney U test was used to see if there is any heterogeneity between the two traveler groups, drivers and passengers. This goal assesses the features of congestion using the Relative Impoftancc Index (RII) and also scores their preference for measures to alleviate it, in addition to identifying the attributes of congestion. Additionally, it suggests legislative recommendations based on the opinions of travelers in an effort to enhance their traveling experience on interurban highways. The second aim of the thesis uses exploratory factor analysis on the characteristics derived from the first objective and empirical knowledge to investigate the interrelationships among the congestion factors. This objectivc has sampled data from 282 replies and 5 experts. The professionals at Thcsc come from a variety of backgrounds in the transportation industry. After conducting an exploratory factual analysis on the traffic congestion attributes identified in objective one using interpretive knowledge based on the experiments, the fiuzy based total interpretive structural modeling technique (Fwzy TISM) was used to model the interrelationships between the factors. This technique provides both direct and indirect links of cause and effect between the factors. Micmac analysis has been used to determine the independent, dependent, linkage, and autonomous components in the connection diagram that was derived via Fuzzy TISM. Eight important factors contributing to traffic jams on interurban highways were identified through exploratory factors analysis. These factors include inadequate road geometry (Cl), environmental factors (C2), external events (C3), inefficient public transportation (C5), driving behavior (C6), special events (C7), and regional economic dynamics (C8). Using the r- uzzy TISM technique, we may partition the variables into different levels of the hierarchical connection diagram. At the Level I factor level, we have C3, C4, and C6. The second Lcvel component is C5, the third is Cl and CT, and the fourth is C2 and C8. Based on the results of the fuzzy Micmac analysis, we can deduce that factors such as Regional Economic Dynamics (C8), Special Events (C7), and Environmental Factors (C2) are independent variables that impact most of the factors, despite having less driving power. The occurrence of these variables also affects the linkage factor. In this study, archaic traffic management (C4) is a linkage factor that has a high driving power and a high dependence power owing to its unstable proper. It impacts and is affected by other factors such as road geometry dynamics (C3), logistical events (C4), inefficient public transportation facilities (C5), and driving behavior (C6). This thesis's first module is complete with the first two objectives. Objectives three and four are located in the second root of the thcsis plant. The third objective of the project is to automate the process of traffic volume determination utilizing modern dccp lcarning dctcctors and trackers. This would enable the collecting of real-time data for congestion prediction. This objective has been tested on a bespoke dataset consisting of 10,000 virtual reality images from India. The photos in this dataset depict a variety of highway situations with several automobiles. A model for vehicle categorization and traffic volume counting has been developed. It has been trained using YOI-O variants, a single stage object identification methodology, and Byte trackcr, a counting method. The results of the traffic volume counter are examined on the intersection scenes and mid blocks of the intcr urban highways on videos ranging from one minute to five minutes. The short videos achieve an accuracy of 98 degrees of freedom, while the long videos reach an accuracy of up to 87 degrees of freedom. In order to build a comprehensive knowledge base for predicting congestion states, the fourth objective of the study suggests a framework for a knowledge base prediction system regarding road congestion. This framework integrates the results of objectives one and three by combining the impedance factors identified in objective one with traffic flow variables obtained from objective three. The 84-hour data sample includes both weekdays and weekends, and this target has been executed on that basis. The ANFIS (Adaptive Network based fuzzy inferencc system) technique, which is based on several membership functions, has been used to analyze and generate the if-then rules for decision makers. It demonstrates that the existence of rnultiple impedance factors is responsible for the differences in congestion levels on weekdays and weekends. Finally, to improve the efficacy and precision of congestion prediction, the proposed knowledge-based basc road traffic system makes use of both uscr-drivcn insights and cxpert knowlcdgc. By combining user feedback with expert analysis and automated data collection techniques, the system provides a comprehensive view of traffic congestion and how to predict it. In the Indian context, where varied traffic situations and intricate urban landscapes call for precise prediction systems, this comprehensive methodology is of utmost importance. The system is able to produce strong and trustworthy predictions because it uses a combination of fuzzy logic and deep learning techniques, which allow it to deal with the inherent uncertainty and complexity of real-world traffic scenarios. Additionally, the system may enhance its predictive capabilities through the incorporation of user feedback and cxpcrt insights, guaranteeing its relevance and efficacy in ever-changing traffic scenarios. Finally, as a solution to the widespread issue of traffic congestion in India, the proposed knowledge-based road traffic congestion prediction system brings hope. The system offers a comprehensive framework for understanding, analysing, and forecasting congestion lcvcls by combining USCr perspectives, cxpcrt knowledge, and sophisticated technology. Improving traffic management strategies, reducing congestion, and making urban transportation systems in India more efficient might all be possible with the use of this system.

Published by: vikaspatanker

Authors: Dr. Suhas Mache, Bhagyashree Ingale

Abstract: Globally, music streaming services have completely changed how people listen to music. This study uses Power BI- based data analysis to investigate Spotify user behavior and listening habits. Using dynamic dashboards and visual aids, the study looks at regional preferences, artist performance, track patterns, and genre popularity. With the goal of assisting artists, marketers, and producers in making defensible decisions based on streaming data, business intelligence approaches provide actionable insights.

DOI: https://doi.org/10.5281/zenodo.15836070

 

Published by: vikaspatanker

Authors: Dyaga Suresh,

Abstract: – Cyclone separators are widely used in industrial applications to remove particulate matter from gaseous streams. The performance of a cyclone is highly dependent on operational parameters such as inlet velocity and particle size. In this study, Computational Fluid Dynamics (CFD) is utilized to simulate and evaluate the performance of an industrial cyclone separator. Various inlet velocities (10 m/s to 25 m/s) and particle diameters (1 µm to 50 µm) were analyzed to determine their effects on separation efficiency and pressure drop. The simulations reveal that both inlet velocity and particle size significantly influence cyclone performance. This work helps optimize cyclone design parameters to improve dust removal efficiency while minimizing energy losses.

 

 

 

Published by: vikaspatanker

Authors: K.Ganesh, Assistant Professor K.Abhiram

Abstract: With these new tools, we can only speculate as to the level of intelligence that the next generation will achieve by modifying current methods. More and more, society benefits from increasingly sophisticated computing techniques. This article explores and discusses the smart road traffic system, which is one of the advantages. Using these state-of-the-art computational approaches will undoubtedly make the control system for road traffic lights easier and more efficient in the future. In order to optimize the overall trip time, the road traffic system takes into account several parameters, such as total travel time, wait time or delay time in the traffic line, vehicle speed, etc. There are a few issues with the road traffic flow, such as drivers not obeying the regulations of the road, accidents, and congestion. The real issue with traffic congestion is the lack of a better model for managing traffic on roads, which necessitates the dedication of researchers to finding a solution. The current traffic light control system has a few limitations, such as not being able to adjust the delay time based on the density of traffic, not being able to prioritize lane flow when emergency vehicles like fire trucks and ambulances are on the road, and so on. These studies aim to find solutions to the aforementioned traffic issues and, using the aforementioned factors, to optimize the overall travel time in a way that causes minimal delay or congestion. To address these issues, this study employs a probabilistic traffic queue analytical model to find a computationally efficient traffic model. An improved traffic light management system is developed by simulating a model that takes vehicle categorization into account and tests it with various traffic conditions. The model accounts for a variable time delay.

Published by: vikaspatanker

Authors: M. Ramesh Reddy, Assistant Professor K.Abhiram

Abstract: Here we offer a study that falls under the umbrella of regional studies. While ideas like regional development and resources—with a focus on transportation infrastructure—have been around for a while, regional science is a more recent subfield of economics. Nearly everyone is more worried about regional economic planning now than they were a decade ago. A plethora of new concerns and challenges have recently come to light, stemming from the experiences of both industrialized and poor nations. The desirability of various sites for different activities has altered due to the integration of economic activity. Both regional planning on an individual level and coordinated regional planning at the system level have recently garnered increased attention from the governments of a number of nations. A big problem for regional economists has been the increased need for comprehensive information brought about by this interest in economic planning. A nation or area needed a specific amount of transportation infrastructure to make the most of its resources at any particular point in its economic growth. Adami (1987) 1. The effectiveness of the transportation network is a key component of the regional economic structure. In order to reach both local and foreign markets, a country's production and distribution system relies on its transportation network, particularly its road network. This network must be both adequate and efficient. In addition to the particular function it provides, the transport network is important because of the integrating and uniting effect it has on society and the economy.

Published by: vikaspatanker

Authors: B.Dileep, Assistant Professor Mudigonda Harish Kumar

Abstract: This paper proposes hybrid traffic flow prediction techniques using Parametrical Doped Learning (PDL) and Truncated Dual Flow Optimization (TDFO) along with Adaptive Wildfire Optimization (AWO) and Spatial Pattern Super Learning (SPSL). These techniques are validated using datasets from LTPP and PeMS. Performance comparison with traditional algorithms like TrAdaBoost, KLT, and others shows superior outcomes in terms of accuracy, F1-score, sensitivity, and recall.

 

Published by: vikaspatanker

Authors: Dr. M.M. Janeela Therasa, Gayathri CL

Abstract: Mobile Edge Computing (MEC) enables low-latency data processing by bringing computation closer to users. However, it faces critical challenges such as limited storage capacity, unpredictable data patterns, network instability, and increasing volumes of duplicate data. These factors lead to performance degradation, increased retrieval latency, and inefficient resource utilization. To tackle these issues, a robust optimization-based deduplication framework is proposed. This system leverages two algorithms: UEDDE-C (Uncertainty-based Edge Data Deduplication with Column and Constraint Generation), which provides high accuracy in detecting exact duplicates, and UEDDE-A (Approximation-based), which is computationally lightweight and effective for identifying near duplicates. Furthermore, since data security is a pressing concern in edge environments, the proposed system integrates AES encryption to safeguard sensitive information before the deduplication process. This ensures not only confidentiality and integrity but also standardizes data into consistent formats for more efficient handling. The proposed framework significantly reduces redundant storage, lowers network traffic, speeds up data access, ensures security compliance, and enhances overall MEC system reliability under uncertain and dynamic conditions.

DOI: https://doi.org/10.5281/zenodo.15834266

 

Published by: vikaspatanker

Authors: Dnyneshwar Gawade

Abstract: Video games have evolved from simple enter- tainment to complex digital environments with significant implications for mental health. This paper explores the dual nature of video games, highlighting their potential benefits, such as cognitive enhancement, stress relief, and social con- nectivity, alongside risks like addiction, social isolation, and increased aggression. Drawing on recent studies, we examine how game genres, play duration, and player demographics influence mental health outcomes. We also discuss future directions, including the development of therapeutic games and the need for nuanced research to inform policy and game design. The findings suggest that moderate gaming can foster mental well-being, while excessive use poses significant risks, necessitating tailored interventions.

DOI: https://doi.org/10.5281/zenodo.15836400

Published by: vikaspatanker

Authors: Vinodhkumar S, Pravin P, Siva Sankaralingam G

Abstract: The occurrence of strokes through a model and prediction helps to find out the utilizing data on demographic aspects, lifestyle, and other parameters of a fairly well-known and public healthcare dataset containing information about age, gender, hypertension, heart disease, smoking, BMI, and average glucose level. Preprocessing of the data involved dealing with missing values, encoding the categorical variables, and balancing the data through the Synthetic Minority Oversampling Tech- nique (SMOTE) to overcome the problem of imbalance. Then, several algorithms were trained and tested, including Logistic Regression, Decision Trees, Random Forest, SVM, and Naive Bayes, and the performance was evaluated in terms of accuracy, precision, recall, and F1 score. The results show that ensemble and tree-based algorithms obtain high precision with more than 90% accuracy in predicting who could have a main risk for stroke. Onset feature importance shows age, hypertension, heart disease, and glucose level as important predictors. The gists from the results show that potential exists in machine-learning methods for early risk assessment of stroke and strongly support the implementation of data-driven tools in clinical decision-making to provide timely intervention to reduce stroke morbidity and mortality.

DOI: https://doi.org/10.5281/zenodo.15833732

 

Published by: vikaspatanker