Reducing carbon monoxide (CO) emissions is imperative for safeguarding human health and environment. CO adversely affects respiratory health, contributing to respiratory problems and, in severe cases, fatalities. Its reduction aligns with the broader efforts to combat climate change, as CO is often emitted alongside other greenhouse gases. Environmental consequences include air pollution and its detrimental impact on ecosystems. Compliance with emission standards is essential, and reducing Carbon emissions can lead to social and economic benefits, such as increased productivity and reduced healthcare costs. Moreover, the focus on emission reduction drives technological innovation, fostering the development of cleaner and sustainable technologies. In essence, addressing CO emissions is vital for creating a healthier, more sustainable future. However, in most of the cases, there has been no much importance given in scientific management of solid wastes. This has therefore resulted in large magnitude of carbon emission causing serious implications. This paper presents a novel approach to solid waste management, combining carbon emission assessment with advanced object detection technology. We develop an integrated waste management model that employs machine learning techniques for the identification and categorization of metals, non-metals, and plastics within the solid waste stream. To optimize waste sorting and recycling processes, we implement an efficient object detection system that leverages computer vision algorithms. This system enhances the precision of material identification within solid waste, thereby improving sorting accuracy. Additionally, we establish a database to quantify carbon emissions associated with distinct waste management methods, encompassing incineration, composting, recycling, bioremediation, and landfills is used for this work. The novelty of the work lies in the integration of CO2 emissions data and object detection resulting into a decision-making model, providing a holistic evaluation of the environmental impact of varied waste management scenarios. The formulation of recommendations for sustainable waste management practices based on the integrated assessment of carbon footprints and material identification is easy to implement in real world.The technical framework proposed here, aims to inform decision-makers on adopting environmentally conscious strategies for waste management.

The increasing reliance on conversational datasets for natural language processing (NLP) applications necessitates a comprehensive understanding of potential data drift phenomena. This paper investigates the phenomenon of data drift within conversational datasets over time, aiming to develop effective methods for detection and mitigation. Our approach involves the analysis of temporal changes in the distribution of conversation data, focusing on linguistic patterns, user preferences, and contextual nuances. A novel framework leveraging advanced statistical methods and machine learning techniques to quantify and detect data drift within the dataset is proposed here. The methodology is designed to adapt to the evolving nature of language use, capturing subtle shifts in conversational dynamics that may impact model performance. Furthermore, experimental results on a diverse set of conversational datasets, demonstrating the efficacy of our approach in identifying and characterizing data drift is presented here. The findings highlight the importance of continuous monitoring and adaptation to evolving linguistic patterns, ensuring the robustness and generalization capability of NLP models over time. This research contributes to the broader understanding of data drift in conversational datasets and provides a foundation for the development of adaptive NLP models capable of maintaining high performance in dynamic linguistic environments. The proposed framework not only enhances the reliability of existing models but also lays the groundwork for future research in addressing the evolving challenges posed by data drift in natural language conversations.

Water being a scarce resource must be managed properly. Water authorities throughout the world are engaging to ease the pressure on this scarce resource.Existing graph partitioning algorithms are insufficient for finding a satisfactory solution to the water distribution network (WDN) sectorization challenge due to its structural as well as hydraulic constraints. Particularly, there is very little work done on sector isolation such that a sector should have at least one source. In this work we propose the graph partition algorithm as a solution to water distribution problem. The method is tested on real world water network and its efficiency is demonstrated.

Finding music based on one’s mood is difficult unless it is manually classified and separated into distinct playlists. This is especially tough when the song is not in English due to varying lexical and syntactic styles. Our project employs textual sentiment analysis by testing various binary classifier algorithms - Random Forest, Naive Bayes, Support Vector Machine (SVM), and AdaBoost - to gauge which method is best for classifying English and Hindi language music lyrics into positive (happy) and negative (sad) sentiment.

The real estate market is increasing at a rapid pace, which has also led to increase in risk of investment in real estate. In this paper analysis of real estate markets and prediction of the risk involved in the investment has been done. The approach proposed here clusters the property based on market value per square feet located in different school districts. This also help buyers to make scientifically based decisions on investing in property. The result demonstrate that tat the proposed prediction model estimates approximate value for their property. The prediction give a lower as well as upper limit on the market value of the property. This prediction can safeguard against asset bubbles that are created by various parties involved in real estate network. We can conclude that when buyers and investors are aware of the market price of the asset in future they can safeguard themselves from asset bubbles. Thus, this work is also used to protect against asset bubbles.

Since the banks are not open 24x7, there was a need for customers of the bank to withdraw cash anytime during the day. So, the Automatic Teller Machine to satisfy this need. There may be situations where you need emergency cash and your Automatic Teller Machine (ATM) is not able to dispense cash of the desired amount. This may be due to insufficient currency or lack of avail-ability of the required denominations. Now, imagine that your ATM is smart and it helps you to withdraw cash easily. It does so, by giving you an amount that is nearest to the requested amount. It can use either the dynamic or greedy technique to map the requested amount to the nearest amount it can dispense. We have to make sure that the transaction is legitimate and also that the cash requested by the customer is for an emergency. For this purpose, the proposed system adds a module to dispense cash in the above-mentioned scenario.

Online social networks play a major role not only in socio psychological front, but also in the economic aspect. The way social network serves as a platform of information spread, has attracted a wide range of applications at its doorstep. In recent years, lot of efforts are directed to use the phenomenon of vast spread of information, via social networks, in various applications, ranging from poll analysis, product marketing, identifying influential users and so on. One such application that has gained research attention is the influence maximization problem. The influence maximization problem aims to fetch the top influential users in the social networks. The aim of the paper is to provide a comprehensive analysis on the state of art approaches towards identifying influential users. In this review, we discuss various challenges and approaches to identify influential users in online social networks. This review concludes with future research direction, helping researchers to bring possible improvements to the existing body of work.

Crowd sourcing techniques are used in social networks to propagate information at a faster pace through campaigns. One of the challenges of crowd sourcing system is to recruit right users to be a part of successful campaigns. Fetching this right group of people, who influence a vast population to adopt information, is termed as influence maximization. Concerns of scalability and effectiveness need an effective and a viable solution. This paper proposes the solution in three stages. At the first stage, the large social network is pruned based on the nodal properties to make the solution scalable. At the second stage, Outdegree Rank (OR), is proposed and at the third stage, Influence Estimation (IE) approach estimates user influence. This work amalgamates aspects of structure, heuristic and user influence, to form STORIE. The proposed approach is compared to standard heuristics, on various experimental setups such as RNNDp, RNUDp and TVM. The spread of information is observed for HEP, PHY, Twitter, Infectious and YouTube data, under Independent Cascade model and STORIE gives optimal results, with an increase up to 50%. Although the paper discusses influence maximization, the proposed approach is also applicable to understand the spread of epidemics, computer virus, and rumor spreading in the real world and can also be extended to detect anomalies in web and social networks. (C) 2017 Elsevier B.V. All rights reserved.

Social networks with millions of nodes and edges are difficult to visualize and understand. Therefore, approaches to simplify social networks are needed. This paper addresses the problem of pruning social network while not only retaining but also improving its information propagation properties. The paper presents an approach which examines the nodal attribute of a node and develops a criterion to retain a subset of nodes to form a pruned graph of the original social network. To authenticate feasibility of the proposed approach to information propagation process, it is evaluated on small world properties such as average clustering coefficient, diameter, path length, connected components and modularity. The pruned graph, when compared to original social network, shows improvement in small world properties which are essential for information propagation. Results also give a significantly more refined picture of social network, than has been previously highlighted. The efficacy of the pruned graph is demonstrated in the information diffusion process under Independent Cascade (IC) and Linear Threshold (LT) models on various seeding strategies. In all size ranges and across various seeding strategies, the proposed approach performs consistently well in IC model and outperforms other approaches in LT model. Although, the paper discusses the problem with the context of information propagation for viral marketing, the pruned graph generated from the proposed approach is also suitable for any application, where information propagation has to take place reasonably fast and effectively.

Mathematical model have long been used to understand several real world processes. They provide sufficient clarification and understanding. One such mathematical model in context of viral spread was epidemic model. Since then, this model has been used in various context including the spread of viral diseases in the population, information diffusion in social networks and so on. Recently, to fill in the gap seen in SIR, a closed model, RnSIR was developed. An extension to RnSIR model, an open RnSIR model which includes the join and exit rates of users, is proposed in this paper. Through simulation on various social networks, the suitability of the model in mapping the information diffusion process in context of joining and exit rate of users is shown. This article discusses the dynamism of information spread and proposes a model can be used to understand spread of computer virus, the spread of epidemics.

With the advent of internet, online social network is seen as playing a very important role in connecting people and a platform to share ideas. In the current scenario, given restriction on resources available for advertisement, the best place to sell one's product would definitely be these online social networks. The popularity of social network has influenced the computer researchers to ponder on the question on who are the people playing vital roles in information spread. This paper reviews the state of art work done previously in estimation of influence in Online Social Network(OSN) and proposes an innovative idea to improve the existing influence estimation algorithm in terms of search space and runtime.