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Box culverts are the structures which are used when the path of water in the natural stream crosses roads, railway lines, flyovers etc. They are normally cheaper than bridges, which make them the natural stream passes through channels. In this work, the review of various authors and their views in the design and analysis of box culvert with software approach and comparison between software and manual approach has shown. The IS standard requirements in the design manual for roads and bridges (IRC-6-2000, IS 21-2000) is used in the structural designing of concrete box culverts. In this paper study about the different classes of IRC loadings and their effect on without and with cushioning conditions imposed on box culvert. The pressure cases are then checked for both with cushioning and without cushioning loading cases. The structure designing includes the considerations of pressure cases (Box empty, Full, surcharge load) and factors such as Impact load, Braking force, Dispersal of load through fill, Effective width, Coefficients of earth pressure, Live load etc. The structural elements are designed to withstand the maximum bending moments and shear forces respectively. In the present study, this paper provides full discussion on the provisions in the codes, considerations and justifications of all the above aspects of design.

This paper presents an experimental study on the performance characteristics of a capacitor start induction motor. The objective of the study is to understand the starting torque, starting current, and speed control of the motor under different loads and input voltages. The experimental setup consists of a capacitor start induction motor, a tachometer, an ammeter, a voltmeter, a wattmeter, and a load. The motor is started by applying the input voltage to the stator winding, and the centrifugal switch disconnects the starting winding after the motor reaches a certain speed. The capacitor provides a phase shift in the current and voltage waveforms, which helps to start the motor. The motor performance is measured using the tachometer, ammeter, voltmeter, and wattmeter, and the data is analyzed to understand the efficiency and performance characteristics of the motor. The experimental results show that the motor can provide a high starting torque and efficient speed control with the use of a capacitor. The study provides practical insights into the performance characteristics and control of the capacitor start induction motor, and can be used to improve the energy efficiency of the motor in various applications.

Traffic flow prediction is an essential tool for traffic management, which can assist traffic operators in making informed decisions for traffic control and management. Accurate traffic flow prediction can also help commuters to plan their journeys efficiently and reduce travel time. The use of Support Vector Regression algorithm for traffic flow prediction has shown promising results in various studies, and it can be used to predict traffic flow at different time intervals, such as hourly, daily, weekly, or even yearly. Additionally, the application of machine learning and artificial intelligence techniques can further enhance the accuracy of traffic flow prediction, which can lead to more effective traffic management strategies

IoT and Remote Detecting Innovation are generally utilized wherever in the current logical world. As the innovation is developing and evolving quickly, Remote detecting Organization (WSN) serves to update the innovation. In the examination field of remote sensor networks, the power effective time is a significant issue. The answer to this issue can be tackled by utilizing the LoRaWAN innovation with IoT. The fundamental thought of this is to grasp how information goes through a remote medium transmission utilizing remote sensor organization and checking framework. This paper plans a water system framework that is robotized by utilizing controllable boundary soil dampness on the grounds that they are the significant elements to be controlled in Dad (Accuracy Horticulture).

The power quality requirement is one of the most important issues especially with the introduction of sophisticated device whose performance is very sensitive to the quality of power supply. Power quality problem is an occurrence manifested as a non standard voltage, current or frequency that results in failure of end user equipment. One of the major problem deal with sag and harmonics .To solve this problem series compensation is used to improve power quality of an isolated power system is considered. The role of compensator not only mitigates voltage sag but also reduces harmonics due to presence of nonlinear load in the network. In this paper series compensation is proposed and principle of harmonics mitigation is explain and the compensation strategies and control philosophy used in series compensation is explain. The isolated power system model is simulated in matlab Simu link software and simulation result shows the reduce harmonics and thus improve power quality of the isolated power system

For monitoring patients at risk for chronic diseases, wireless and wearable sensors are now widely used in the healthcare sector. This guarantees that patients adhere to their treatment regimens and safeguards them from unexpected attacks. Numerous sensors have produced a vast amount of data. In this paper, we propose to use fog computing to support the monitoring of asthma patients using IoT-enabled devices such as smart inhalers to monitor the rate and environmental factors surrounding asthma attacks, ensuring that data is collected and processed effectively. The main challenge would be limiting the selection of information to that which is pertinent to the patient's health in the given context. A fog computing layer is necessary because a simple sensor-to-cloud architecture is unworkable. By minimizing the amount of data transferred back and forth between the cloud and the sensors and removing the risk that a data center failure carries, this increases overall system efficiency.

The use of chat applications has increased to a large extent because it provides users with the facility to interact with their family and friends instantly. But these chat applications are being used for carrying out some unethical practices like cyber-bullying, online harassment, etc and there is a need to find a way to curb this issue. This paper aims to give a clear insight into solutions currently being employed or suggested to overcome this problem and to identify the drawbacks associated with these solutions on a large scale. It also proposes a working model for chat apps with profanity detection based on the gaps identified which can detect foul language and thus it can significantly reduce these unethical practices prevalent today in the world of social media.

Touch or the tactile sense is a important element of human interaction, and it has the power to generate emotional responses in individuals. The presence of heavy objects can give the impression of importance to job candidates, rough objects can make social interactions appear more challenging, and hard objects can lead to a more rigid approach in negotiations [1]. In the context of interior design, touch can be used to create a sensory experience and influence the emotional responses of those who inhabit a space. In this paper, we explore the role of touch or tactile sense in interior design and its impact on emotional experience. Through experiments, this study will examine the effects of different types of touch surfaces, such as smooth versus textured, . Our results show that to how much degree, the touch can significantly impact emotional response in interior spaces, with certain types of touch surfaces generating specific emotional responses. These findings will have important implications for the use of touch in interior design and suggest that designers should consider to incorporate the role of touch based on these findings to create a sensory based experience for the users of space.

This paper presents a comprehensive description, from a security and accessibility perspective, of various security systems and technologies. For everyone who owns or rents a home, home security should be a top concern. Every individual needs a safe and secure residential space. Most of the market’s security systems; however, are either costly or unsafe. Many times there are a number of loopholes present in the security systems and security devices which can be breached easily. The various security automation technologies considered in this review paper include Fiber sensor, Accelerometer, Global Mobile Communication System, Mobile Home Automation Systems, Internet Home Automation Systems. There is a decrease in the price of sensors as we see progress in the technology market, which makes vibration signature identification systems a cheap and effective alternative to the expensive security systems on the market

With a value of around 40% of overall energy consumption, buildings are a significant main energy user in the global energy sector. Currently, the absence of conventional energy sources encourages the construction of net zero energy buildings (NZEBs). Net or nearly zero energy buildings (NZEB) are exceptionally energy-efficient structures with very little demand for energy, all of which are supplied by renewable resources. Such structures annually balance their energy use and production. It's crucial to understand the broad notion of net zero energy construction. The paper's objective is to present and discuss various strategies and ideas to achieve Net Zero Building through actual Modeling. Designing and Optimization of Building

The increasing popularity of e-commerce has led to a growing demand for tools that help consumers track prices and make informed purchasing decisions. This paper presents an online price tracker that enables users to monitor the prices of products across multiple e-commerce websites in real-time. The tracker utilizes web scraping techniques to extract pricing data from various e-commerce platforms and presents it in a user-friendly interface. Users can set up custom alerts to receive notifications when the price of a product drops below a certain threshold, allowing them to take advantage of discounts and save money. The price tracker also provides historical price data for each product, enabling users to analyze trends and make more informed purchasing decisions. Overall, the online price tracker provides a valuable tool for consumers to make informed decisions when shopping online.

Machine Learning has become the need of today’s world. Any intelligent system is incomplete without Machine Learning. People use ML models in various applications. Every ML-based project has to go through some of the steps during the development process. This process consists of certain common steps which every ML project needs to follow. The process of building ML models is tedious and time consuming. There are some common problems which people face while developing MLbased projects. That includes lack of resources and infrastructure, lack of uniformity in codebase and writing repetitive code for every project. These problems are very serious as building ML models needs a lot of effort and time. This paper aims to describe some of the problems that people face during the development of ML-based projects. It also provides a solution to reduce many problems and save efforts in the development process. The system proposed in this paper can save a lot of time and effort in building ML models. It will help to build projects faster and increase efficiency.

Instant messaging services on mobile devices, such as WhatsApp, have become immensely popular, largely due to their end-to-end encryption (E2EE) feature, which ensures user privacy. However, this has raised concerns for some governments who argue that E2EE makes it challenging to combat terrorism and organized crime. These governments have expressed the desire for a "backdoor" to access messages in cases of credible threats to national security. However, WhatsApp users have strongly opposed this idea, citing concerns about privacy infringement and potential exploitation by hackers. This paper presents the advantages of maintaining E2EE in WhatsApp and argues against granting governments a "backdoor" to access user messages. It highlights the benefits of encryption in safeguarding consumer security and privacy, while also acknowledging the challenges it poses to public safety and national security. In the realm of internet messaging security, cryptography plays a crucial role in protecting networks. This paper aims to raise awareness among common computer users about the importance of messaging security and its requirements. Several cryptographic techniques have been developed to achieve secure communication, and the proposed messaging system ensures security in accordance with standard security models

Physiological interference in a person’s life can bring out many difficulties which affect the basic abilities of a person to do simple tasks. Depression, one such issue, can be detected through only medically trained psychiatrists and the early detection of depression is crucial in providing effective treatment. Social Media platforms prove to be a valuable source of information for this motive. Social Media provides a safe platform for individuals to express their emotions and feelings while at the same time, it can also contribute to the development of depression symptoms particularly in vulnerable populations i.e. Younger generations, due to factors such as social comparison, cyberbullying, social isolation and constant need for validation. This paper further explores evidence of the link between social media use and depression in aiding early detection. The objective of the current study is to apply various ML techniques to assist psychiatrists in recognizing patient symptoms. The plat-form used for our research is Twitter. The model detects the symptoms/behavior of early depression in the users through their current and past tweets. For this purpose, we have deployed various approaches to train and test an ML model or classifier using the right features depending on the information gathered from a questionnaire and through the features extracted from a user’s tweets and their social network activities.

The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in any metal and coal extraction techniques for proliferation of production levels has caused further concern for safety in this industry for mines workers. This paper presents Mines workers face numerous hazards while working in mines, and it is essential to provide them with a safe working environment. In this paper, we propose a smart alerting system for mines workers that utilizes sensors and sensing technique to detect potential hazards and alert workers in real-time. The proposed system is working smartly - it monitors, controls and alerting by itself and feedback Button is provided for workers from which they can with the help this system send alert signal to control room. The system proposed here consists of devices sensor & Button equipped with sensors that monitor various parameters such as temperature, gas concentration. The data transmitted to a central server, by Wi-Fi and using ESP NOW technique using for transfer deep mine data to central server and data collected by these sensors are used to analyze the data and detect potential hazards.

It is necessary to simulate and investigate the proper feasibility of Solar PV system at a given location. This study is done to evaluate the feasibility of grid connected rooftop solar photovoltaic system for a K.K. Wagh Institute of Engineering Education and Research, Nashik, India. This paper focuses on the aspects of the design involved in the setup of the system, not only just the design, but also the component such as installation, site evaluation of a given rooftop to the final cost analysis. This paper will give the average understanding of how a rooftop photovoltaic system is been processed.

This paper provides a comprehensive review of a papers which are related to bank application designed for devices, focusing on its features, usability, security, and overall user experience. The application is evaluated based on its ability to perform typical banking tasks, such as checking account balances, transferring funds and managing transactions. The review also examines the application's design, navigation, and user interface, as well as its security features, including authentication, encryption, and protection against unauthorized access. Overall, the review concludes that the bank application is a highly effective and user-friendly tool for managing finances on the go, with robust security measures in place to protect sensitive information.

Breast cancer is among the most widespread ailments afflicting women globally. The timely identification and accurate diagnosis are vital for successful therapy and improved prognosis. Over the past years, researchers have extensively utilized machine learning algorithms to detect breast cancer from medical images. This paper proposes an innovative hybrid clustering approach combining both k-means and Gaussian mixture models (GMM) to enhance breast cancer detection performance. By utilizing the k-means clustering approach as a basis, our algorithm generates initial cluster centers from the input data. With these results, we then proceed to implement the GMM algorithm to further refine our clustering outcomes and calculate each cluster's probability distribution accordingly. Through evaluation on publicly available mammography images, our hybrid algorithm outperformed both k-means and GMM algorithms in terms of sensitivity, specificity and area under the receiver operating characteristic curve (AUC-ROC).

Software-defined wide area network (SD-WAN) is a networking technology that enables organizations to build secure, high-performance WANs (Wide Area Networks) using software-based controls that provide simplified network management, help businesses offer seamless connectivity to their users in this current world trend of cloud technology and Edge computing. It operates in a centralized manner, allowing for the control of the entire network at once surpassing the limitations of traditional WANs. This paper discusses the SD-WAN technology, including why it was created, its benefits, the components that make up the technology, the challenges associated with its adoption, the security features it offers, and the future of SD-WAN in networking.

To enhance the security of the ATM. To avoid ATM robberies and the wrong person misusing the ATM so that they can lead a secure and reliable life. The system is to support the intelligence system. Without hesitation, use ATMs to make the world a conversion community. Once the client inserts the cardboard into the ATM, a session is processed, so the system can start a face detection victimization camera placed close to the ATM and builds short-lived identity information of the clients, and the user face verification to perform on the ATM. A good user would use the traditional method because the invalid user cannot access the ATM card, they offer the secondary identification to the system mechanically, and the unauthorized person would continue the transactions. The objective of the proposed study in this paper is to prevent ATM fraud as well as secure transactions from the user side.

Small and medium-sized enterprises are increasingly adopting cloud computing, and optimizing the cost of cloud resources has become a crucial concern for them. Although several methods have been proposed to optimize cloud computing resources, these methods mainly focus on a single factor, such as compute power, which may not yield satisfactory results in real-world cloud workloads that are multi-factor, dynamic, and irregular. This paper proposes a new approach that utilizes anomaly detection, machine learning, and particle swarm optimization to achieve a cost-optimal cloud resource configuration.

In every part of the world, there is a tremendous growth in digital literacy in the present era. People are trying to access internet-based applications with the use of digital machines. As a result, the internet as become a primary requirement for everyone, and most business transactions often take place conveniently across the network. On the other hand, intruders involved in making intrusions and doing activities such as capturing passwords, compromise on the route, collecting details of credit cards, etc. Many malicious activities are taking place over the network due to this intruding activity on the internet. The intrusion detection system (IDS) helps to find the attacks on the system and the intruders are detected. This paper has expected for an approach to develop IDS by using the principal component analysis (PCA) and the random forest classification algorithm. Where the PCA will help to shape the dataset by reducing the dimensionality of the dataset and the random forest will help in classification. Results obtained states that the proposed approach works more resourcefully in terms of accuracy as compared to other methods like SVM, Naïve Bayes, and Decision Tree.

Phishing attacks are a common form of cyber-attacks that can result in data breaches, financial losses, and harm an organization's reputation. The purpose of the study is to evaluate and compare three popular anti-phishing tools' effectiveness in detecting and preventing such attacks. The research method involves conducting a study to identify best performing antiphishing tool. The findings show that some tools have high accuracy; others have a lower false positive rate. Overall, this research paper provides useful information about the effectiveness of anti-phishing tools and can help organizations select the most appropriate tool for their specific needs.

The aerospace sector has seen a significant prominence in the field of aerodynamics. Aerodynamic behaviour differs from one structure to the next depending on the specifics of each one. Due to flexible aeroplane structures, aeroelastic phenomena occur, when structural deformations alter aerodynamic forces. A feedback mechanism between the increased structural deformations and the added aerodynamic forces increases further aerodynamic loads. These interactions may diminish until they achieve an equilibrium state or, if resonance takes place, they may diverge drastically. The most challenging to forecast instability aeroelasticity aeroelastic phenomenon is flutter. An accurate finite element aerodynamic wing model is required for flutter study. A review of various approaches for conducting flutter analysis of AGARD 445.6 wing is presented in this paper. AGARD 445.6 wing has been used as standard configuration for the analogy of all aeroelastic behaviours. Under different flow regimes, the paper examines the platforms for finite element software that are used for flutter analysis.

Solid propelled rocket motors (SRMs) static firing tests are crucial tests in the aerospace industry while developing new motors as well as to ensure the quality of a motor batch. The operator can use this sort of test to determine whether the motor performance meets the project criteria by obtaining the measured "thrust versus time of burning" graphic the motor produces while burning. In this paper over all thrust measurement uncertaintyof a solid propellant rocket motor test bed is studied. During static test of solid motors requires the dynamic characteristics of the Static test configuration. To find out the magnitude of thrust oscillation, characterization of test setup and force transfer characteristics from motor case to load cell are required.

The modelling and simulation of the batteries are the subjects of this study. It requires using MATLAB to understand the mathematical link between the battery input, output, and simulator parameters. Currently, we observe an increase in interest in the power system from entities like electric utilities, energy service providers, auto manufacturers, etc. This paper demonstrates how to simulate charging or discharging using Simulink and MATLAB. Since its development over the past century, electricity has become the most adaptable and common type of energy used worldwide. With the help of batteries, we may store electrical energy in the form of chemical energy, which can later be used to produce electricity once more

Personality detection has become an important area of research due to its potential applications in various fields such as marketing, psychology, and human resource management. In this paper, we present a personality detector that utilizes the Big Five personality traits model to analyze and predict the personality traits of individuals. The Big Five personality traits model is widely used in personality psychology, and it consists of five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. Our system utilizes a machine learning algorithm that analyzes various features such as text, social media posts, and speech to predict an individual's personality traits. The front-end of the system is developed using Python and Django, making it easy to use and accessible to anyone with an internet connection. We evaluated our system using a dataset of 1000 individuals and achieved an accuracy of 85% in predicting their personality traits

In today’s modern world people all around the globe are becoming more interested in their health and lifestyle. But just avoiding junk food and doing an exercise is not enough, we require a balanced diet. A balanced diet based on our height, weight and age can lead a healthy life. Combined with physical activity, your diet can help you to reach and maintain a healthy weight, reduce your risk of chronic diseases (like heart disease and cancer), and promote your overall health. A balanced diet is one that gives your body the nutrients it needs to function correctly.’ Our aim is to make a system that helps users to make personalized diet plan and recall them for their meal time to time and help them to create a balance in overall calorie intake throughout the day. The system would remind the user on a daily basis to have their meal and with many options to choose from for a personalize meal plan for a day. Also, the user will be able to choose any type of healthy food they want as per their convenience out of the options that will be there to them by the system. Our body use calories for basically everything like breathing, walking, running etc. On average a person needs 2000 calories per day but specifically intake of calories depends upon persons physical aspects like weight, height, age and gender. So, your food choices each day affect your health — how you feel today, tomorrow, and in the future. Thus, a proposed system gives required calories for a diet plan based on your physical aspects and your end goal.

Renewable energy sources are increasingly fulfilling the need for continuous energy supply. However, energy derived from these sources cannot be directly utilized and must be stored in energy storage systems such as Battery Energy Storage Systems (BESS), Compressed air systems, Mechanical systems, Hydraulic systems, among others. In this paper, we will discuss the study and analysis of a Gravity-based energy storage system and its fabrication of a model-based representation. The objective is to improve the overall concept and efficiency of the system. Gravity-based energy storage systems utilize gravity's force to store potential energy. The system functions by elevating a heavy object to a high altitude and subsequently releasing it to generate electricity. The lifting motion stores potential energy, which is then converted to kinetic energy as the weight descends and can be used to produce electricity. The paper will provide additional information about the specific gravity-based energy storage system being analysed, as there are different designs and configurations. Additionally, the paper will compare and contrast this method of energy storage with other methods such as battery storage and compressed air storage, highlighting the advantages and disadvantages of each. Lastly, the paper will touch on the development and testing of a physical model or simulation of the gravity-based energy storage system. The paper will provide details on the design and testing process, as well as any modifications or improvements made to the system based on the findings

bstract: The present paper examines the organisational commitment and job satisfaction among employees.80 employees working in several companies and institutions were taken. To evaluate Organisational Commitment Questionnaire (OCQ) was used to measure organisational commitment and Job Satisfaction Scale (JSS) was used for measuring job satisfaction of the employees. To analyse the obtained data, Person’s Correlation and t-test were employed. The result of the present study indicates that there is a significant relationship between organisational commitment and job satisfaction. The t-test results indicate that there is no significant difference in organisational commitment and job satisfaction among male and female employees. This study gives an understanding about organisational commitment and job satisfaction among employees.

The usage of smart phones equipped with GPS navigation unit have increased rapidly from 3% to more than 20% in the past five years. Hence, a smart phone can be used efficiently for personal safety or various other protection purposes especially for women. This app can be activated by a single click when the user feels she is in danger. This application communiqués the user’s location to the registered contacts for every few seconds in the form of message. Thus, it acts like a sentinel following behind the person till the user feels she is safe. This paper presents analysis a unique feature of this application to send the message to the registered contacts continuously till they are pressing ‘HELP’ button. Continuous location tracking information via SMS helps to find the location of the victim quickly and can be rescued safely. This application aims to ensure women safety. This is achieved by addressing the circumstances that compromise the safety of women in today’s day and age. This app ensures women are not put into such situations through various features offered by our system.

Efficient and reliable monitoring of wild animals in their natural habitats is essential to inform conservation and management decisions. Automatic covert cameras or “camera traps” are being an increasingly popular tool for wildlife monitoring due to their effectiveness and reliability in collecting data of wildlife unobtrusively, continuously and in large volume. However, processing such a large volume of images and videos captured from camera traps manually is extremely expensive, time-consuming and also monotonous. This presents a major obstacle to scientists and ecologists to monitor wildlife in an open environment. Leveraging on recent advances in deep learning techniques in computer vision, we propose in this paper a framework to build automated animal recognition in the wild, aiming at an automated wildlife monitoring system. In particular, we use a single-labeled dataset from Wildlife Spotter project, done by citizen scientists, and the state-of-the-art deep convolutional neural network architectures, to train a computational system capable of filtering animal images and identifying species automatically. Our experimental results achieved an accuracy at 96.6% for the task of detecting images containing animal, and 90.4% for identifying the three most common species among the set of images of wild animals taken in Southcentral Victoria, Australia, demonstrating the feasibility of building fully automated wildlife observation. This, in turn, can therefore speed up research findings, construct more efficient citizen science based monitoring systems and subsequent management decisions, having the potential to make significant impacts to the world of ecology and trap camera images analysis.

In recent days, Parking allocation has become a major problem in modern cities for which numerous smart parking systems have been developed. This paper aims to provide comprehensive study, comparison and extensive analysis of Smart Parking Systems in terms of technological approach, user interface, computational approaches, and service provided. Moreover, the paper fills up the research gap by providing a clear insight into the suitability of SPSs in various environmental conditions and highlights their advantages/disadvantages. The extensive comparison among multiple aspects of SPSs would enable researchers, designers, and policymakers to identify the best suited Smart Parking System and understand the current trends in this sector. In order to overcome difficulties such as waiting in long queue and circling around the blocks to find a vacant parking space, in this paper we are proposing a unique system wherein people living in the vicinity of crowded streets can rent their unused parking space. Using this approach, especially malls can productively manage the parking problem effectively during rush hours. This system also provides convenience to customers by reducing the amount of time spent to find a vacant parking slot by allowing them to reserve a vacant slot through pre-book.

This research paper examines the relationship between mindfulness and satisfaction with life among employees. The objective of this study was to explore the influence of mindfulness on satisfaction with life, and to determine whether there is a significant correlation between these two constructs. The study employed a survey research design and collected data from a sample of employees working in various industries in India. Mindfulness was measured with the following test Mindful Attention Awareness Scale (MAAS) and satisfaction with life with the following test Satisfaction with Life Scale (SWLS). The results of the study indicate a significantly positive correlation between mindfulness and satisfaction with life among employees. The findings also suggest that mindfulness has a positive influence on satisfaction with life, indicating that employees who practice mindfulness tend to have higher levels of satisfaction with their lives. The study has important implications for organizations and employers who seek to improve the well-being of their employees. By promoting mindfulness practices, employers may be able to enhance the overall satisfaction with life of their employees. This, in turn, may lead to improved job performance, reduced absenteeism, and higher levels of engagement. Overall, the study highlights the importance of mindfulness as a tool for improving employee well-being and underscores the need for organizations to prioritize mindfulness practices in their workplace wellness programs. Further research is needed to explore the specific mechanisms by which mindfulness influences satisfaction with life among employees, as well as the long-term impact of mindfulness interventions on employee well-being

The smart irrigation system was developed to optimize water use of crops. The system has a distributed wireless network of soil-moisture temperature sensors placed in the root zone of the plants. Also, a gateway unit handles sensor information, triggers, actuators, and transmits data to the web application. The intelligence of The proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web- services and a web-based information visualization and decision support system provides real-time information insights based on the analysis of sensor dataand weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging

Plant identification is a significant undertaking however its care is considerably more significant. The identification of plants has a lot of benefits for wide range of people ranging from forestry services, pharmaceutical labs, government and common public. The research paper aims on developing a machine learning technology combined with mobile app development which is able to recognize plants and suggest good care for them like how much water, sunlight, fertilizer, etc is optimum for the plant. Plant identification with its care, based on leaf, is becoming an interesting task. Each plant leaf carries unique identity, which matches to no other plant, information that can be used in the identification and subsequently, determining measures for its care. A large number of features were taken from each leaf such as its length, area of hull, perimeter and color. All the work of research in this filed has further aggravated an interest in the development of automated systems for the recognition of different plant species. A full autonomous method of recognizing plants using Artificial Intelligence tools like Teachable Machine and TfLite. We gathered dataset from google images and several images with varying textures and lighting conditions clicked from my smartphone camera from the Kangra forest range. There are several applications that help in recognizing the plant species using features from their leaves but we are also adding an additional feature of its care.

The retail industry is becoming increasingly competitive, and businesses must keep up with the latest technological advancements to stay ahead of the curve. This research paper explores the benefits of a smart POS application integrated with cloud technology. The paper discusses the current state of the retail industry, the challenges facing retailers, and the potential benefits of using a smart POS application. The literature review of smart POS systems, the design and development process, and the outcomes and discussion of the application’s implementation are all included in this paper. Despite the potential benefits of digital point-of- sale (POS) systems, small businesses in India have been slow to adopt this technology. This paper aims to identify the factors that affect the adoption of digital POS systems in Indian small businesses. A survey was conducted with 167 small businesses across different industries in India. The results showed that factors such as cost, perceived usefulness, compatibility with existing systems, and complexity of the technology were the primary barriers to adoption. On the other hand, factors such as perceived ease of use, vendor support, and trial ability were identified as the primary drivers of adoption.

Forest fire vaticination refers to the process of using colorful ways and tools to read the liability and implicit inflexibility of a fire outbreak in a forested area. Forest fires are caused by a combination of factors similar as dry rainfall conditions, high temperatures, and mortal conditioning similar as conflagrations, cigarettes, and fireworks. There are several styles used in forest fire vaticination, including statistical analysis, machine literacy algorithms, and remote seeing ways. These styles help to gather and dissect data on rainfall conditions, energy humidity content, geomorphology, and other factors that contribute to the liability of a fire outbreak. Forest fire vaticination models can be used to give early warning systems to warn authorities and residers of implicit fire peril. These models also help to identify areas that are at high threat of backfires and enable authorities to take necessary preventives, similar as enforcing fire bans and evacuation orders, to help or minimize the impact of forest fires. Overall, forest fire vaticination plays a critical part in precluding and mollifying the damage caused by backfires. By furnishing accurate and timely information, it allows authorities to take visionary measures to reduce the threat of fire outbreaks and cover both mortal and natural coffers. In future predicting forest fire is expected to reduce the impact of fire. In this paper we are implementing the forest fire prediction system which predicts the probability of catching fire using meteorological parameters like position (latitude and longitude), temperature and more. we used Random Forest regression algorithm to implement this module.

The world including India have faced a lot of difficulties passing through Covid-19 waves not only once, but for several times which has also given a big threat to the world economy and human lives. Different waves have disrupted the lives and livelihood of the people. As per views of scientists the Covid-19 virus is something unique which has been changing its form from time to time. Recently, in March, 2023, it has come to light that Corona virus again has come in the name of “XBB.1.5” with 89.6% of affected cases. This small piece of study is based on the marginal class of people who are selling their products from door-to-door and small scale entrepreneurs. The main objective of the paper is to draw the attention of researchers and readers on the business threats and sufferings of small scale business personnel and daily sellers. The research is done on the basis of face-to-face interviews with their photos in order to share the experience the interviewees earned during different phases of Covid-19. This paper is concluded with a common view of those simple folk that this virus would never perish from this world, but we have to take timely precautionary measures to be protected from the virus.

The current storage mechanism currently stores user data on cloud servers. Thus, users give up their rights. You don't have control over the data and run the danger of privacy violations. Encryption technology is often the cornerstone of the traditional privacy protection strategies even if it is not very effective at thwarting assaults from within a cloud server because it cannot be fully analyzed from within. This paper's goal is to offer a fog computing-based three-layer storage structure for resolving the cloud storage issue. Frameworks like the one this study suggests can be utilized to fully utilize cloud storage while preserving user privacy. The Hash-Solomon coding technique also enables the segmentation of data. Then, to ensure privacy, a small amount of data can be stored on local computers and fog servers. The theoretical safety study and experimental assessment have demonstrated the feasibility of our proposal, which is a major advance over the existing cloud storage method. If one data component is missing, all the data information is lost. We shall demonstrate the effectiveness of the system as we secure it. Using this computational intelligence-based method, it is also possible to calculate the distribution percentage that is stored in clouds, fog, and local computers. Through theoretical safety analysis and experimental evaluation, our strategy— which represents a significant improvement over the current cloud storage strategy—has been demonstrated to be workable. If one data component is missing, all the data information is lost. Additionally, the distribution fraction saved can be determined thanks to the computational intelligence employed in this method.

As there is an increase in the usage of digital applications, the availability of data generated has increased to a tremendous scale. Data is an important component in almost every domain where research and analysis are required to solve problems. It is available in a structured or unstructured format. Therefore, to get corresponding data as per the application purpose, easily and quickly from different sources of data on the internet, an online content summarizer is desired. Summarizers make it easier for users to understand the content without reading it completely. Abstractive Text Summarizer helps in defining the content by considering the important words and helps in creating summaries that are in a human-readable format. The main aim is to make summaries in such a way that they should not lose their context. Various Neural Network models are employed along with other machine translation models to bring about a concise summary generation. This paper aims to highlight and study the existing contemporary models for abstractive text summarization and also to explore areas for further research

The paper describes about the dynamic electric vehicle charging system from solar. It reduces fuel and pollution. Nowadays electric vehicle are growing in numbers because of high rates of fuel Electric vehicle has now hit the road worldwide and are slowly growing in numbers. Also it is proven that electric vehicle helpful in reducing cost of travel and which is cheaper than fuel. So we have developed an electric charging system with unique ideas for solving charging system problem. In this electric vehicle no need of any wire, no need of external power supply, no need to stop vehicle for charging, we can charge EV in moving condition. In this system we use, battery, transformer, Atmega controller, LCD display, regulatory circuit, solar panel, coils of copper, AC to DC converters to develop the system more accurate. This whole system describes that how an electric vehicle can be charged in moving condition and also eliminating the need to stop the vehicle and charge it. Thus, insulating in the copper coil in the road, we can charge the electric vehicle in the moving condition.

The cloud computing is the architecture in which no central controller is present due to which various breaches occurred in the network. To secure data transmission from source to destination two type of encryption schemes i.e: fully homomorphism and fully disk encryption are introduced. The fully homomorhic encryption scheme is more security and light as compared to fully disk encryption. In the paper, improvement in the fully homomorphic encryption is proposed using elliptic curve cryptography and OTP generation.

Chatbots are computer programs that use text or voice-based interfaces to replicate human conversation. They are often used to automate mundane processes, provide customer support, or aid in the retrieval of information. Chatbots are built with a number of strategies that enable them to interpret and respond to user inputs in a more human-like manner. They can be employed in a variety of industries, including e-commerce, healthcare, and banking. We have analysed and compared numerous chatbot strategies in this report to establish the optimal way for our own chatbot project. We reviewed twenty-six papers on chatbot development and assessed the advantages and disadvantages of various strategies. Natural language processing techniques, such as tokenization and named entity recognition, have been shown in our research to be critical for interpreting user inputs. We also discovered that dialogue management methods, such as rule-based and machine learning-based approaches, have an important influence in influencing discussion flow. Furthermore, we discovered that natural language generation techniques, such as template-based and neural network-based methods, are critical in generating effective chatbot responses. We also investigated various services on the market in order to create a functional chatbot for our college. We also emphasized the various applications of chatbots as well as the current hurdles in the industry. Based on these findings, we chose a technique for our own chatbot project that employs advanced natural language processing and machine learning techniques to create more human-like conversations and improve overall user experience.

Our research paper presents an AI text summarization system that utilizes GPT, a powerful language model, to generate concise and meaningful summaries of lengthy text documents. The system consists of four modules: User, Android Application, GPT API, and GPT server. The User interacts with the system through the Android Application, which serves as the user interface. The GPT API acts as the intermediary between the Android Application and the GPT server, which hosts the GPT model and handles the text summarization process. The system employs state-of-the-art natural language processing techniques to generate summaries while preserving contextual coherence and salient information. The system's summarization capabilities are evaluated using metrics such as Rouge and F1 scores, demonstrating its effectiveness in capturing key information from different types of text documents. The system's integration with Android platforms provides convenient access for mobile users, making it suitable for applications such as news summarization, document summarization, and content curation. The modular architecture of the system allows for scalability and flexibility, enabling future enhancements and extensions. Our AI text summarization system utilizing GPT presents a promising approach for automatically summarizing large volumes of text, providing users with time-saving and meaningful summaries. The system has potential applications in various domains and can serve as a foundation for further research in the field of text summarization and natural language processing

This paper proposes low-power multiplier architectures based on Vedic mathematics, which is a set of ancient Indian techniques for performing arithmetic operations. The proposed architectures use the Urdhva-Tiryagbhyam sutra from Vedic mathematics, which enables the efficient multiplication of numbers with fewer partial products. The proposed architectures have been implemented and simulated using the 45-nm CMOS technology. The simulation results demonstrate that the proposed architectures achieve significant power savings compared to conventional multipliers while maintaining reasonable area and delay. Therefore, the proposed architectures are suitable for use in low-power and high-performance applications. The Vedic multiplier consists of several sub-modules, each of which performs a specific function in the multiplication process. These submodules include the partial product generator, the multiplier, and the adder. The partial product generator generates partial products based on the input numbers and the Vedic sutras. The multiplier module then combines these partial products to create the final product. Finally, the adder module adds the product to the previous state of the circuit to generate the final result.

The object detection based on deep learning is an important application in deep learning technology, which is characterized by its strong capability of feature learning and feature representation compared with the traditional object detection methods. The paper first makes an introduction of the classical methods in object detection and expounds the relation and difference between the classical methods and the deep learning methods in object detection. Then it introduces the emergence of the object detection methods based on deep learning and elaborates the most typical methods nowadays in the object detection via deep learning. In the statement of the methods, the paper focuses on the framework design and the working principle of the models and analyzes the model performance in the real-time and the accuracy of detection. Eventually, it discusses the challenges in the object detection based on deep learning and offers some solutions for reference.

YouTube is a popular social networking platform that allows users to upload, watch and comment on videos. However, YouTube also attracts spammers who post unwanted or abusive comments on videos, which can affect the quality and credibility of the content and platform. In this paper, we propose a novel approach to detect and delete YouTube spam comments using natural language processing (NLP) algorithms such as Rapid Fuzz and Levenshtein distance. Rapid Fuzz is a Python library that provides fast and accurate fuzzy string matching, while Levenshtein distance is a metric that measures the minimum number of edits required to transform one string into another. We use these algorithms to compare the similarity of comments and identify duplicates, which are a common form of spam. We also use the YouTube API to access and delete a channel’s spam comments. We evaluate our approach on comments of YouTube Video and show that it can effectively detect and delete spam comments with high accuracy and efficiency

Computer vision is related to object detection. Detecting instances of objects in images and videos is made possible by object detection. It recognizes the component of Pictures rather than conventional article recognition techniques and produces an keen comprehension of pictures very much like human vision works. In this paper, We restored starts the concise presentation of profound learning and item discovery system like Convolutional Brain Network (CNN), Repetitive brain network (RNN), quicker RNN, You just look once (Consequences be damned). After that, we concentrate on the modifications to our object detection architectures that we have proposed. In images, the conventional model can identify a small object. We have a few changes to the model. The method we propose yields the correct result precisely.

The criminal cases are increasing rapidly in our country due to which number of pending cases are also piling up. The continuous increase in the criminal cases is proving that it would be very difficult to be classified and to be solved. Recognizing the criminal activity patterns of a place is very important in order to prevent it from happening any other case. The crime solving agencies are doing a better work and can do a better work if they have a good knowledge of the pattern of criminal activities that are happening in a particular area or region. This problem can only be done by using machine learning any by using different algorithms to find the patterns of the criminal activities in a particular area or region. Data pre-processing is as important as final prediction and label encoding to clean and nourish the data. This research paper gives an efficient machine leaning model using different algorithms for predicting the next criminal case of the country