Volume 12 | Issue 5

 Abstract

This system makes use of sensors and ESP 32 micrpocontroller to identify cracks and obstacles on railway track and send the location of cracks if any to the railway authority through the app.This can be used to avoid railway accidents .This becomes automated .

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 Keywords

ESP 32, sensors , railway app, crack and obstacle detection

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My present project concern itself how the term perception enplane by Advaita. According to the Advaita theory of Perception, it is the Chaitanya within us that makes perception possible,. The Chetana (intelligence) within us unites with the chenta in the object, and result is perception. The following well-know illustration from the Vedanta paribhasa gives an account of the nature of perception: "as water from a tank may flow through a channel into a plot of land and assume in shape, so the radiant mind (taijasa-Antahkarana) goes out through the eye or any Vedanta paribhasa cites instances, of perception experience where no sense contact is involved 5, such as pleasure pain, other internal perceptions where moder of mind are directly apprecheded, further, it clearly states that the fact of the sense organ is not the

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Chaitanga, Sannikarsa, Upadhis, Upadhi, Ghatakasa, Kala, Asasa, Kula, Nirupadhika, Paricayaka, Vritti, Janta, Jneya, Advaita, Pramata.

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The growing computation power has made the deep learning algorithms so powerful that creating a indistinguishable human synthesized video popularly called as deep fakes have became very simple. Scenarios where these realistic face swapped deep fakes are used to create political distress, fake terrorism events, revenge porn, blackmail peoples are easily envisioned. In this work, we describe a new deep learning-based method that can effectively distinguish AI-generated fake videos from real videos. Our method is capable of automatically detecting the replacement and reenactment deep fakes. We are trying to use Artificial Intelligence(AI) to fight Artificial Intelligence(AI). Our system uses a Res-Next Convolution neural network to extract the frame- level features and these features and further used to train the Long Short Term Memory(LSTM) based Recurrent Neural Network(RNN) to classify whether the video is subject to any kind of manipulation or not, i.e whether the video is deep fake or real video. To emulate the real time scenarios and make the model perform better on real time data, we evaluate our method on large amount of balanced and mixed data-set prepared by mixing the various available data-set like Face-Forensic++[1], Deepfake detection challenge[2], and Celeb-DF[3]. We also show how our system can achieve competitive result using very simple and robust approach.

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RESNEXT and LSTM Architecture

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"Infospectrum" is a web-based platform designed to streamline the job search process for users. Leveraging advanced technologies such as Convolutional Neural Networks (CNN) and collaborative filtering, the platform analyzes user resumes to suggest the most suit able job roles and the corresponding skills to acquire. By parsing resume information and employing machine learning algorithms, Infospectrum aims to make the job search journey smoother for users by providing personalized recommendations. Developed using the Django framework and incorporating APIs like YouTube, the platform offers learning sources for recommended skills. With a dedicated team trained in TensorFlow, Keras, and Git, Infospectrum aspires to empower users with the necessary tools to secure fulfilling employment opportunities.

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 Keywords

Convolutional Neural Network, Collaborative filtering, User Resume Analysis, Job role recommendation, Personalized recommendations, Skill enhancement.

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 Abstract

Diabetic retinopathy (DR) is a severe complication of diabetes that damages the retina and can lead to vision loss. Early detection and treatment of DR are crucial to prevent blindness. This study proposes using deep learning techniques to automatically detect and classify DR severity from fundus images. The methodology involves collecting fundus images, pre-processing them, training convolutional neural network models, and evaluating model performance using common classification metrics. Various deep learning architectures such as VGGNet, ResNet, and DenseNet will be explored. The models will be trained on large datasets such as Kaggle Diabetic Retinopathy Detection dataset. If successful, the deep learning model can be integrated into screening systems to detect DR early and enable timely treatment. Te process has the capability of reducing vision impairment that is noticed in DR globally.

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VGGNet ,ResNet, and DenseNet

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Environmental degradation is probably the biggest problem that the human rice as a whole is facing. To live in a safe and clean environment is a fundamental right that every person should have. This essay examines the historical evolution of efforts to incorporate environmental protection into the international human rights framework. It traces the major conferences, conventions, and initiatives undertaken since the 1970s to mitigate environmental issues from a human rights perspective. While significant progress has been made, including the recent UN recognition of the human right to a healthy environment, critical gaps remain. It argues that addressing these interconnected challenges requires a holistic, inclusive approach. Ultimately, the essay argues that integrating environmental protection with human rights is paramount in upholding dignity, equality, and other such fundamental rights.

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 Keywords

Environment, Environmental degradation, human rights, climate change, climate refugees, environmental rights

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Approximation computing emerged as a key option for balancing the need for accuracy with the need to use as little energy as possible. Some programmes, like multimedia and signal processing, can handle errors and still process information correctly. This means that the accuracy level is lower than what is required at the circuit level, but the service quality is still good enough at the application level. Automatic recognition of R-peaks is the first and most important step that an electrocardiogram (ECG) signal must go through before it can be processed and analyzed. As its name suggests, the Haar discrete wavelet transform (HDWT) is a simple pre-processing filter that works great for finding electrocardiogram (ECG) R-peaks in embedded systems with very little power, like wrist tech. A study is used to show an approximate HDWT hardware architecture for ECG processing that is very good at using energy efficiently. Our best choice, which includes pruning in the roughly HDWT hardware design, only needs seven extra things to be added. In addition, this article looks at how to use a truncation method to make things use less energy. This is done by looking at how the signal-to-noise ratio changes over time and how that affects the ECG peak-detection programme in the end. According to the findings of this study, our HDWT approximation hardware architecture proposal is capable of bearing larger truncation levels than the HDWT that was first developed. Finally, our results show that using our HDWT matrix approximation idea along with pruning and the highest level of truncation still achieves an average R-peak recognition performance accuracy of 99.68%. This results in a decrease of approximately nine times the amount of energy that was previously consumed.

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Haar DWT, Energy-Efficient, ECG.

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The Concerns about synthetic chemicals in textiles are growing, and this, together with an increase in skin allergies and infections, highlights the need for safer and more environmentally friendly textile finishing options. However the study aspects into the use of extracts from Psidium guajava plants as functional finishes on cotton fabrics to improve their dermatological qualities and add antifungal and antibacterial capabilities. Selected guava leaves, known for their skin-friendly qualities, were processed using the Soxhlet extraction method to yield strong extracts that were then used as finishes on cotton textile materials. The guava leaves extracts shows low toxicity levels, biodegradability, and environmentally gentle profile make them attractive green substitutes for synthetic chemical agents that are frequently utilized in textile industry. The study was to determine the fabric property and change due to the finishing process. The Antibacterial activity of the coated materials were analyzed with Klebisiella pneumonia, Streptococcus pneumonia, Staphylococcus aureus and Pseudomonas aeruginosa was within an acceptable level.

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Textile finishing, Cotton, Psidium guajava, extract, Soxhelet, Antibacterial, Physical properties.

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 Abstract

Power generation is one of the issues. Now -a-days number of power sources are present, non-renewable and renewable, but still we cannot overcome our power needs. Among these human population is one of the resources. In this project we are generating power by running or walking. Power can be generated by walking upon stairs. This system can be installed in homes, schools, colleges, where the people move around the clock. When the people walk on the steps or that of platform, power is generated by using weight of person. This mechanical energy applied on the crystal into electrical energy. When there is some vibrations, stress or straining force exert by foot on flat platform

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Renewable Energy power, Electrical Energy, Mechanical Energy, Foot Step

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The RFID-based smart attendance system is a creative method of automating and streamlining the tracking and monitoring of attendance in commercial and educational contexts. RFID (Radio Frequency Identification) technology is used by the system. RFID scanners are positioned at strategic entry points, and RFID tags are assigned to particular persons. When an individual passes the reader with a valid RFID tag, their attendance is automatically entered into a central database. This approach significantly reduces the amount of manual effort required to track attendance while minimizing errors and enhancing data security. Administrators may monitor attendance trends and generate comprehensive reports using its real-time data analytics and reporting features. The integration of RFID technology enables adaptability and adaptation to satisfy various organizational needs, hence permitting additional features such as access control and time management. Since an RFID-based attendance system promotes more efficiency, enhanced accountability, and fewer administrative tasks, it is a valuable tool for modern educational institutions and enterprises seeking to raise operational efficacy and production.

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Smart Attendance, Advanced Technology, RFID, Tag, Efficient, Administrative Operations

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Wine Quality, Machine Learning, Random Forest, Prediction, Chemical Properties

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Drug misuse in young people is a serious issue that has a big impact on their health and wellbeing. This research study tells how drugs affect youngsters using machine learning approaches. To better understand the fundamental causes of young drug abuse, we will analyze big datasets that include data on drug consumption patterns, demographics. We want to know which risk factors are associated with drug use so that we may develop predictive models to aid in early detection and treatment. We will be able to offer insights on preventive measures and support networks by analysing a variety of characteristics, like age, gender, socioeconomic status, peer pressure, and mental health. This research contributes to the continuous efforts to improve youngster's health and wellbeing and to battle drug addiction in youth as well as society.

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Drug consumption, youth, machine learning, predictive models, and prevention of substance misuse.

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Understanding weather conditions plays a pivotal role in our daily lives, influencing a wide range of decisions from outdoor activities to disaster preparedness. Effective weather visualization not only aids in comprehending complex meteorological phenomena but also enhances our ability to adapt to changing environmental conditions. Augmented Reality (AR) technology has transformed how users perceive and interact with information in the environment. We aim to present the weather conditions more descriptively and interactively using AR. In this paper, we present a novel application of AR for visualizing real-time weather conditions at the user's location. The live weather data from OpenWeatherMap API is integrated into Unity 3D using C# script. The AR scene dynamically adapts to changing weather conditions, providing users with an immersive experience.

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Augmented Reality, Weather Visualization, Open Weather Map API, Unity 3D, C# script

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The project focuses on developing an automated bird species identification system using audio signal processing and machine learning techniques. By harnessing a dataset of bird vocalizations, the project aims to train a model capable of accurately classifying bird species based on their vocalizations. This involves extracting relevant features from audio recordings, such as Mel-Frequency Cepstral Coefficients (MFCCs), to represent the unique characteristics of each bird species' vocalizations. These features are then used to train an Artificial Neural Network (ANN) model, enabling it to learn and recognize patterns in the audio data. Once trained, the model is integrated into a web application interface, allowing users to upload audio recordings and receive predictions of the bird species present. This system provides a practical tool for researchers and conservationists to quickly and accurately identify bird species in the field, aiding in ecological research and conservation efforts.

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Automated bird species identification, audio signal processing, machine learning, Mel-Frequency Cepstral Coefficients (MFCCs), Artificial Neural Network (ANN), web application interface, conservation, ecological research, bird vocalizations, dataset, classification, pattern recognition, researchers, conservationists.

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The pharmaceutical sector is not an exception to how quickly artificial intelligence (AI) is changing other industries. AI is being used more and more in the pharmaceutical sector to automate, improve, and personalize processes ranging from medication administration to drug development. This review focus on how AI is being used in drug discovery, Health care system, etc. We will also talk about the benefits and drawbacks of artificial intelligence in the pharmacy sector. The new AI pharmacy system automates repetitive operations, offers individualized treatment plans, lowers costs, and improves patient outcomes while replacing the outdated manual processes and human decision-making of the previous pharmacy system. But it's crucial to make sure AI is applied morally and sensibly and that its effects on society and the workforce are properly taken into account. The primary advantage of incorporating artificial intelligence (AI) into certain pharmacy applications is increased precision and effectiveness in patient treatment. All things considered, this paper will provide some insight into the pharmacy industry's future and the revolutionary potential of artificial intelligence in this sector.

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Artificial Intelligence; Future of Pharmacy; Milestones in AI; Drug Discovery; Health Care System; Advantages and Disadvantages of AI.

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This paper presents a novel approach for cancer prediction leveraging machine learning techniques integrated with a conversational chatbot interface. The proposed system harnesses the power of machine learning algorithms to analyze diverse patient data including genetic information, medical history, lifestyle factors, and demographic details to predict the likelihood of developing various types of cancer. Moreover, the integration of a chatbot interface enables seamless interaction between the system and users, facilitating personalized consultations, symptom assessments, and risk factor evaluations. By combining advanced predictive analytics with intuitive user interaction, this system aims to enhance early detection and preventive measures for cancer, ultimately improving patient outcomes and healthcare efficiency.

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EfficientNet, cancer classification, brain tumors, lung cancer, high accuracy, precision, recall, F1 scores, state-of-the-art, clinical practice, early detection, treatment outcomes.

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A vital component of the human body, hair shields the scalp. After shampooing, hair conditioner is a hair care product that is applied to the hair and hair tips to condition the hair before being rinsed out. Hair conditioner is used to make hair easier to manage and seem more glossy. Its primary goal is to lessen friction between hair strands to make combing and brushing simpler. The primary goal is to create the hair care product that is most successful in meeting people's needs and to assess the finished product to determine its intended impact on the user. The majority of hair repair and conditioning solutions on the market today work by coating hair fibber's with intricate formulas made of macromolecule and surfactant mixes. This causes the damaged portions in the outermost region of the capillary fibbers to partially cover. This reduces friction between the fibber's, making them easier to manage and more hydrated. A thorough analysis of the various physicochemical factors connected to the conditioning process, such as the deposits' thickness, water content, topography, or frictional qualities, is necessary for optimizing shampoo and conditioner formulations. The several physicochemical factors that affect our comprehension of the most basic underpinnings of the conditioning process are covered in this review. Herbal conditioners contain fenugreek, curry leaves, and mint leaves as their key constituents. Based on physicochemical parameters like stability studies and efficiency, it was assessed and examined. Changing customer expectations and emphasizing safety and efficacy would be part of a more radical strategy to popularize herbal conditioners. Producing herbal hair conditioners is the goal. In comparison to synthetic conditioners, all herbal conditioners showed equivalent solids percentage, high viscosity, steady lather, pH within the allowed range, and good wetting qualities.

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Herbal Conditioner, Fenugreek, Nourishing , Efficiency.

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 Abstract

Agriculture is the most important sector of Indian Economy. Indian agriculture sector accounts for 18 percent of India's GDP and provides employment to 50% of the country's workforce. But latest studies have shown a steady decline in the contribution made by agriculture to the Indian economy although it is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India. This project explores the integration of Internet of Things (IoT) and Artificial Intelligence (AI) with a focus on Machine Learning (ML) to implement smart cultivation practices for Carnation Flowers. Through the deployment of sensor networks and IoT devices, real-time data on environmental conditions, soil moisture, and plant health are collected. The collected data is then processed using advanced AI and ML algorithms to provide predictive analytics, enabling precision cultivation strategies and proactive disease management. The synergy of IoT and AIML in Carnation Flower cultivation offers an intelligent and automated framework that enhances yield quality, optimizes resource utilization, and promotes sustainable and efficient farming practices.

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 Keywords

IOT, ML, Agriculture, Carnation cultivation, CNN, python.

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The advantages of yoga asanas and exercise for physical and mental health are available to people of all ages. To prevent any damage to the bones, muscles, and ligaments, yoga and other gym exercises, postures must be executed correctly, especially when done without an instructor. So, even without a human instructor, the use of artificial intelligence and machine learning combined with picture processing will aid to provide feedback to the performance. With the help of Tensorflow, MoveNet and MediaPipe, this study provides a technique for real-time posture estimation that identifies pose issues and lets users fix them.

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Artificial Intelligence, Machine Learning, CNN, Tensorflow, MediaPipe, MoveNet, yoga, gym.

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ABSTRACT: Smart health is a new revolution in health industry where information and technology are integrated to improve patient outcomes. The goal of smart health care system is to integrate innovative technologies such as the Internet of Health Things (IoHT), medical Cyber-Physical Systems (medical CPS), health cloud, health fog, big data analytics, machine learning, blockchain, and smart algorithms to deliver improved, value-added and cost-effective healthcare services and enhance the effectiveness and efficiency. The Internet of Things (IoT) can be described as network of physical objects that are embedded with sensors, software, and other technologies that interact and communicate with each other. The branch of IoT termed as Healthcare Internet of Things (H-IoT) is dedicated towards medical science for rapid automation of the healthcare sector. Due to the large amount of complex heterogenous data involved in healthcare there is need for the integration of machine learning (ML) algorithms into IoT to enhance the performance of system.

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 Keywords

IoT, IoHT, Machine Learning, Smart Health, sensors.

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