Volume-9, Issue-5

September-October, 2023

Research Paper

1. Liver disease detection using Machine Learning techniques

Liver disease is the leading reason of death worldwide, The liver is responsible for metabolic, strength-storing, and waste-filtering functioning in your body. The aim of this study is to develop a machine learning-based technique for liver disease prediction in people. This study on liver disease detection models is meant to determine the best techniques for selecting and synthesizing the many studies of high quality. The majority of health data is nonlinear, correlation-structured, and complex, making it complex to evaluate. The use of ML-based techniques in healthcare has been ruled out. This work uses various machine learning algorithms like decision trees, Naïve Bayes, SVM, Random Forest, CatBoost, and Soft Voting Classifier on the Indian Liver patient dataset to predict liver disease. The research work gives the correct or maximum accuracy model showing that the model is able to predict liver diseases effectively. Our end result shows that the Voting classifier attains higher accuracy as compared to other machine-learning models

Published by: Taranpreet Kaur, Dr.Vinay ChopraResearch Area: Machine Learning

Organisation: DAV Institute of Engineering and Technology, Jalandhar, PunjabKeywords: Liver Disease, SVM, NB, Random Forest, Catboost, Soft Voting Classifier

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Research Paper

2. Improving performance in Central Sterile Supply Department(CSSD) management through performance measurements utilizing user satisfaction surveys and intervention.

Surgical procedures leading to surgical site infections, and medical procedures associated with iatrogenic infections can be made negligible with sterilization and decontamination of instruments and medical devices The Central Sterile Services Department (CSSD) is the hub in the hospital working to provide sterile instruments and packs to all areas of the hospital. The quality of services provided by the CSSD Department in AIIMS Bibinagar regarding the processing as well as the service was to be assessed by all the user departments. The questionnaire was devised after discussion and distributed to the end-user personnel. There were 19 questions addressing the timing, etiquette, equipment, damages, delays, validation as well as feedback regarding improvement. There were a total of 107 respondents of whom 87 were nursing officers and 25 were doctors from medical, surgical, obstetric, orthopedic, ENT, and ophthalmological specialties. The data collected was collated in Microsoft Excel and descriptive analysis was done. The results showed that more than 90% were satisfied with the validation process, the packing of items, the etiquette of the personnel, and the cleanliness of the equipment. Critical responses to consider for intervention included a lack of awareness regarding the indicators and the validation of the sterilization process, nearly 68% lacked knowledge of this. Further, there was dissatisfaction regarding updates and any recent innovations in the system. Only 62% were overall satisfied with the CSSD service, with 11% not sure as to what can be improved. Interventions to improve the services to address the issues raised from the feedback were created utilizing the available resources

Published by: Dr. S. Kalyani Surya Dhana Lakshmi, Dr. A. Bhargav Ram, Rahul K.Research Area: Hospital Administration

Organisation: All India Institute of Medical Sciences, Bibinagar, TelanganaKeywords: Central Sterile Services Department, Quality Of Services, Feedback , Questionnaire, User Satisfaction

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Review Paper

3. Analyzing the potential role of Artificial Intelligence in overcoming limitations of robots in space exploration

Robotic systems are an integral part of space exploration missions. These robots can explore and navigate extraterrestrial terrain and can survive in the harsh environments of space. Autonomous navigation and mapping capabilities are just some of the components that are essential for us to efficiently traverse and gather scientific data. This research paper aims to explore the application of artificial intelligence (AI) techniques in enhancing the exploration efficiency of planetary robots. The paper also discusses the limitations of robots in space exploration and outlines how AI can be used to overcome them.

Published by: Siddharth KannanResearch Area: Aerospace Engineering

Organisation: Prabhavati Padamshi Soni International Junior College, Mumbai, MaharashtraKeywords: AI, Space Exploration, Algorithms, Autonomous Navigation

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Research Paper

4. Evaluation of limestone resources in parts of Palnad basin, Nalgonda district, Telangana

Palnad Basin is the storehouse of limestone which is equivalent to the Narji limestones of the Kurnool Basin. Krishna stream cut over the Palnad Basin generally is EW course and limestones uncovered on either side. Palnad Basin covers parts of Nalgonda district of Telangana and the Krishna & Guntur districts of Andhra Pradesh. The Palnad limestones are lithologically similar to the Narji limestones of the Kurnool basin (Madhusudhan Rao, 1964). (Madhusudhan Rao, 1964). They are well exposed on either side of the Krishna River to the North of river Krishna in Nalgonda, to the South of river Krishna in Guntur district of Andhra Pradesh, and to the East of river Krishna in Krishna district of Andhra Pradesh. Nalgonda district is located in the southern part of Telangana state in India and encompasses an area of roughly 14,240 square kilometers. The district has abundant natural resources, including limestone, and quartz. The geology of Nalgonda district is diversified, with rocks ranging in age from the Archean to the Quaternary. The district is separated into four major geological units: the Archean Gneissic Complex, the Proterozoic Cuddapah Supergroup, the Mesozoic Deccan Traps, and the Recent Alluvium. Limestones display a considerable range in hue viz., purple, green, pale green, chocolate, buff, dark grey, and light grey. The limestones are fine-grained with shallow dips across the whole basin, but rolling dips are not unusual. Limestone resources evaluated in

Published by: Raju Macharla, A. Narsing RaoResearch Area: Geology

Organisation: Osmania University, Hyderabad, TelanganaKeywords: Palnad Basin, Limestones, Nalgonda, Resource Evaluation,

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Research Paper

5. Vehicle Diagnostics Systems and Intelligent Failure Prediction

The automotive industry's rapid growth has led to increased vehicle numbers and subsequently higher failure rates. Conventional diagnostics react to failures, lacking preventive capabilities. Current On-Board Diagnostics are no longer sufficient, necessitating upgrades. To address this, I propose implementing Internet of Things (IoT) devices and Deep Learning Models to predict failures in advance, saving costs and avoiding mishaps. These models utilize historical data and vehicle tests to establish threshold values, triggering warnings if the system detects potential failures. The system comprises an OBD device sending data to a remote server, which updates a dashboard with real-time failure alerts.

Published by: Suyash PustakeResearch Area: Computer Science

Organisation: AISSMS College of Engineering, Pune, MaharashtraKeywords: Vehicle Diagnostics, Sensor Fusion, Predictive Analytics, Deep Learning

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Research Paper

6. Utilizing LSTM neural networks for sentiment analysis of tweets

Deep Neural Networks are considered as one of the most powerful machine learning methods of recent times. Recurrent neural networks, including LSTM variations, exhibit exceptional performance in sequence-oriented assignments, while also falling within the domain of autoregressive models, wherein forecasts are tied to the historical input context. In this paper, we experiment with LSTM for Twitter sentiment analysis. Leveraging advances in Natural Language Processing (NLP), we show the efficacy of our algorithm with extremely competitive results.

Published by: Manan GangwaniResearch Area: Natural Language Processing/Machine Learning

Organisation: Podar International School, Maharashtra, MumbaiKeywords: Neural Networks Long-short term memory Sentiment Analysis

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Research Paper

7. Free-body modal analysis of a Baja SAE vehicle chassis

With the growing need to design increasingly efficient and complex systems, engineering studies are increasingly resorting to computer simulation techniques to analyze the performance and behavior of physical systems. These simulations help to reduce the time and cost of developing new projects. The aim of this article was to carry out a free-body modal simulation of the chassis of a Baja SAE off-road mini vehicle, using the finite element method. The study used Solidworks software to generate the 3D model of the chassis and Ansys software to carry out the simulations. At the end of the simulations, it was possible to see that the chassis structure has natural frequencies between 36 and 86 Hertz (Hz) when the structure is free, which are different from the frequencies of the main source of forced vibration in the structure. In this way, it can be concluded that the structure does not enter the resonance phenomenon, meeting the design assumptions.

Published by: Leandro de Paula Freire, Luiz Augusto Ferreira de Campos VianaResearch Area: Mechanical Engineering

Organisation: Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerais, Arcos - MG, 35588-000, BrazilKeywords: Finite Elements, Modal Analysis, Mechanical Vibrations, Baja Sae, Chassis

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Research Paper

8. Periodicity of the Probability Distribution of a Particle in a Box

We consider a particle in a two-dimensional infinite potential square well in states that are superpositions of either two or three energy eigenstates. These have probability distributions that are periodic in time. We compute the periods in both cases and simulate the time dependence of the probability distributions.

Published by: Jettae SchroffResearch Area: Quantum Mechanics

Organisation: Cambridge Centre for International Research, Cambridge, United KingdomKeywords: Physics, Quantum Mechanics, Probability Distributions, Eigenstates, Simulation, Particle In A Box

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Research Paper

9. Smart Water Management

The shortage of water supplies has emerged as a pressing worldwide issue in a world that must contend with the twin problems of a growing population and climate change. The need for effective water management has grown, and it is all too easy to see the results of carelessness and human mistake in managing water resources. Artificial Intelligence (AI), however, is a promising solution in the realm of computer science. A developing area of computer science called artificial intelligence has the power to completely alter how we manage our water resources. Computers, as opposed to people, are known for their accuracy and dependability. Utilizing AI in water management could not only correct past mistakes but also save millions of liters of water each year, thereby helping the world's population, which is always expanding. At its foundation, smart water management comprises effectively managing water resources with the least amount of human involvement. Data-driven "intelligent" applications have already revolutionized many elements of our daily lives in the digital age. Water utilities that are forward-thinking can greatly improve their operational performance by using this digital technology revolution. For water utilities starting their journey toward digital transformation, this abstract offers an introduction to the core AI ideas. It puts a strong emphasis on streamlining water distribution processes and dealing with the urgent problem of unaccounted-for water. Water utilities may use a wealth of data and information to improve service delivery, lower operating costs, and make better decisions by utilizing the power of AI algorithms and big data analytics. This succinct review describes the wide-ranging uses of big data analytics and AI-related algorithms in the water supply industry. It also explores how water utilities might use AI to predict and reduce unaccounted-for water, a problem that persists in the industry. Finally, actionable suggestions for implementing AI are offered, along with first cost projections.

Published by: Pranav PradhanResearch Area: Computer Science

Organisation: Pune Vidyarthi Griha's College of Engineering and Technology, Pune, MaharashtraKeywords: Artificial Intelligence, Water Management, Hydraulic Modelling 1.0, Hydraulic Modelling 2.0, Big Data.

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Online paper publication is ongoing for the current issue and authors can submit their paper for this issue until Ongoing Submissions.