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IJIRTM: Volume-7, Issue-3, 2023

Paper Title : Stock Price Forecast Using Long Short Term Memory (LSTM) Algorithm
Author Name : Kunal Panthi, Dr.Vineet Richhariya, Dr.Sadhna K. Mishra
Keywords : Stock Market, Machine Learning, Predictions, Classification, forecasting, Data mining, Google Stock Forecasting, Logistic Regression (LR), LSTM.
Abstract :

One of the most significant activities in the world of finance is stock trading. Trying to anticipate the future value of a stock or other financial instrument traded on a financial exchange is known as stock market prediction. This describes how machine learning was used to predict a stock. The majority of stockbrokers employ technical, fundamental, or time series analysis when making stock predictions. In this article, we suggest a machine learning (ML) strategy that will be trained using the stock market data that is currently available to gather intelligence before using the learned information to make an accurate prediction. We used ML algorithms like LR and LSTM in these, and the summary of model performance parameters shows that LSTM outperforms LR Model for the datasets.

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Paper Title : First Steps: Stepping Towards Recovery for Bed-Bound Patients
Author Name : Capt. Usha Banerjee, Ms.D. Maryline Flinsi
Keywords : Ambulation, Bed bound patients, Bedridden patients, First step, Steps corner.
Abstract :

Ambulation is crucial as it sets the path to recovery. It is about setting the patient to become more independent. It helps to prevent post-operative / procedure and bed bound complications and has large range of benefits for the patients - stimulates circulation, promotes flow of oxygen throughout the body, helps to maintain normal breathing function, prevents formation of blood clots, increases muscle tone & strength, aids in joint flexibility, relieves constipation, and improves self esteem. Group Director Nursing of Apollo Hospitals Group, has developed a campaign titled “first steps” to celebrate the moment when the patient start ambulation after a long duration of being bed bound due to a surgery/ procedure/ prolonged hospitalization. This campaign was conducted in all Apollo units across the country .In the campaign the units made a beautiful First step corner in the corridors and labeled the area as “The Steps Corner” & decorated it with quotes or tagline to motivate & cheer the patients. When the patient is ambulated, the nurse must walk along till this area slowly and progressively based on the patient’s condition – target this as a mile stone when they are able to walk till there. The patients were made to write something on the board/ pin their comments every day and let it be there for a long while to motivate the other patients. The units which decorate the “First Steps” corner most creatively & records maximum number of tender loving care moments related to first steps was awarded at the end of the month.

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Paper Title : Genuine Information Testbed for 5G/B5G Shrewd Organization as a Review
Author Name : Pramod Kumar Chaudhary, Dr.Neetesh Raghuwanshi
Keywords : True-data testbed, Wireless communication networks, Artificial intelligence (AI), Big data, Internet of everything (IoE).
Abstract :

Future past fifth-age (B5G) and 6th era (6G) versatile correspondences will move from working with relational correspondences to supporting web of everything (IoE), where astute interchanges with full reconciliation of enormous information and computerized reasoning (simulated intelligence) will assume a significant part in further developing organization effectiveness and offering top notch assistance. As a fast developing worldview, the simulated intelligence enabled portable correspondences request a lot of information procured from genuine organization climate for methodical test and check. Thus, we construct the world's most memorable genuine information testbed for 5G/B5G smart organization (TTIN), which involves 5G/B5G on location trial organizations, information obtaining and information distribution center, and computer based intelligence motor and organization streamlining. In the TTIN, genuine organization information procurement, capacity, normalization, and examination are accessible, which empower framework level web-based check of B5G/6G-orientated key advancements and backing information driven network enhancement through the shut circle control system. This paper expounds on the framework engineering and module plan of TTIN. Itemized specialized details and a portion of the laid out use cases are likewise displayed.

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Paper Title : Improved the Proficiency of Energy for Device To Device Correspondence Underlying Cellular 5G Network
Author Name : Pramod Kumar Chaudhary, Dr.Neetesh Raghuwanshi
Keywords : D2D Communication, cellular network, power optimization, PSO, Interference, frequency reuse.
Abstract :

Gadget to-Gadget (G2G) correspondence has drawn in loads of consideration as one of the most progressive remote correspondence advances which permits admittance to administrations presented by neighbouring gadgets bypassing the Base Station (BS). The likely benefits of this immediate correspondence worldview incorporate high information rate, network offloading and range augmentation, as well as business vicinity administrations and person to person communication. Since D2D correspondence is conceived as short-range direct correspondence between adjacent clients, it is likewise vital to display the D2D-empowered cell networks as various locales rather than fixed districts. The thought of fixed areas permits demonstrating of the area subordinate execution of clients. In such manner it is a profoundly moving open issue to logically research the intra-cell impedance in a D2D-empowered cell organization and the presentation of underlay D2D correspondence when the clients are restricted in a limited locale.

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Paper Title : Detection of Cyber Attacks Using Machine Learning Techniques
Author Name : Albert.N.Rejy, Dr.Sadhna K. Mishra
Keywords : Cyber security, Malware detection, Machine learning, cyber threat intelligence. Cyber- attacks.
Abstract :

Professionals in cyber security give risk assessment more consideration and provide ways to mitigate it. Designing effective methods was a goal established for the field of cyber defense. Despite its success in cyber defense, machine learning has also grown to be a significant worry for data privacy. Unprecedented advancements in computing, storage, and computational technologies have led to the fast growth of cloud computing, networking, and evolutionary computation. There is an increasing need for comprehensive and sophisticated information security and privacy problems as the world rapidly digitizes. Additionally, there are increasingly complex strategies for defending against security threats. Worldwide cyber terrorism is growing thanks to various computer flaws. Global computer security issues including malware detection, ransom ware recognition, fraud detection, and spoofing identification were addressed using machine learning techniques. The study examines the use of cyber training to both offensive and defense, offering information on cyber risks based on machine learning techniques and Machine learning methods that describe how machine learning is used for computer defense, such as the discovery and avoidance of attacks, vulnerability scanning and recognition, and public internet risk assessment, are used to analyze the far more prevalent types of cyber security concerns.

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Paper Title : Solving Enneagonal Transportation Problem by Various Mechanism in Pythagorean Fuzzy Territory
Author Name : R Rajani, G. Charles Rabinson
Keywords : Pythagorean Transportation problems, Enneagonal fuzzy numbers, ranking method, Centroid Ranking Technique, proposed ranking method, Initial Basic Feasible Solution, Optimal Solution.
Abstract :

The Pythagorean Enneagonal Transportation Problem (PEGTP) is used to determine the quantity and need of goods transported from one source to various destinations. In this paper, the Enneagonal Pythagorean Fuzzy Numbers are used to find the Transportation Problem utilizing the proposed ranking method and Centroid Ranking Technique. By adopting the proposed ranking method under the score function method, the cost of transportation can be reduced. A numerical example is used to demonstrate the process.

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Paper Title : Study on Public Health Transportation Using Triangular Neutrosophic Fuzzy Environment
Author Name : Alphonse Arul Rajan V. S, G. Charles Rabinson
Keywords : Transportation Problem, Neutrosophic Fuzzy number, Neutrosophic Fuzzy Transportation problem.
Abstract :

Transportation is a very essential tool to transport the particles/products from one place (origin) to another place (destination) with minimum cost or minimum time. Nowadays we come across so many problems (unexpected situations) like traffic, breakdown, etc. when we transport our particles/products from one place (origin) to another place (destination). Our wish to rectify this kind of unexpected situations with help of Neutrosophic Fuzzy Concept. We are going to justify this idea with some numerical examples.

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Paper Title : Cyber Attack Detection in A Network Using Machine Learning
Author Name : Albert.N.Rejy, Dr.Sadhana K Mishra
Keywords : Cyber-crime, Machine learning, Cyber-security, Intrusion detection system.
Abstract :

Everywhere, cybercrime is on the rise and takes advantage of various computer environment weaknesses. Ethical hackers place more emphasis on identifying vulnerabilities and suggesting methods for mitigating them. In the subject of cyber security, there has been a pressing need for the creation of efficient methods. The majority of IDS approaches now in use are unable to handle the dynamic and intricate nature of cyber attacks on computer networks. Due to machine learning's success in solving problems related to cyber security, it has lately become a topic of significant relevance. For the most difficult problems in cyber security, such as intrusion detection, malware classification and detection, spam detection, and phishing detection, machine learning approaches have been used. Machine learning may identify cyber security risks more effectively than other software-oriented approaches, which lessens the workload on security analysts even if it cannot automate a full cyber security system. As a consequence, effective adaptive approaches, such as various machine learning techniques, can lead to increased detection rates, decreased false alarm rates, and reasonable computation and transmission costs. Our main goal is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively.

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Paper Title : Early Initiation of Breast Feeding Among Primigravida Mothers in selected Hospitals of Bhopal City, Madhya Pradesh
Author Name : Ms.Ragni Dubey
Keywords : Breast Feeding, Medical Sector, Nursing.
Abstract :

Background: Breast feeding, the most natural way of infant feeding to satisfy nutritional, metabolic and psychological needs of the baby. Breastfeeding provides frequent, close physical contact and helps mother and baby to become better acquainted. Antibodies from the mother are passed through the milk to breastfed babies and provide greater resistance to various infections. Methods: The conceptual framework adopted for the study was based on modified Pender’s health promotion model .The study utilized a descriptive survey research design for the study. The data was collected from Sultania Janana hospital, Bhopal Madhya Pradesh using Non- probability purposive sampling technique. The sample consisted of 60 primigravida mother from the hospital to assess the knowledge regarding early initiation of breast feeding among primigravida mother with selected socio-demographic variables The tool use for generating data was a structured interview schedule, which was also translated to Hindi and distributed for enhancing the knowledge of the primigravida mother regarding early initiation of breast feeding. Result: Majority, 41(68.3%) primigravida mothers had adequate, 18(30%) primigravida mothers had moderate knowledge and 1(1.7%) primigravida mother had inadequate knowledge regarding early initiation of breast feeding. The mean knowledge score was 18 and the SD 4.87. Hence, the calculation statistically proves that the research hypothesis H1 is accepted null hypothesis is rejected at 0.05 level of significance. Based on the statistical analysis it was found that qualification of mother, dietary pattern, Occupation of mother, present history of any medical disorder, present weeks of pregnancy and discarding the first milk had no significant association with knowledge regarding early initiation of breast feeding among primigravida mothers. Hence the research hypothesis H2- there is a significant association between the level of knowledge regarding early initiation of breast feeding among primigravida mothers with selected socio demographic variable is rejected at 0.05 level of significance. Conclusion: There is need to impart more knowledge among primigravida mothers about nutrition in pregnancy. More attention should be paid on nutritional guidance among primigravida mother especially in young and less educated women for healthy outcome of mother and babies. This study emphasizes the importance of dietary counseling by attending doctors, nurse, and dietitians as an integral part of postnatal care. The study also has implication in the field of nursing education, nursing administrations nursing practice and nursing research.

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Paper Title : Machine Learning Based Disease Classification: A State-ofthe- Art Survey
Author Name : Rahul Kumar, Prof.Chetan Agrawal, Prof.Prachi Tiwari
Keywords : Machine Learning, Classification, Disease Classification, SVM, ANN.
Abstract :

Huge volumes of data are frequently handled in the medical industry. Results may be impacted if large amounts of data are handled using traditional ways. In specifically, for disease prediction, machine learning algorithms can be utilized to gather data for medical research. For the evaluation of patient medications and specialists, early disease detection is essential. The diagnosis of many diseases is done using machine learning algorithms including decision trees, support vector machines, multilayer perceptron, Bayes classifiers, K-Nearest Neighbors ensemble classifier techniques, etc. The use of machine learning algorithms can result in quick and accurate disease prediction. The application of machine learning approaches to forecast various diseases and their types is examined in this research article. This study looked at studies that largely dealt with the prediction of diabetes, heart disease, breast cancer, chronic kidney disease, and machine learning. The hybrid strategy that improves the performance of individual classifiers is also examined in the paper.

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Paper Title : Evaluation of Gi-Fi Technology for Short-Range, High-Rate Wireless Communication
Author Name : Dr.B.K. Verma, Dr.Shashi Bhushan
Keywords : Gi-Fi, Gigabit wireless; Wi-Fi, Wireless technology; Bluetooth.
Abstract :

GiFi is a wireless transmission system which is ten times faster than Wi-Fi and it’s chip delivers short-range multi-gigabit data transfer in an indoor environment. In this paper this technology is defined in detail. Also a comparison is perform between Gi-Fi and some of existing technologies and it shows that this gigabit wireless technology has an edge over current technologies due to its high speed data transfer rate that is 5 Gbps, low power consumption and low frequency interference. The benefits and features of this new technology are explained and the applications for that are prescribed that can be helpful for use in development of the next generation of devices and places. By providing low-cost, high broadband access, with very high speed large files swapped within seconds it is expected that Gi-Fi to be the preferred wireless technology used in home and office of future. In this paper future of gigabit wireless technology also is traced.

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