Recent advancements in cancer diagnosis using machine learning techniques: a systematic review of decades of research, comparisons, and problems

Cancer is a non-communicable disease that spreads throughout the body through uncontrolled cell growth. The malignant cell grows into a tumor, which weakens the immune system and disrupts other biological processes. The most frequent types of cancer are breast, lung, and cervical cancer. Several screening methods are available to detect the presence of cancer at various stages. Misdiagnosis can occur in some circumstances owing to human mistakes or incorrect data interpretation, resulting in the loss of human lives. To address these issues, this research study proposes an effective machine learning-based review and diagnosis technique backed by intelligence learning models. Artificial intelligence-based feature selection and classification techniques are used to detect cancer at an earlier stage, improve prediction accuracy, and save lives. In this research study, breast, cervical, and lung cancer datasets from the University of California, Irvine repository was used in these experimental investigations. To train and validate the optimal features minimized by the proposed system, the authors used supervised machine learning approaches. There could be numerous features that may contribute to the occurrence of cancer, it is difficult to pinpoint the specific environmental and other diagnostic features that contribute to it, but it still plays a role in determining cancer occurrence. We can achieve our goal of estimating the probability of cancer occurrences by using machine learning algorithms and frequent diagnostic data. Cancer data sets contain a variety of patient information features, but not all of them are useful in cancer prognosis. In such cases, a feature selection approach plays a crucial role in identifying the relevant feature set. In this research, we compare the effects of feature selection approaches on the accuracy provided by existing machine learning algorithms. We investigated the following machine learning methods for this purpose: Logistic Regression(LR), Naive Bayes(NB), Random Forest(RF), Hoeffding Tree(HT), and Multi-Layer Perceptron(MLP). Information Gain(IF), Gain Ratio(GR), Relief-F(R-F), and One-R(OR) were all evaluated as feature selection strategies.The training and performance models are validated using various accuracy matrices such as accuracy, sensitivity, specificity, f-measure, kappa score, and area under the ROC curve(AUC) using the 10-fold cross-validation approach. The accuracy of the proposed framework was 100%, 100%, and 91.30% on breast, cervical, and lung cancer datasets, respectively. Furthermore, this approach may serve as a versatile tool for extracting patterns from several clinical trials for various forms of cancer conditions

An Essay on the Consequences of Emotional Intelligence in Connection to Work Performance and Achievement

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Entrepreneur… TO BE or NOT TO BE

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A Study on Sentiment Analysis for Low-Resource Language with Emphasis on Khasi Language

Sentiment Analysis is an NLP task of finding the opinion and classifies the opinionexpressed in a text according to its polarity (e.g., positive, negative or neutral).Low resource sentiment analysis refers to the task of performing sentimentanalysis on text data with limited annotated data available. This is a commonscenario in many real-world applications, where annotating large amounts oftext data can be time-consuming, expensive, or even impossible. To overcomethis challenge, various methods have been proposed to perform sentiment analysiswith limited annotated data, such as transfer learning, multi-task learning,unsupervised learning, and active learning. In this paper, we look into worksdone on low-resource language sentiment analysis, compare the approaches nothese papers and compiling the success, challenges and pending issues on them.This paper gives an outline of how sentiment analysis is performed and presentsa set of prerequisite before applying sentiment analysis on Khasi Text

AI and Marketing in Higher Education Institutes in India: Navigating the Reality

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Exploring the relationship between Academic Motivation and Achievement: A study of University Students in North-East India

The relationship between academic motivation and academic achievement among university students in North-East India are investigated in this study. This study examines the factors that influence academic motivation, namely intrinsic motivation (Knowledge, Accomplishment, and Stimulation) extrinsic motivation (Introjected regulation, External regulation, and Identified regulation) and “Amotivation” using Vallerand et al. (1992) Academic Motivation Scale as a guide. 462 university students representing a range of academic fields were surveyed to collect the necessary data for the study. Grade point averages, or GPAs, are used to quantify academic success and used as a parameter to judge academic achievement. Descriptive analysis indicated that primary source of motivation for female university students are intrinsic whereas for male it was external. However, there were no significant differences in their motivation and achievements. GPA’s showed positive correlation with all the constructs of academic motivation. The only factor that could significantly predict GPAs were “Amotivation” and intrinsic motivation for accomplishment according to multiple regression analysis.Keywords: Academic Motivation, Academic Achievement, Academic Motivation Scale, “Amotivation”, Intrinsic motivation, extrinsic motivation, North-East India

Energy Aware Routing using AODV Protocol for Low Energy Consumption in WSN

Wireless Sensor Network (WSN) is a self-configured and infrastructure-less network that is used to monitor the environmental conditions and transfer sensor data to the desired destination in a particular region. Energy consumption is the most important concern in WSN, which is considered as an active research area. Routing selection is one method that is used to optimize energy in WSN. There are many protocols for discovering a route between two nodes. However the performance of the Ad hoc On-Demand Distance Vector Protocol (AODV) routing protocol is a more suitable one. It is a generic reactive protocol for routing mostly used in MANET (Mobile Ad Hoc Networks) and WSNs (Wireless Sensor Networks). This protocol supports unicasting and multicasting and will also identify the shortest path. The aim of this paper is discuses about energy-aware routing,  is implemented in the AODV protocol which is derived from nodes remaining energy. The remaining energy of node is computed by Max-Min energy algorithm in order to extend the network's life span and facilitates to keep the network lively. The performance of AODV is compared with Modified AODV protocols. The comparison is done by various performance metrics such as PDR (Packet Delivery Ratio), throughput, delay time, loss rate, and energy consumption. Analysis on the experimental results showed that MAODV protocol gives better results than traditional AODV protocol and it is also inferred that MAODV avoids too much energy consumption of nodes in the network

A Performance Analysis and Design of Skewed Intersection at Vivekanand Tiraha (Vidisha) : A Case Study

ABSTRACT:The increasing traffic volume at Vivekananda Tiraha has increased many problems like congestion ,increasing conflict points, roadside parking hindering traffic movement, oversized vehicles , etc. In order to solve these problems in an efficient and appropriate manner a traffic management system should be designed at the intersection. A performance study of skewed intersections at Vivekananda Tiraha is done to regulate the flow of traffic in a channelized manner. To solve this either a Rotary for a traffic signal can be designed at an intersection.Rotary requires bulging of the weaving area to provide yielding of vehicles coming from Vidisha. It also requires widening of approach from Bhopal end in order to counter problem of skewness, as vehicles coming from Bhopal are not able to see the complete weaving area and tends to move straightwards towards Vidisha.Widening also required for construction of splitter island on undivided Bhopal leg. Thus rotary of proper dimension cannot be constructed due to lack of space at the junction .Thus the best choice we have is installation of a traffic signal with proper markings at each approach .For achieving this objective PCU count has been worked out using traffic control room cameras installed at the intersection. Keywords: Skewed Intersection,Widening,Roadside parking , Conflict Point

Applications of Chatbots & AI Image Generators

This research digs into the domain of testing and evaluating chatbots and im-age-generation AI bots which are wide-ranging applications. While these AI systems hold promise in enhancing user experience and operational efficiency, their deployment necessitates rigorous inspection. Chatbots are integral to customer service and demand precision and reliability. Image-generation AI focuses on output quality. To ensure its responsible use, vigorous testing and evaluation are necessary. The research will evaluate the current state of chat-bots and AI image generators, revealing gaps in outcomes and ethical considerations. This study contributes to the responsible and effective deployment of chatbots and image-generation AI bots, to pave the way for their continued integration into our daily lives and businesses

Attitude Towards SIP: An Empirical Examination

The purpose of this study is to examine the attitude of individuals towards systematic investment plans. The study attempted to understand how personality traits of individuals affect their attitude towards the selected investment category. The study also assessed the impact of investment strategy, investment attitude, investment priority and risk capacity affected attitude towards SIP. The findings of the study revealed that openness to experience has an impact on attitudes towards SIP. Whereas investment attitude, agreeableness, conscientiousness, extraversion, emotional stability, risk capacity, and investment priority do not have any impact on attitude towards SIP.Keywords: systematic investment plans, mutual funds, personality traits, risk capacit