INTRODUCTION: Cervical cancer is the leading cancer among the other female cancers. It develops in the cervix of women. It takes decades in development thus can be preventable if diagnosed at an early stage. The cervix is classified into three types Type I/II/III. The efficacy of the treatment depends on the diagnosis of the right type of cervix. There is a thin line difference between the three types. Thus, identification of the right type of cervix becomes a difficult task for the health care providers too. To aid this problem, we proposed an algorithm based on the standard transfer learning approach used for building a model that classifies cervix images.OBJECTIVES: The objective of this study is to develop a cervical cancer predictive model based on deep learning and transfer learning techniques that will recognize and classify the cervix images into one of the classes (Type 1/Type2/Type3).METHODS: Techniques used for carrying out the experimental work includes deep learning and Transfer Learning. The three pertained models namely InceptionV3, ResNet50, and VGG19 are used for creating ConvNet that will classify the cervix images.RESULTS: The result of the experiment reveals that the Inception v3 model is performing better than Vgg19 and ResNet50 with an accuracy of 96.1% on the cervical cancer dataset.CONCLUSION: In the future, augmentation techniques can be employed to achieve better accuracy.

Sign languages are important for the deaf and hard-of-hearing communities, as they provide a means of communication and expression. However, many people outside of the deaf community are not familiar with sign languages, which can lead to communication barriers and exclusion. Each country and culture have its own sign language, and some countries have multiple sign languages. Indian Sign Language (ISL) is a visual language used by the deaf and hard-of-hearing community in India. It is a complete language, with its own grammar and syntax, and is used to convey information through hand gestures, facial expressions, and body language. Over time, ISL has evolved into its own distinct language, with regional variations and dialects. Recognizing hand gestures in sign languages is a challenging task due to the high variability in hand shapes, movements, and orientations. ISL uses a combination of one-handed and two-handed gestures, which makes it fundamentally different from other common sign languages like American Sign Language (ASL). This paper aims to address the communication gap between specially abled (deaf) people who can only express themselves through the Indian sign language and those who do not understand it, thereby improving accessibility and communication for sign language users. This is achieved by using and implementing Convolutional Neural Networks on our self-made dataset. This is a necessary step, as none of the existing datasets fulfills the need for real-world images. We have achieved 0.0178 loss and 99% accuracy on our dataset.

INTRODUCTION: With the progression of innovation and its joint effort with health care services, the world has achieved a lot of benefits. AI procedures and machine learning techniques are constantly improving existing statistical methods for better results in the medical field. These improved methods will assist health care providers in providing intelligent medical services. OBJECTIVES: This Cervical cancer is the fourth most common cancer among the other female cancers. This cancer is preventable with early diagnosis. This reason becomes the motivation of the research work. For efficiently and timely prognosis of cervical cancer require a computer-assisted algorithm METHODS: The work demonstrated in this paper contributes to the techniques of machine learning for diagnosing cervical cancer. The machine learning algorithms used in this research are K Nearest Neighbour, Support Vector Machine and Random Forest Tree. These classification algorithms are used with class balancing techniques including undersampling, Oversampling and SMOTE. RESULTS: The evaluation metrics used for comparative analysis includes accuracy, sensitivity, specificity, negative predicted accuracy, and positive predictive accuracy. The results show the Random Forest algorithm with SMOTE technique delivered more promising results over SVM and KNN for four target variables Schiller, Biopsy, Hinselmann , and Cytology respectively. CONCLUSION: It is concluded that with the limited amount of data which also suffers from the unbalancing problem the promising results drawn using the proposed model.

This research aims to propose a more sophisticated clustering and community detection technique in complex social networks through the use of neural networks; autoencoder, in particular. In the past, methods for network analysis and community detection used several graph algorithms, but with the advancements in deep learning, autoencoders are used for learning node features. These node representations are learnt using a neural network-based autoencoder and then clustering algorithms like k-means, agglomerative and spectral clustering are performed. These algorithms are then improved by incorporating with the Louvain algorithm for community detection. The proposed method, named the Spectral Louvain Algorithm, offers several advantages: it saves the stage of feature extraction, is suitable for Call Detail Record (CDR) and Social Network Analysis (SNA), does not require model retraining for different scale networks, and can work in mesh scale networks. It has better accuracy and performance than former approaches, even getting 100% of NMI and ARS, 78% of modularity in Karate Club and 89% of NMI, 93% of ARS and 86% of modularity in Dolphin data set with the least conductance. This method is particularly useful in discovering new relations and structures in a system and it is very efficient.