The Journal Punjab Academy of Sciences (JPAS) is being published by Punjab Academy of Sciences (PAS) since 2004. It is a peer reviewed, open access journal. The all-inclusive approach of the Journal enables communication between scholars, scientists and academicians. It forms the basis for the development of further ideas and tracks emerging ideas in the field of science. Original research articles, reviews and short communications in all areas of physical, chemical and biological sciences, as well as related disciplines including engineering sciences are accepted for publication. The JPAS (Print ISSN 2229-7014) is widely circulated journal and helps to increase the visibility and creditability of the researchers and further in their career opportunities. 29 Punjab Science Congress "National Conference on Cutting Edge Science & Technology: Empowering Youth for Viksit Bharat" February 7-9, 2026 4th Annual National Seminar on "ROLE OF ARTIFICIAL INTELLIGENCE & AUTOMATION IN MODERN AGE AGRICULTURE" 29TH OCTOBER 2025 Membership form
BREEDING ECOLOGY OF ASIAN KOEL (EUDYNAMYS SCOLOPACEUS) IN AGROECOSYSTEM OF PUNJAB
The present study was designed to study the breeding biology of Asian koel (Eudynamys scolopaceus) that belongs to order cuculiformes. It does not build its own nest and depends upon host to raise their young ones. This is called brood parasitism. This behavior of Asian Koel in relation to its host, house crow (Corvus splendens) was noted. The study was divided into three different habitat types. Type A (agricultural field area), type B (tree plantation area) and type C (residential/ roadside area). Breeding activities like calling, competition and mate selection/ courtship display were observed and found maximum at type B. The trees species utilized for nesting by house crow was noted. The proportion of parasitized nests was found maximum at habitat type B (0.10) whereas no parasitism was observed at type A and C. The combined proportion of all the types was recorded as 0.05.
IN VITRO EVALUATION OF THE NEMATICIDAL EFFECT OF SOME MEDICINAL PLANTS ON HAEMONCHUS CONTORTUS (NEMATODA)
The aim of present study is to investigate the nematicidal efficacies of five medicinal plants, Acacia nilotica, Azadirachta indica, Dalbergia sissoo, Ocimum sanctum and Ficus religiosa. Aqueous extracts of these plants were investigated in vitro conditions for their anthelmintic activity against adults of Haemonchus contotus. Aqueous extract of each plant was used at five concentrations (10, 20, 30, 40 and 50 %). The results showed that all the extracts has a dose related effect on the motility of worms and the mortality of the worms increases as the concentration of the extract increases. The overall findings of the study revealed that aqueous extracts prepared from the medicinal plants studied have potential nematicidal properties and in vivo studies are suggested to make use of these plants as effective drugs against Haemonchosis in small ruminants as an alternative to chemotherapy.
STUDIES ON THE SECRETORY-EXCRETORY SYSTEM OF NEMATODES: A REVIEW
A considerable variability occurs in the structure of the excretory system of nematodes. In animal-parasitic nematodes the morphology of the excretory organs is different among the larval and adult stages of the same species. Two types of excretory system were found in case of nematodes, namely glandular and tubular. These types have several modifications. Structure of the excretory glands, lateral canals and excretory sinus along with modification of different parts in various nematode species was studied by different workers. These workers have critically assessed the morphology, histology and histochemistry of different parts of excretory system and their probable role in excretion among different nematode species. The present paper reviews the research work carried on the excretory system of nematodes.
Review of Mode Division Multiplexed passive optical networks and its hybrid with TDM and WDM systems
We review the use of Mode Division Multiplexing (MDM) as a multiplexing technology for the realization of passive optical networks. We start with approaches for MDM realization – with the use of complex MIMO digital signal processing or without complex DSP using few mode fibers. We proceed to explain basic architecture of MDM PON, and its hybrid with time division multiplexing - TDM MDM PON and hybrid of time, wavelength and mode division multiplexing – MDM TDM WDM PON. We then proceed to discuss recent progress in the field of Mode Division Multiplexing.
DETECTION OF MENTAL STRESS USING BIOSIGNALS THROUGH MACHINE LEARNING- A BRIEF SURVEY
This paper explores the classification of various existing biosignals used for stress detection and evaluates their effectiveness. Out of all the biosignals reviewed, ECG was chosen as the best biosignals . Further review was conducted to determine the most suitable machine learning model for the chosen ECG signal. Results have shown that KNN gives the best accuracy of 96.41% followed by SVM with a considerably high accuracy of 90.10%. The best ML models were then evaluated on other biosignals for comparison of effectiveness on different data signals. The findings of this study offer useful insights into the selection of optimal biosignals and machine learning algorithms for detecting stress, which can contribute to development of personalized stress management technologies and improving mental health.
GOVERNMENT POLICIES' INFLUENCE ON REAL ESTATE CUSTOMER PURCHASING BEHAVIOUR
With the liberalization of the market, the realty sector in India acquired importance, as more business possibilities, as well as labour relocation, led to the growing demand for both residential and commercial space. This research is on the Indian real estate business. This study was primarily concerned with consumer purchasing behaviour concerning residential housing. The principal goal of this study was to examine the impact of government policies on several types of buyers' property purchasing behaviour in different residential areas of Punjab and to determine whether government policies will inspire a person to buy real estate property and whether customers are becoming more confident. The paper is helpful for both buyers and real estate agents. It helps buyers understand the factors that go into making a purchase decision, and it helps real estate agents understand what buyers want and how they think.
PERFORMANCE ASSESSMENT OF III-V HETEROJUNCTION FINFETS AT 14nm GATE LENGTH
Fin shaped field effect transistors have become prominent candidate in high-performance processors of leading semiconductor industries. It is owing to the fact that thin fin of structure and presence of gates on multiple sides increase electrostatic integrity. Objective of the present work is to design FinFET for gate length of 14nm and analyse performance parameters for different channel materials. Fin width and fin height of device is set as 7nm and 12nm; 25nm and 30nm respectively. Channel of FinFET has been structured with III-V type materials such as AlGaAs, InGaAs and InP. Cogenda Visual TCAD tool has been used for accurate design and extensive simulations of FinFET. Performance has been investigated in terms of on-current (Ion), leakage current (Ioff) and subthreshold swing (SS) for applied drain voltage and gate voltage. These parameters have been extracted from current-voltage characteristics of the device. Ion/Ioff is of the order 107, Ioff is of the order 10-14 and SS is around 80mV/dec for InP material. Based on performance comparison, it is noted that superior performance is achieved in case of InP material as compared to other materials.
A MILLIMETER WAVE OVER NEXT GENERATION PASSIVE OPTICAL NETWORK STAGE 2: AN IDEA FOR FUTURE GENERATION NETWORK
The potential of milli meter wave to support next-generation passive optical network (NGPON2) technology is covered in this research. Milli meter wave offers optical access networks a number of benefits, including higher capacity, better spectrum usage, decreased latency, and improved security. The study goes on to look at the difficulties that need to be overcome in order to fully utilize milli meter wave for NGPON2, such as the requirement for higher data rates, expanded transmission range, and enhanced antenna performance. The report concludes with a summary of the various milli meter wave enabled NGPON2 components and the ongoing research to improve their performance.
Next Generation Passive Optical Networks 2
FACIAL EXPRESSION RECOGNITION WITH LOCAL BINARY PATTERN IN MACHINE LEARNING: A REVIEW
Automatically analyzing facial expressions is an intriguing and challenging problem which has significant applications in numerous fields, including data-driven animation and human–computer interaction. An essential step in successfully recognizing facial expressions is obtaining an accurate facial representation from original face image. For person-independent facial expression recognition, in this paper we have local feature- based facial representation and local binary patterns (LBP) techniques. On a number of databases, comprehensive analysis has been presented on various machine-learning techniques for facial recognition. LBP Feature extraction technique has been demonstrated to recognize facial expressions. Further, to extract the most discriminating local binary pattern features a boosted- LBP feature with support vector machine classifiers has been suggested which results in best face recognition performance accuracy. Additionally, we have described the investigations of low-resolution facial expression recognition with LBP feature extraction which is a crucial issue which have not received earlier consideration.
Expression-specific local binary pattern
Class-regularized locality preserving projection
REDUCTION OF BLOCKING PROBABILITY IN GMPLS OPTICAL NETWORKS USING NOVEL HYBRID OPTIMIZATION ALGORITHM
In this paper, an optimized algorithm hybridizing Minimum Execution Time (MET), Weighted Round Robin (WRR) and Particle Swarm Optimization (PSO) is presented to minimize the blocking probability in Generalized Multi-Protocol Label Switched (GMPLS) optical networks to improve Quality of Service (QoS). MET is used for distributing the bandwidth in a well-organized approach to further reduce the latency in the network. The deviation in blocking probability is calculated depending on demanding traffic load and number of wavelengths accessible at the instant when user demand occurs in the GMPLS optical network. WRR is used to increase the flexibility in computing the path between the nodes. The overheads are also optimized using PSO with increasing number of iterations. The consequences exposed that the blocking probability reduces with growing number of accessible wavelengths. The value of blocking probability obtained is <1% by using the proposed hybrid optimization technique for bandwidth allocation and path computation.
Generalized Multi-Protocol Label Switching
CLASSIFICATION OF MODULATION TECHNIQUES USING CONVOLUTIONAL NEURAL NETWORK: A REVIEW
A method to classify the required modulations among the various kinds of distributed systems by using the Convolutional neural network (CNN). A supervised Machine learning (ML) algorithm, Support vector machine (SVM) is used to classify the required modulation among all the modulations due to its advantages of majorly low complexity. In this paper, different researchers’ research work is studied, and different problems are faced like CNNs are a regularised version of multilayer perceptrons that were motivated by the biological process of neuronal connection. They are efficiently used in a variety of classification problems because, in contrast to other classification methods, they require less preparation. The CNN is explained in simple terms with relevant mathematical analysis. For a better understanding of the classification of modulated techniques, some analog modulation techniques such as Binary phase shift keying (BPSK), Quadrature phase shift keying (QPSK), 8-ary phase shift keying (8-PSK), 16-ary Quadrature amplitude modulation (16-QAM), 64-ary Quadrature amplitude modulation (64-QAM), 4-ary pulse amplitude modulation (PAM4), Gaussian frequency shift keying (GFSK), Continuous phase frequency shift keying (CPFSK) and digital modulations such as Broadcast FM (B-FM), Double sideband amplitude modulation (DSB-AM), Single sideband amplitude modulation (SSB-AM) are considered for review.
A REVIEW ON IMPROVEMENT OF DRONE DETECTION AND TRACKING SYSTEM USING ARTIFICIAL INTELLIGENCE
The development of the drone industry has led to an increase injudicious, unauthorized, and illegal drone use, which has resulted in significant harm to society and the economy. They look at some major incidents involving drones around the world and identify essential characteristics for upcoming anti-drone systems. According to this study, the drone industry has made drones that can be used in everyday life available to the general public because of the widespread interest in them. However, as the use of drones became more widely available, the likelihood of accidents has increased, raising concerns regarding safety and security: slipping out of control, colliding with others, or breaking into secured properties. It is fundamental for the two eyewitnesses and the robot to know about an oncoming robot for wellbeing reasons. This paper presents a comprehensive drone detection system based on machine learning as a result of a literature review of various studies and the various issues encountered. All of these issues will be addressed by future research.
UNLOCKING INDUSTRY 5.0 POST-COVID 19 CATASTROPHE
A new industrial revolution has erupted amid the Covid-2019 catastrophe. Covid-2019 has certainly pushed the swift development of Industry 4.0 but has also further led to the fast evolution to a new worker-centric style termed Industry 5.0. It can be said firmly that, unlike all the previous four revolutions which were based on technological advancements the new revolution is people centric. The European countries are leading from the front in the implementation of this new revolution to empower employees instead of replacing them with new technologies. Industry 4.0 had been on a boom in the past decade but had a major shortcoming of over-dependence on industrialization. The dire need was felt to collaborate workers with their smart industries for optimization of business productivity. This paper is an attempt to understand the new human-centric revolution termed Industry 5.0and a few challenges being identified to be effective in competitive businesses. Lastly, it is found that the execution of Industry 5.0 could reduce the over-dependence on technology and would emphasize the association between workers and industries.
THEORETICAL INVESTIGATION OF THE COMPACT DIELECTRIC POLYMER USING PVDF-HFP
Reducing the size of the dielectric material is a big problem to prepare next-generation dielectric gadget. In this paper, we proposed solution of this problem that affects the size of the dielectric particles. Due to the large size of 140nm, a good permittivity has been detected. This has been explained by the property of a material-like structure, model, lattice size, or shape and having a very large permittivity. We observed the permittivity for the approx. size of about 1.1µm, due to the domain – wall role and boundary effect. The proposed work will highlight necessity to make devices that have a very small size and may give very large production with the help of PVDF HFP.
MODELLING AND ANALYSIS OF FULLY INTEGRATED 3:2 SWITCHED CAPACITOR DC-DC CONVERTER
A type of DC-DC power converter known as a switched-capacitor converter efficiently changes one voltage level to another by employing switches and capacitors. They provide high performance point-of-load voltage regulation for digital systems. This paper investigates general steady-state performance model of 3:2 series-parallel switched capacitor DC-DC converter. The existing basic SC converter analysis is extended to derive minimum achievable power loss for efficiency enhancement that suffers from both conduction and switching losses. The method relies on charge-multiplier approach and analysis is done to achieve 48mA load current realized on a 22 nm technology node. According to the findings, a unit cell's switch size equates to 1Ω on resistance, and a switching frequency of at least 200 MHz is needed to achieve the target efficiency of more than 85% and an output voltage more than 0.72V.
HANDLING MISSING VALUES IN NUMERIC DATASET USING MACHINE LEARNING TECHNIQUES: A REVIEW
Data mining is essential for pre-processing task to ensure the quality of the final product. These tasks include data preparation, cleaning, integration, transformation, reduction, and discretization. Missing values are a common problem that regularly occurs throughout the data cleaning process in various research fields. To complete missing values, eliminate noise and remove inconsistencies is an important process in the preparation of the data. This paper focuses on a review of several classification methods, including their benefits and shortcomings. It is used in a variety of industries, including internet marketing, healthcare, social networking, finance, and insurance. The accuracy of data imputation for machine learning classifiers such as Bayesian Networks, Decision Trees and K-Nearest Neighbors (KNN), as well as Support Vector Machines, is compared in this paper. Based on the findings, Bayesian appears to provide the most promising results when compared to the other classifiers.
A SURVEY ON CROP YIELD PREDICTION USING MACHINE LEARNING
Agriculture is the science and practice of raising plants and animals. India is the second-largest agricultural nation in the world, with 60.45% of its land used for farming. In India, agriculture is one of the most common and least-paid professions. Being an agricultural nation, India's economy is heavily dependent on rising agricultural yields and agro-industrial goods. Machine learning can bring a boom in the agriculture field by changing the income scenario for optimal crop. This paper focuses on predicting the yield of the crop by applying various machine learning algorithms. Machine learning algorithms' predictions will assist farmers in selecting the best crops for their farms based on factors including soil type, temperature, humidity, water level, spacing depth, soil PH, season, fertilizer, and months. This paper focuses on a concise comparative work of several papers that discuss many methods for assessing crop yield. It seeks to predict agricultural yield through consideration and investigation of the datasets of previous years of the crop. The study also describes various current methods for auditing crop yield. It also includes a comparison of several algorithms and their advantages and disadvantages.
AN OVERVIEW OF ADVANCED TECHNOLOGIES APPLIED TO IDENTIFIED PRINTED AND HANDWRITTEN TEXT IN GURMUKHI SCRIPT: A REVIEW
Handwriting recognition of Gurmukhi is an area of research which has gained traction in recent years. This paper reviews the state of the art of handwriting recognition for Gurmukhi. A brief overview of the various approaches used for Gurmukhi handwriting recognition, their benefits and drawbacks, the current challenges and the future research directions is presented. Specifically, the review will focus on the recent advances in deep learning models, feature extraction techniques, and preprocessing strategies. It further examines the challenges and potential areas of improvement in the field of Gurmukhi handwriting recognition. Finally, it provides insights into the potential applications of this technology and the opportunities for future research in the area. The paper concludes by highlighting the importance of handwriting recognition for Gurmukhi and the need for further research and development in the field.
BREAST CANCER PREDICTION VIA GRID SEARCH HYPERPARAMETER OPTIMIZATION
Breast Cancer is a serious health issue worldwide, and early detection is crucial in preventing deaths. Machine learning can help identify tumors efficiently, and this paper introduces the Grid Search Hyperparameter Optimization (GSHPO) method to optimize the parameters of six existing models, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest. The best parameters were applied to predict outcomes in six datasets, including OWBCD, WDBC, Coimbra, BRCA, Haberman, and SEER. The results show that tuning the hyperparameters of models has a significant positive impact on prediction accuracy.
Breast Cancer, early detection, machine learning, hyperparameter optimization, logistic regression, support vector machine, k-nearest neighbor, naive bayes, decision tree, random forest, OWBCD, WDBC, Coimbra, BRCA, Haberman, SEER
A REVIEW ON MULTI-BAND OPTICAL COMMUNICATION SYSTEM
Multiband optical communication has attracted a lot of interest since it is difficult to increase capacity per optical fibre within traditional, constrained bandwidths. Our goal is to increase bandwidth from the currently popular C or L bands to other bands like the S, E, and O bands. Multiband optical communication is the promising technique to extend the lifetime of currently deployed networks. In this paper, we review the components, potentialities and challenges of Multi-band optical communication system over single mode fibers, compared to transmission solutions such as multi-fiber (MF), multi-mode fiber (MMF) and multi-core fiber (MCF).
MULTI-INDEX TRANSPORTATION PROBLEM: AN OVERVIEW OF ITS VARIANTS, SOLUTION TECHNIQUES AND APPLICATIONS
Multi-index Transportation Problem (MTP) and its variants have originated to model one of the complex scenarios of transportation problem where cost and/or time of transportation also depend upon type of transported commodity and/or freight vehicle used in transportation along with given sources and destinations. In almost last six and half decades, researchers have extensively studied MTP and its variants from different perspectives, developed many solution approaches and established its applicability by modeling many problems of engineering and management as MTP. However, a comprehensive review of the problem domain is surprisingly absent from the concerned literature, therefore, this paper strives to fill this void by systematically examining 59 published articles filtered through a well-defined search criterion. Methodologically, the examined articles are categorized into various classes based on two, possibly exhaustive but certainly not exclusive, attributes of classification, one is, approach used to study the problem and, another is, contribution of the study. Classification is presented in a two-way tabular form that will serve as a guiding map to young researchers who are intending to study MTP, its variants, and/or applications in engineering and management. This paper also provides an indication for the future work. It concludes that, since its inception, MTP has evolved as one of the very important problem domains in logistics research and metaheuristic approach has contributed most in its study.
On a monthly basis, conventional electricity meters given by government power supply organizations monitor the energy use of an entire residence or enterprise. This implies that customers have no way of tracking individual appliance energy use. In addition, consumers are unaware of their everyday energy use habits. Smart meters may be created by incorporating IOT into our traditional electrical meters. The primary purpose of IOT-based smart meters is to improve the existing system's performance, reliability, and features through an active customer approach. Smart energy meters acquire all energy-related data at a low cost. It tackles power quality concerns while also highlighting the potential to improve Demand Side Management (DSM).
A COMPREHENSIVE STUDY OF THE IOT ATTACKS ON DIFFERENT LAYERS
A key innovation in utilizing the enormous capacity of wireless media in the physical world is the internet of things. The internet of things' (IoT) recent explosive expansion is a result of its versatility, adaptability, and intelligence. Most Internet of Things (IoT) apps run tasks automatically, without interacting with people or real-world items. By connecting with a variety of smart programs that are running independently on various platforms, practically everywhere in the globe, we may manage our surroundings. The potential threats in the five layers of IoT are specifically reviewed in this paper. In this study, we will outline various IoT components i.e. (architecture, IoT Security attacks, Attacks and Targeted Layer of IoT) and the general description of IoT-layered security. In this paper, author discuss the IoT attacks which are performed on different layers of IoT architecture. We have discussed security attacks in detail in this paper with relation to various IoT layer types. We recently presented a few of the IoT's uses. This study will help academics and manufacturers assess and narrow the attack surface area on IoT devices.
STUDY OF CLASSIFIERS FOR THE IDENTIFICATION OF FAKE NEWS
A significant source of local and international news for millions of individuals, online social networking websites are rapidly growing. OSNs, on the contrary side, have two disadvantages. Despite the numerous benefits they provide, such as endless, simple communication and instant access to data and news, they can also come with a number of drawbacks and concerns. The spread of misleading information is one of their toughest obstacles. Fake news detection is a difficult & unsolved issue. Nevertheless, designing a solution is anything but straightforward given the unique characteristics and challenges of identifying bogus news on OSNs. Artificial intelligence (AI) techniques, on the other hand, are still incapable of overcoming this difficult obstacle. Even worse, by creating and disseminating false information, artificial intelligence (AI) systems like deep learning and machine learning are being abused to trick customers. Because the material is meant to closely mirror reality, it can occasionally be difficult to assess its reliability using AI alone and without support from outside sources. As a result, it's still very difficult to recognise bogus news automatically. This research seeks to offer a fundamental review of the methods currently used to recognise false news and prevent it from spreading over OSNs in addition to classifying fake news using several classifiers.
online social networks and information disorder
RECENT MACHINE LEARNING ADVANCES IN AGRICULTURE
The economy of India is significantly influenced by agriculture. The majority of Indians are either directly or indirectly dependent on agriculture. Therefore, it cannot be denied that agriculture plays an important role in the nation. So it is important for former to make best decision while growing any crop based on multiple factors. In this era of advancement there are number of technologies which can be used in agriculture field one of these is Machine Learning (ML). In this paper we have summarized different ML algorithms that are implemented by different researchers in their studies for Crop recommendation, yield prediction. We have concluded that Random Forest, Support Vector Machine, Naïve Bayes, Neural Network, Decision Tree, K-nearest neighbour, XGBoost(eXtreme Gradient boosting), Multivariate Linear Regression, Logistic Regression, Chi-Square Automatic Interaction Detection (CHAID) and Sliding Window non-Linear Regression comes with maximum accuracy. This prediction has been done based on various factors such as soil type, temperature, rainfall, pH value of soil, Nitrogen Phosphorus Potassium (NPK) content of soil, sowing season, porosity of soil, erosion, water holding, drainage, crop yield, crop consumed, humidity and location parameters. Furthermore, in future ML algorithms can be utilized in various sectors of Agriculture like disease detection in crops, soil suitability recommendation and prediction of retail prices.
COMPARATIVE ANALYSIS OF SILVER AND GRAPHENE BASED METASURFACE UNIT-CELL STRUCTURE FOR WIDEBAND THz ANTENNA
The rapid growth of wireless communication is accelerating like never before. It is propelled by the introduction of the devices that uses 5G and beyond. It is foreseeable that the antennas in THz range, smart antennas and reconfigurable intelligent surfaces (RIS) are going to be vital in the future. A lot of traction is happening in the THz frequency range with the amalgamation of the THz antennas based off of metasurface integrations. In this paper a metasurface based THz antenna is designed using 2 different materials and their performance is evaluated and compared. The unit cell structure is made up of silver and graphene materials and geometries are modified for the patch antenna. The improved performance of graphene over silver is obtained and the results are presented in a novel and comparably comprehensible manner.
50G-PON-BASED DP-QPSK TECHNIQUE BY UTILIZING THE MILLIMETER FREQUENCY SPECTRUM
For the fifth generation (5G) broadband fronthaul network, the architecture of the 50 Gbps passive optical network (50G-PON) is provided. This research makes use of a quadrature phase shift keying (QPSK) structure with a coherent receiver network and the dual polarization (DP) idea. In order to maximize band usage, the millimeter wave at 32 GHz is modulated over 50G DP-QPSK that spans a 60 km fiber span. A more effective technique to utilize millimeter wave in the high spectrum region for mobile broadband is through passive optical network solutions. The performance is assessed using the constellation figure and signal spectra.
Dual Polarization-Quadrature Phase Shift Keying
GREEN CLOUD COMPUTING: A COMPREHENSIVE REVIEW OF ECO-FRIENDLY COMPUTING STRATEGIES
As the demand for cloud computing continues to increase, the environmental impact of data centers is becoming a growing concern. Green cloud computing has emerged as a solution to address this issue, reducing the carbon footprint of cloud computing while maintaining its performance and functionality. This paper comprehensively reviews eco-friendly computing strategies for Green Cloud Computing, such as virtualization, consolidation, dynamic resource allocation, energy-efficient hardware design, Renewable energy sources, and energy-efficient cooling systems. Green computation algorithms, which are an integral component of the green cloud computing movement that aims to reduce the carbon footprint of computing systems, are explored. The algorithms are designed to optimize energy efficiency, reduce power consumption, and ensure the efficient use of resources while maintaining performance and functionality. The algorithms are essential to widely adopt eco-friendly computing strategies and create a more sustainable future for cloud computing.
SIMULATION METHODOLOGY FOR ANALYZING THE PERFORMANCE OF TFET THROUGH THM-TFET MODEL FILE IN CADENCE
MOSFETs are used to create the majority of real-time devices at present due to their fast-switching times. MOSFET subthreshold swing scaling cannot be 60mV/decade lowered. Because of its low subthreshold swing and low leakage current, tunnel FET (TFET) is a viable MOSFET substitute. This paper shows the simulation of the TFET inverter using cadence virtuoso by using the model file called THM-TFET, it is Verilog-A coded and used for the DC and AC simulations of Tunnel FET(TFET).
EMOTION RECOGNITION USING TWITTER DATA: AN ENSEMBLE MACHINE LEARNING TECHNIQUE
Due to the expansion of world of the internet and the quick acceptance of platforms for social media, information is now able to exchange in ways never previously imagined in history of mankind. A social networking site like Twitter offers a forum where people may interact, discuss, as well as respond to specific issues via short entries, like tweets of 140 characters and fewer. Users may engage by utilizing the comment, like and share tabs on texts, videos, images and other content. Although platforms for social media are now so extensively utilized, individuals are creating as well as sharing so much information than shared before, which can be incorrect or unconnected to reality. It is difficult to identify erroneous or inaccurate statements in textual content autonomously and find emotions of people. In this paper, we suggest an Ensemble method for sentiment and emotion analysis. Different textual features of actual and Emotion and sentiment have been utilized. We used a publicly accessible dataset of twitter sentiment analysis that included total 48,247 authenticated tweets out of 23,947 of which were authentic positive texts labelled as binary 0s and 24,300 of which were negative texts labelled as binary 1s. In order to assess our approach, we used well-known (ML) machine learning models such as Logistic Regression (LR), Decision Tree (DT), AdaBoost, SGD, XG-Boost, and Naive Bayes. In order to get more accurate findings, we created a multi-model sentiment and emotion analysing system utilizing the ensemble approach and the classifiers stated above. Our recommended ensemble learner method outperforms individual learners, according to an experimental study.